From e4e777496010b2fc259c346e9c8da9e74a4bac56 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9lodie=20Monod?= Date: Thu, 18 Jul 2024 17:34:18 +0100 Subject: [PATCH 01/22] Create draft-pdf.yml --- .github/workflows/draft-pdf.yml | 34 +++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 .github/workflows/draft-pdf.yml diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 0000000..8ec7d31 --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,34 @@ +name: Draft PDF +on: [push] + push: + paths: + - paper/** + - .github/workflows/draft-pdf.yml + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper.md + - name: Upload + uses: actions/upload-artifact@v4 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper/paper.pdf + - name: Commit PDF to repository + uses: EndBug/add-and-commit@v9 + with: + message: '(auto) Paper PDF Draft' + # This should be the path to the paper within your repo. + add: 'paper.pdf' # 'paper/*.pdf' to commit all PDFs in the paper directory From 19f3f5739ef24ad433dba59e7e95b60df21e4915 Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Thu, 18 Jul 2024 17:40:48 +0100 Subject: [PATCH 02/22] add paper joss --- paper/paper.bib | 59 ++++++++++++++++++++++++++ paper/paper.md | 107 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 166 insertions(+) create mode 100644 paper/paper.bib create mode 100644 paper/paper.md diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..72e3977 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,59 @@ +@article{Pearson:2017, + url = {http://adsabs.harvard.edu/abs/2017arXiv170304627P}, + Archiveprefix = {arXiv}, + Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.}, + Eprint = {1703.04627}, + Journal = {ArXiv e-prints}, + Keywords = {Astrophysics - Astrophysics of Galaxies}, + Month = mar, + Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}}, + Year = 2017 +} + +@book{Binney:2008, + url = {http://adsabs.harvard.edu/abs/2008gady.book.....B}, + Author = {{Binney}, J. and {Tremaine}, S.}, + Booktitle = {Galactic Dynamics: Second Edition, by James Binney and Scott Tremaine.~ISBN 978-0-691-13026-2 (HB).~Published by Princeton University Press, Princeton, NJ USA, 2008.}, + Publisher = {Princeton University Press}, + Title = {{Galactic Dynamics: Second Edition}}, + Year = 2008 +} + +@article{gaia, + author = {{Gaia Collaboration}}, + title = "{The Gaia mission}", + journal = {Astronomy and Astrophysics}, + archivePrefix = "arXiv", + eprint = {1609.04153}, + primaryClass = "astro-ph.IM", + keywords = {space vehicles: instruments, Galaxy: structure, astrometry, parallaxes, proper motions, telescopes}, + year = 2016, + month = nov, + volume = 595, + doi = {10.1051/0004-6361/201629272}, + url = {http://adsabs.harvard.edu/abs/2016A%26A...595A...1G}, +} + +@article{astropy, + author = {{Astropy Collaboration}}, + title = "{Astropy: A community Python package for astronomy}", + journal = {Astronomy and Astrophysics}, + archivePrefix = "arXiv", + eprint = {1307.6212}, + primaryClass = "astro-ph.IM", + keywords = {methods: data analysis, methods: miscellaneous, virtual observatory tools}, + year = 2013, + month = oct, + volume = 558, + doi = {10.1051/0004-6361/201322068}, + url = {http://adsabs.harvard.edu/abs/2013A%26A...558A..33A} +} + +@misc{fidgit, + author = {A. M. Smith and K. Thaney and M. Hahnel}, + title = {Fidgit: An ungodly union of GitHub and Figshare}, + year = {2020}, + publisher = {GitHub}, + journal = {GitHub repository}, + url = {https://github.com/arfon/fidgit} +} \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 0000000..40b1b79 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,107 @@ +--- +title: '\texttt{TorchSurv}: A Lightweight Package for Deep Survival Analysis' +tags: + - Python + - radiomics + - medical imaging +authors: + - name: Alex Zwanenburg + orcid: 0000-0002-0342-9545 + affiliation: "1, 2" + - name: Steffen Löck + orcid: 0000-0002-7017-3738 + affiliation: "2, 3, 4" +affiliations: + - name: "National Center for Tumor Diseases Dresden (NCT/UCC), Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany" + index: 1 + - name: OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl + Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany + index: 2 + - name: German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany + index: 3 + - name: Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany + index: 4 +date: 7 February 2024 +bibliography: paper.bib +--- + +# Summary + +The forces on stars, galaxies, and dark matter under external gravitational +fields lead to the dynamical evolution of structures in the universe. The orbits +of these bodies are therefore key to understanding the formation, history, and +future state of galaxies. The field of "galactic dynamics," which aims to model +the gravitating components of galaxies to study their structure and evolution, +is now well-established, commonly taught, and frequently used in astronomy. +Aside from toy problems and demonstrations, the majority of problems require +efficient numerical tools, many of which require the same base code (e.g., for +performing numerical orbit integration). + +# Statement of need + +`Gala` is an Astropy-affiliated Python package for galactic dynamics. Python +enables wrapping low-level languages (e.g., C) for speed without losing +flexibility or ease-of-use in the user-interface. The API for `Gala` was +designed to provide a class-based and user-friendly interface to fast (C or +Cython-optimized) implementations of common operations such as gravitational +potential and force evaluation, orbit integration, dynamical transformations, +and chaos indicators for nonlinear dynamics. `Gala` also relies heavily on and +interfaces well with the implementations of physical units and astronomical +coordinate systems in the `Astropy` package [@astropy] (`astropy.units` and +`astropy.coordinates`). + +`Gala` was designed to be used by both astronomical researchers and by +students in courses on gravitational dynamics or astronomy. It has already been +used in a number of scientific publications [@Pearson:2017] and has also been +used in graduate courses on Galactic dynamics to, e.g., provide interactive +visualizations of textbook material [@Binney:2008]. The combination of speed, +design, and support for Astropy functionality in `Gala` will enable exciting +scientific explorations of forthcoming data releases from the *Gaia* mission +[@gaia] by students and experts alike. + +# Mathematics + +Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$ + +Double dollars make self-standing equations: + +$$\Theta(x) = \left\{\begin{array}{l} +0\textrm{ if } x < 0\cr +1\textrm{ else} +\end{array}\right.$$ + +You can also use plain \LaTeX for equations +\begin{equation}\label{eq:fourier} +\hat f(\omega) = \int_{-\infty}^{\infty} f(x) e^{i\omega x} dx +\end{equation} +and refer to \autoref{eq:fourier} from text. + +# Citations + +Citations to entries in paper.bib should be in +[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) +format. + +If you want to cite a software repository URL (e.g. something on GitHub without a preferred +citation) then you can do it with the example BibTeX entry below for @fidgit. + +For a quick reference, the following citation commands can be used: +- `@author:2001` -> "Author et al. (2001)" +- `[@author:2001]` -> "(Author et al., 2001)" +- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" + +# Figures + +Figures can be included like this: +![Caption for example figure.\label{fig:example}](figure.png) +and referenced from text using \autoref{fig:example}. + +Figure sizes can be customized by adding an optional second parameter: +![Caption for example figure.](figure.png){ width=20% } + +# Acknowledgements + +We acknowledge contributions from Brigitta Sipocz, Syrtis Major, and Semyeong +Oh, and support from Kathryn Johnston during the genesis of this project. + +# References From 66285a48efbdc32651a35bcccaf775cdac057e03 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9lodie=20Monod?= Date: Thu, 18 Jul 2024 17:44:37 +0100 Subject: [PATCH 03/22] Update draft-pdf.yml --- .github/workflows/draft-pdf.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 8ec7d31..c7ff936 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -1,5 +1,5 @@ name: Draft PDF -on: [push] +on: push: paths: - paper/** From 9d596f4165465688017f305b3bfb9ffb87c7ef8e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9lodie=20Monod?= Date: Thu, 18 Jul 2024 17:45:42 +0100 Subject: [PATCH 04/22] Update draft-pdf.yml --- .github/workflows/draft-pdf.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index c7ff936..231eae8 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -17,7 +17,7 @@ jobs: with: journal: joss # This should be the path to the paper within your repo. - paper-path: paper.md + paper-path: paper/paper.md - name: Upload uses: actions/upload-artifact@v4 with: From 831c6a8a0eb77bd79c180a0cc6281a95aa5461b0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9lodie=20Monod?= Date: Thu, 18 Jul 2024 17:48:51 +0100 Subject: [PATCH 05/22] Update draft-pdf.yml --- .github/workflows/draft-pdf.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 231eae8..cb23d1e 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -25,7 +25,7 @@ jobs: # This is the output path where Pandoc will write the compiled # PDF. Note, this should be the same directory as the input # paper.md - path: paper/paper.pdf + path: paper.pdf - name: Commit PDF to repository uses: EndBug/add-and-commit@v9 with: From dce716d632ab1bec53b227997df86ace1d6dba40 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9lodie=20Monod?= Date: Thu, 18 Jul 2024 17:50:57 +0100 Subject: [PATCH 06/22] Update draft-pdf.yml --- .github/workflows/draft-pdf.yml | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index cb23d1e..196d07a 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -1,10 +1,6 @@ name: Draft PDF -on: - push: - paths: - - paper/** - - .github/workflows/draft-pdf.yml - +on: [push] + jobs: paper: runs-on: ubuntu-latest @@ -26,9 +22,3 @@ jobs: # PDF. Note, this should be the same directory as the input # paper.md path: paper.pdf - - name: Commit PDF to repository - uses: EndBug/add-and-commit@v9 - with: - message: '(auto) Paper PDF Draft' - # This should be the path to the paper within your repo. - add: 'paper.pdf' # 'paper/*.pdf' to commit all PDFs in the paper directory From 90fd07faec139b8f3f62267b3ddce6899485c3c4 Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Thu, 18 Jul 2024 18:56:47 +0100 Subject: [PATCH 07/22] first draft of paper --- paper/paper.bib | 421 +++++++++++++++++++++++++++++++++++++++++------- paper/paper.md | 214 +++++++++++++++--------- 2 files changed, 501 insertions(+), 134 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 72e3977..549469a 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,59 +1,364 @@ -@article{Pearson:2017, - url = {http://adsabs.harvard.edu/abs/2017arXiv170304627P}, - Archiveprefix = {arXiv}, - Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.}, - Eprint = {1703.04627}, - Journal = {ArXiv e-prints}, - Keywords = {Astrophysics - Astrophysics of Galaxies}, - Month = mar, - Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}}, - Year = 2017 -} - -@book{Binney:2008, - url = {http://adsabs.harvard.edu/abs/2008gady.book.....B}, - Author = {{Binney}, J. and {Tremaine}, S.}, - Booktitle = {Galactic Dynamics: Second Edition, by James Binney and Scott Tremaine.~ISBN 978-0-691-13026-2 (HB).~Published by Princeton University Press, Princeton, NJ USA, 2008.}, - Publisher = {Princeton University Press}, - Title = {{Galactic Dynamics: Second Edition}}, - Year = 2008 -} - -@article{gaia, - author = {{Gaia Collaboration}}, - title = "{The Gaia mission}", - journal = {Astronomy and Astrophysics}, - archivePrefix = "arXiv", - eprint = {1609.04153}, - primaryClass = "astro-ph.IM", - keywords = {space vehicles: instruments, Galaxy: structure, astrometry, parallaxes, proper motions, telescopes}, - year = 2016, - month = nov, - volume = 595, - doi = {10.1051/0004-6361/201629272}, - url = {http://adsabs.harvard.edu/abs/2016A%26A...595A...1G}, -} - -@article{astropy, - author = {{Astropy Collaboration}}, - title = "{Astropy: A community Python package for astronomy}", - journal = {Astronomy and Astrophysics}, - archivePrefix = "arXiv", - eprint = {1307.6212}, - primaryClass = "astro-ph.IM", - keywords = {methods: data analysis, methods: miscellaneous, virtual observatory tools}, - year = 2013, - month = oct, - volume = 558, - doi = {10.1051/0004-6361/201322068}, - url = {http://adsabs.harvard.edu/abs/2013A%26A...558A..33A} -} - -@misc{fidgit, - author = {A. M. Smith and K. Thaney and M. Hahnel}, - title = {Fidgit: An ungodly union of GitHub and Figshare}, - year = {2020}, - publisher = {GitHub}, - journal = {GitHub repository}, - url = {https://github.com/arfon/fidgit} +@article{davidson2019lifelines, + title={lifelines: survival analysis in Python}, + author={Davidson-Pilon, Cameron}, + journal={Journal of Open Source Software}, + volume={4}, + number={40}, + pages={1317}, + year={2019} +} + +@article{therneau2015survival, + title={Package ‘survival’}, + author={Therneau, Terry M and Lumley, Thomas}, + journal={R Top Doc}, + volume={128}, + number={10}, + pages={28--33}, + year={2015} +} + +@article{paszke2019pytorch, + title={Pytorch: An imperative style, high-performance deep learning library}, + author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others}, + journal={Advances in neural information processing systems}, + volume={32}, + year={2019} +} + +@article{polsterl2020scikit, + title={scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn}, + author={P{\"o}lsterl, Sebastian}, + journal={The Journal of Machine Learning Research}, + volume={21}, + number={1}, + pages={8747--8752}, + year={2020}, + publisher={JMLRORG} +} + +@article{potapov2023packagesurvAUC, + title={Package ‘survAUC’}, + author={Potapov, Sergej and Adler, Werner and Schmid, Matthias and Potapov, Maintainer Sergej}, + journal={Statistics in Medicine}, + volume={25}, + pages={3474--3486}, + year={2023} +} + +@inproceedings{he2020momentum, + title={Momentum contrast for unsupervised visual representation learning}, + author={He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross}, + booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, + pages={9729--9738}, + year={2020} +} + +@article{katzman2018deepsurv, + title={DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network}, + author={Katzman, Jared L and Shaham, Uri and Cloninger, Alexander and Bates, Jonathan and Jiang, Tingting and Kluger, Yuval}, + journal={BMC medical research methodology}, + volume={18}, + number={1}, + pages={1--12}, + year={2018}, + publisher={BioMed Central} +} + +@article{Kvamme2019pycox, + author = {H{{\aa}}vard Kvamme and {{\O}}rnulf Borgan and Ida Scheel}, + title = {Time-to-Event Prediction with Neural Networks and Cox Regression}, + journal = {Journal of Machine Learning Research}, + year = {2019}, + volume = {20}, + number = {129}, + pages = {1--30}, + url = {http://jmlr.org/papers/v20/18-424.html} +} + +@inproceedings{nagpal2022auton, + title={auton-survival: An open-source package for regression, counterfactual estimation, evaluation and phenotyping with censored time-to-event data}, + author={Nagpal, Chirag and Potosnak, Willa and Dubrawski, Artur}, + booktitle={Machine Learning for Healthcare Conference}, + pages={585--608}, + year={2022}, + organization={PMLR} +} + +@article{Cox1972, + title = {Regression Models and Life‐Tables}, + volume = {34}, + ISSN = {2517-6161}, + number = {2}, + journal = {Journal of the Royal Statistical Society: Series B (Methodological)}, + publisher = {Wiley}, + author = {Cox, D. R.}, + year = {1972}, + month = jan, + pages = {187–202} +} + +@article{Kzlers2018, + title = {The Weibull Distribution}, + volume = {15}, + ISSN = {1740-9713}, + number = {2}, + journal = {Significance}, + publisher = {Oxford University Press (OUP)}, + author = {Kızılers\"{u}, Ayşe and Kreer, Markus and Thomas, Anthony W.}, + year = {2018}, + month = apr, + pages = {10–11} +} + +@article{Carroll2003, + title = {On the use and utility of the Weibull model in the analysis of survival data}, + volume = {24}, + ISSN = {0197-2456}, + url = {http://dx.doi.org/10.1016/S0197-2456(03)00072-2}, + DOI = {10.1016/s0197-2456(03)00072-2}, + number = {6}, + journal = {Controlled Clinical Trials}, + publisher = {Elsevier BV}, + author = {Carroll, Kevin J.}, + year = {2003}, + month = dec, + pages = {682–701} +} + +@Manual{survivalpackage, + title = {A Package for Survival Analysis in R}, + author = {Terry M Therneau}, + year = {2024}, + note = {R package version 3.5-8}, + url = {https://CRAN.R-project.org/package=survival}, + } + +@Manual{torchlifeAbeywardana, + title = {torchlife: Survival Analysis using pytorch}, + author = {Sachinthaka Abeywardana}, + year = {2021}, + url = {https://sachinruk.github.io/torchlife//index.html}, + } + +@Manual{survAUCpackage, + title = {Estimators of prediction accuracy for time-to-event data}, + author = {Sergej Potapov and Werner Adler and Matthias Schmid}, + year = {2023}, + note = {R package version 1.2-0}, + url = {https://CRAN.R-project.org/package=survAUC}, + } + +@Manual{timeROCpackage, + title = {Time-Dependent ROC Curve and AUC for Censored Survival Data}, + author = {Paul Blanche}, + year = {2019}, + note = {R package version 0.4}, + url = {https://CRAN.R-project.org/package=timeROC}, + } + +@Manual{risksetROCpackage, + title = {Riskset ROC Curve Estimation from Censored Survival Data}, + author = {Patrick J. Heagerty}, + year = {2022}, + note = {R package version 1.0.4.1}, + url = {https://CRAN.R-project.org/package=risksetROC}, + } + +@Manual{survivalROCpackage, + title = {Time-Dependent ROC Curve Estimation from Censored Survival Data}, + author = {Patrick J. Heagerty}, + year = {2022}, + note = {R package version 1.0.3.1}, + url = {https://CRAN.R-project.org/package=survivalROC}, + } + +@article{survcomppackage, + title = {survcomp: an R/Bioconductor package for performance assessment and comparison of survival models}, + volume = {27}, + ISSN = {1367-4803}, + url = {http://dx.doi.org/10.1093/bioinformatics/btr511}, + DOI = {10.1093/bioinformatics/btr511}, + number = {22}, + journal = {Bioinformatics}, + publisher = {Oxford University Press (OUP)}, + author = {Schr\"{o}der, Markus S. and Culhane, Aedín C. and Quackenbush, John and Haibe-Kains, Benjamin}, + year = {2011}, + month = sep, + pages = {3206–3208} +} + +@Manual{riskRegressionpackage, + title = {Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks}, + author = {Thomas Alexander Gerds and Johan Sebastian Ohlendorff and Paul Blanche and Rikke Mortensen and Marvin Wright and Nikolaj Tollenaar and John Muschelli and Ulla Brasch Mogensen and Brice Ozenne}, + year = {2023}, + note = {R package version 2023.12.21}, + url = {https://CRAN.R-project.org/package=riskRegression}, + } + +@Manual{SurvMetricspackage, + title = {Predictive Evaluation Metrics in Survival Analysis}, + author = {Hanpu Zhou and Xuewei Cheng and Sizheng Wang and Yi Zou and Hong Wang}, + year = {2022}, + note = {R package version 0.5.0}, + url = {https://CRAN.R-project.org/package=SurvMetrics}, + } + +@Manual{pecpackage, + title = {Prediction Error Curves for Risk Prediction Models in Survival Analysis}, + author = {Thomas A. Gerds}, + year = {2023}, + note = {R package version 2023.04.12}, + url = {https://CRAN.R-project.org/package=pec}, + } + +@article{Heagerty2000, + title = {Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker}, + volume = {56}, + ISSN = {1541-0420}, + url = {http://dx.doi.org/10.1111/j.0006-341x.2000.00337.x}, + DOI = {10.1111/j.0006-341x.2000.00337.x}, + number = {2}, + journal = {Biometrics}, + publisher = {Oxford University Press (OUP)}, + author = {Heagerty, Patrick J. and Lumley, Thomas and Pepe, Margaret S.}, + year = {2000}, + month = jun, + pages = {337–344} +} + +@article{Heagerty2005, + author = {Heagerty, Patrick J. and Zheng, Yingye}, + doi = {10.1111/j.0006-341x.2005.030814.x}, + issn = {1541-0420}, + journal = {Biometrics}, + month = {February}, + number = {1}, + pages = {92–105}, + publisher = {Oxford University Press (OUP)}, + title = {Survival Model Predictive Accuracy and ROC Curves}, + url = {http://dx.doi.org/10.1111/j.0006-341x.2005.030814.x}, + volume = {61}, + year = {2005} +} + + +@article{Blanche2013, + title = {Review and comparison of ROC curve estimators for a time‐dependent outcome with marker‐dependent censoring}, + volume = {55}, + ISSN = {1521-4036}, + url = {http://dx.doi.org/10.1002/bimj.201200045}, + DOI = {10.1002/bimj.201200045}, + number = {5}, + journal = {Biometrical Journal}, + publisher = {Wiley}, + author = {Blanche, Paul and Dartigues, Jean‐Fran\c{c}ois and Jacqmin‐Gadda, Hélène}, + year = {2013}, + month = jun, + pages = {687–704} +} + +@article{Uno2007, + title = {Evaluating Prediction Rules fort-Year Survivors With Censored Regression Models}, + volume = {102}, + ISSN = {1537-274X}, + url = {http://dx.doi.org/10.1198/016214507000000149}, + DOI = {10.1198/016214507000000149}, + number = {478}, + journal = {Journal of the American Statistical Association}, + publisher = {Informa UK Limited}, + author = {Uno, Hajime and Cai, Tianxi and Tian, Lu and Wei, L. J}, + year = {2007}, + month = jun, + pages = {527–537} +} + +@article{Blanche2018, + author = {Blanche, Paul and Kattan, Michael W and Gerds, Thomas A}, + doi = {10.1093/biostatistics/kxy006}, + issn = {1468-4357}, + journal = {Biostatistics}, + month = {February}, + number = {2}, + pages = {347–357}, + publisher = {Oxford University Press (OUP)}, + title = {The c-index is not proper for the evaluation of $t$-year predicted risks}, + url = {http://dx.doi.org/10.1093/biostatistics/kxy006}, + volume = {20}, + year = {2018} +} + +@article{Uno_2011, + author = {Uno, Hajime and Cai, Tianxi and Pencina, Michael J. and D’Agostino, Ralph B. and Wei, L. J.}, + doi = {10.1002/sim.4154}, + issn = {1097-0258}, + journal = {Statistics in Medicine}, + month = {January}, + number = {10}, + pages = {1105–1117}, + publisher = {Wiley}, + title = {On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data}, + url = {http://dx.doi.org/10.1002/sim.4154}, + volume = {30}, + year = {2011} +} + +@article{Harrell1996, + author = {Harrell, Frank E. and Lee, Kerry L. and Mark, Daniel B.}, + doi = {10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4}, + issn = {1097-0258}, + journal = {Statistics in Medicine}, + month = {February}, + number = {4}, + pages = {361–387}, + publisher = {Wiley}, + title = {Multivariate Prognostic Models: Issues in Developing models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors}, + url = {http://dx.doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4}, + volume = {15}, + year = {1996} +} + +@article{Graf_1999, + author = {Graf, Erika and Schmoor, Claudia and Sauerbrei, Willi and Schumacher, Martin}, + doi = {10.1002/(sici)1097-0258(19990915/30)18:17/18<2529::aid-sim274>3.0.co;2-5}, + issn = {1097-0258}, + journal = {Statistics in Medicine}, + month = {September}, + number = {17–18}, + pages = {2529–2545}, + publisher = {Wiley}, + title = {Assessment and comparison of prognostic classification schemes for survival data}, + url = {http://dx.doi.org/10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5}, + volume = {18}, + year = {1999} +} + +@article{Efron1977, + title = {The Efficiency of Cox’s Likelihood Function for Censored Data}, + volume = {72}, + ISSN = {1537-274X}, + url = {http://dx.doi.org/10.1080/01621459.1977.10480613}, + DOI = {10.1080/01621459.1977.10480613}, + number = {359}, + journal = {Journal of the American Statistical Association}, + publisher = {Informa UK Limited}, + author = {Efron, Bradley}, + year = {1977}, + month = sep, + pages = {557–565} +} + +@article{Breslow1975, + title = {Analysis of Survival Data under the Proportional Hazards Model}, + volume = {43}, + ISSN = {0306-7734}, + url = {http://dx.doi.org/10.2307/1402659}, + DOI = {10.2307/1402659}, + number = {1}, + journal = {International Statistical Review / Revue Internationale de Statistique}, + publisher = {JSTOR}, + author = {Breslow, N. E.}, + year = {1975}, + month = apr, + pages = {45} } \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index 40b1b79..bff1cae 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,107 +1,169 @@ --- -title: '\texttt{TorchSurv}: A Lightweight Package for Deep Survival Analysis' +title: 'TorchSurv: A Lightweight Package for Deep Survival Analysis' tags: - - Python - - radiomics - - medical imaging + - Python package + - Deep Survival Analysis + - PyTorch + - TorchSurv authors: - - name: Alex Zwanenburg - orcid: 0000-0002-0342-9545 - affiliation: "1, 2" - - name: Steffen Löck - orcid: 0000-0002-7017-3738 - affiliation: "2, 3, 4" -affiliations: - - name: "National Center for Tumor Diseases Dresden (NCT/UCC), Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany" + - name: Mélodie Monod + orcid: 0000-0001-6448-2051 + affiliation: "1" + - name: Peter Krusche + affiliation: "1" + - name: Qian Cao + affiliation: "2" + - name: Berkman Sahiner + affiliation: "2" + - name: Nicholas Petrick + affiliation: "2" + - name: David Ohlssen + affiliation: "3" + - name: Thibaud Coroller + orcid: 0000-0001-7662-8724 + affiliation: "3" +affiliations:Pm + - name: Novartis Pharma AG, Switzerland index: 1 - - name: OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl - Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany + - name: Center for Devices and Radiological Health, Food and Drug Administration, MD, USA index: 2 - - name: German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany + - name: Novartis Pharmaceuticals Corporation, NJ, USA index: 3 - - name: Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany - index: 4 -date: 7 February 2024 +date: 18 July 2024 bibliography: paper.bib --- # Summary -The forces on stars, galaxies, and dark matter under external gravitational -fields lead to the dynamical evolution of structures in the universe. The orbits -of these bodies are therefore key to understanding the formation, history, and -future state of galaxies. The field of "galactic dynamics," which aims to model -the gravitating components of galaxies to study their structure and evolution, -is now well-established, commonly taught, and frequently used in astronomy. -Aside from toy problems and demonstrations, the majority of problems require -efficient numerical tools, many of which require the same base code (e.g., for -performing numerical orbit integration). +`TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive survival model parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex input data scenarios. +`TorchSurv` has undergone rigorous testing on open source data and synthetically generated survival data that include edge cases. The package is comprehensively documented and contains illustrative examples. The latest documentation of `TorchSurv` can be found on [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). + +`TorchSurv` provides a user-friendly workflow for defining a survival model with parameters specified by a `PyTorch`-based (deep) neural network. At the core of `TorchSurv` lies its `PyTorch`-based calculation of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. +In survival analysis, each observation is associated with survival data denoted by $y$ (comprising the event indicator and the time-to-event or censoring) and covariates denoted by $x$. A survival model that is able to capture the complexity of the survival data $y$, is parametrized by parameters denoted by $\theta$. For instance, in the Cox proportional hazards model, the survival model parameters $\theta$ are the relative hazards. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the survival model parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. +Additionally, `TorchSurv` offers evaluation metrics (the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic curve (ROC), the Concordance index (C-index) and the Brier Score) to characterize the predictive performance of survival models. +Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: + +1. Initialize a `PyTorch`-based neural network that defines the function from the covariates to the survival model's parameters. In the context of the Cox relative hazards model for example, the parameters are the log relative hazards. +2. Initiate training: For each epoch on the training set, + - Draw survival data $y^{\text{train}}$ (i.e., event indicator and time-to-event or censoring) and covariates $x^{\text{train}}$ from the training set. + - Obtain parameters $\theta^{\text{train}}$ based on drawn covariates $x^{\text{train}}$ using `PyTorch`-based neural network. + - Calculate the loss given survival data $y^{\text{train}}$ and parameters $\theta^{\text{train}}$ using `TorchSurv`'s loss function. In the context of the Cox relative hazards model for example, the loss function is equal to the negative of the log partial likelihood. + - Utilize backpropagation to update parameters $\theta^{\text{train}}$. +3. Obtain parameters $\theta^{\text{test}}$ based on covariates from the test set $x^{\text{test}}$ using the trained `PyTorch`-based neural network. +4. Evaluate the predictive performance of the model using `TorchSurv`'s evaluation metric functions (e.g., C-index) given parameters $\theta^{\text{test}}$ and survival data from the test set $y^{\text{test}}$. + + # Statement of need -`Gala` is an Astropy-affiliated Python package for galactic dynamics. Python -enables wrapping low-level languages (e.g., C) for speed without losing -flexibility or ease-of-use in the user-interface. The API for `Gala` was -designed to provide a class-based and user-friendly interface to fast (C or -Cython-optimized) implementations of common operations such as gravitational -potential and force evaluation, orbit integration, dynamical transformations, -and chaos indicators for nonlinear dynamics. `Gala` also relies heavily on and -interfaces well with the implementations of physical units and astronomical -coordinate systems in the `Astropy` package [@astropy] (`astropy.units` and -`astropy.coordinates`). +Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning offers promising avenues for building complex survival models. However, existing libraries often restrict users to predefined parameter forms and limit seamless integration with `PyTorch`. + +Table 1 compares the functionalities of `TorchSurv` with those of +`auton-survival` [@nagpal2022auton], +`pycox` [@Kvamme2019pycox], +`torchlife` [@torchlifeAbeywardana], +`scikit-survival` [@polsterl2020scikit], +`lifelines` [@davidson2019lifelines], and +`deepsurv` [@katzman2018deepsurv]. +While several libraries offer survival modelling functionalities, as shown in Table 1, no existing library provides the flexibility to use a custom `PyTorch`-based neural network to define the survival model parameters $\theta$ given a set of covariates $x$. In existing libraries, users are limited to specific forms to define $\theta$ (e.g., linear function of covariates) and the log-likelihood functions available cannot be leveraged because they do not allow for seamless integration with `PyTorch`. +Specifically, the limitations on the log-likelihood functions include protected functions, specialized input requirements (format or class type), and reliance on external libraries like `NumPy` or `Pandas`. Dependence on external libraries hinders automatic gradient calculation within `PyTorch`. Additionally, the implementation of likelihood functions instead of log-likelihood functions, as done by some packages, introduces potential numerical instability. +With respect to the evaluation metrics, `scikit-survival` stands out as a comprehensive library. However, it lacks certain desirable features, including confidence intervals and comparison of the evaluation metric between two different models, and it is implemented with `NumPy`. +Our package, `TorchSurv`, is specifically designed for use in Python, but we also provide a comparative analysis of its functionalities with popular `R` packages for survival analysis in Table 2. `R` packages do not make log-likelihood functions readily accessible and restrict users to specific forms to define $\theta$. However, `R` has extensive libraries for evaluation metrics, such as the `RiskRegression` library [@riskRegressionpackage]. `TorchSurv` offers a comparable range of evaluation metrics, ensuring comprehensive model evaluation regardless of the chosen programming environment. + +The outputs of both the log-likelihood functions and the evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is summarized in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). + +# Functionality + +## Loss functions + +**Cox loss function.** The Cox loss function is defined as the negative of the Cox proportional hazards model's partial log-likelihood [@Cox1972]. The function requires the subject-specific log relative hazards and the survival data (i.e., event indicator and time-to-event or censoring). The log relative hazards should be obtained from a `PyTorch`-based model pre-specified by the user. In case of ties in the event times, the user can choose between the Breslow [@Breslow1975] and the Efron method [@Efron1977] to approximate the Cox partial log likelihood. We illustrate the use of the Cox loss function for a pseudo training loop in the code snippet below. -`Gala` was designed to be used by both astronomical researchers and by -students in courses on gravitational dynamics or astronomy. It has already been -used in a number of scientific publications [@Pearson:2017] and has also been -used in graduate courses on Galactic dynamics to, e.g., provide interactive -visualizations of textbook material [@Binney:2008]. The combination of speed, -design, and support for Astropy functionality in `Gala` will enable exciting -scientific explorations of forthcoming data releases from the *Gaia* mission -[@gaia] by students and experts alike. +```python +from torchsurv.loss import cox +my_model = MyPyTorchModel() # PyTorch model for log hazards (1 output) +for data in dataloader: + x, event, time = data + log_hzs = my_model(x) # torch.Size([64, 1]), if batch size is 64 + loss = cox.neg_partial_log_likelihood(log_hzs, event, time) + loss.backward() # native torch backend +``` -# Mathematics +**Weibull loss function.** The Weibull loss function is defined as the negative of the Weibull AFT's log-likelihood [@Carroll2003]. The function requires the subject-specific log parameters of the Weibull distribution (i.e., the log scale and the log shape) and the survival data. The log parameters of the Weibull distribution should be obtained from a `PyTorch`-based model pre-specified by the user. We illustrate the use of the Weibull loss function for a pseudo training loop in the code snippet below. -Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$ +```python +from torchsurv.loss import weibull +my_model = MyPyTorchModel() # PyTorch model for log parameters (2 outputs) +for data in dataloader: + x, event, time = data + log_params = my_model(x) # torch.Size([64, 2]), if batch size is 64 + loss = weibull.neg_log_likelihood(log_params, event, time) + loss.backward() # native torch backend +``` -Double dollars make self-standing equations: +**Momentum** + When training a model with a large file, the batch size is greatly limited by computational resources. This impacts the stability of model optimization, especially when rank-based loss is used. Inspired from MoCO \citep{he2020momentum}, we implemented a momentum loss that decouples batch size from survival loss, increasing the effective batch size and allowing robust train of a model, even when using a very limited batch size (e.g., $batch_{size} \leq 16$). We illustrate the use of momentum for a pseudo training loop in the code +snippet below. -$$\Theta(x) = \left\{\begin{array}{l} -0\textrm{ if } x < 0\cr -1\textrm{ else} -\end{array}\right.$$ +```python +from torchsurv.loss import Momentum +my_model = MyPyTorchModel() # PyTorch model for log hazards (1 output) +my_loss = cox.neg_partial_log_likelihood # any torchsurv loss +momentum = Momentum(backbone=my_model, loss=my_loss) -You can also use plain \LaTeX for equations -\begin{equation}\label{eq:fourier} -\hat f(\omega) = \int_{-\infty}^{\infty} f(x) e^{i\omega x} dx -\end{equation} -and refer to \autoref{eq:fourier} from text. +for data in dataloader: + x, event, time = data + loss = model_momentum(x, event, time) # torch.Size([64, 1]) + loss.backward() # native torch backend -# Citations +# Inference is computed with target network (k) +log_hzs = model_momentum.infer(x) # torch.Size([64, 1]) +``` -Citations to entries in paper.bib should be in -[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) -format. +## Evaluation Metrics Functions -If you want to cite a software repository URL (e.g. something on GitHub without a preferred -citation) then you can do it with the example BibTeX entry below for @fidgit. +The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the individual risk score estimated on the test set and the survival data on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. -For a quick reference, the following citation commands can be used: -- `@author:2001` -> "Author et al. (2001)" -- `[@author:2001]` -> "(Author et al., 2001)" -- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" +**AUC.** The AUC measures the discriminatory capacity of a model at a given time $t$, i.e., the model’s ability to provide a reliable ranking of times-to-event based on estimated individual risk scores [@Heagerty2005;@Uno2007;@Blanche2013]. -# Figures +```python +from torchsurv.metrics import Auc +auc = Auc() +auc(log_hzs, event, time) # AUC at each time +auc(log_hzs, event, time, new_time=torch.tensor(10.)) # AUC at time 10 +``` -Figures can be included like this: -![Caption for example figure.\label{fig:example}](figure.png) -and referenced from text using \autoref{fig:example}. +**C-index.** The C-index is a generalization of the AUC that represents the assessment of the discriminatory capacity of the model over time [@Harrell1996;@Uno_2011]. -Figure sizes can be customized by adding an optional second parameter: -![Caption for example figure.](figure.png){ width=20% } +```python +from torchsurv.metrics import ConcordanceIndex +cindex = ConcordanceIndex() +cindex(log_hzs, event, time) # c-index +``` -# Acknowledgements +**Brier Score.** The Brier score evaluates the accuracy of a model at a given time $t$. It represents the average squared distance between the observed survival status and the predicted survival probability [@Graf_1999]. The Brier score cannot be obtained for the Cox model because the survival function is not available, but it can be obtained for the Weibull model. + +```python +from torchsurv.metrics import Brier +surv = survival_function(log_params, time) +brier = Brier() +brier(surv, event, time) # Brier score at each time +brier.integral() # integrated brier score +``` + + +**Additional features.** In \texttt{TorchSurv}, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). +Lastly, functionalities including the confidence interval, one-sample hypothesis test to determine whether the metric is better than that of a random predictor, and two-sample hypothesis test to compare two evaluation metrics between different models are implemented. For the hypothesis tests, the significance level, typically referred to as $\alpha$, can be modified as needed. The following code snippet exemplifies the aforementioned functionalities for the C-index. + +```python +cindex.confidence_interval() # CI, default alpha = .05 +cindex.p_value(alternative='greater') # pvalue, H0: c = 0.5, HA: c > 0.5 +cindex.compare(cindex_other) # pvalue, H0: c1 = c2, HA: c1 > c2 +``` + +# Conflicts of interest + +MM, PK, DO and TC are employees and stockholders of Novartis, a global pharmaceutical company. -We acknowledge contributions from Brigitta Sipocz, Syrtis Major, and Semyeong -Oh, and support from Kathryn Johnston during the genesis of this project. # References + From 0ef1fe345aa1fd922175d4cbd2840bc309c0747d Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Thu, 18 Jul 2024 19:00:08 +0100 Subject: [PATCH 08/22] fix bug in header --- paper/paper.md | 1 - 1 file changed, 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index bff1cae..1c77620 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -22,7 +22,6 @@ authors: - name: Thibaud Coroller orcid: 0000-0001-7662-8724 affiliation: "3" -affiliations:Pm - name: Novartis Pharma AG, Switzerland index: 1 - name: Center for Devices and Radiological Health, Food and Drug Administration, MD, USA From 503fc41365ff55b84b1f1f041a0fde3928192e65 Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Thu, 18 Jul 2024 19:02:47 +0100 Subject: [PATCH 09/22] fix bug with citep --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 1c77620..270cbc2 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -100,7 +100,7 @@ for data in dataloader: ``` **Momentum** - When training a model with a large file, the batch size is greatly limited by computational resources. This impacts the stability of model optimization, especially when rank-based loss is used. Inspired from MoCO \citep{he2020momentum}, we implemented a momentum loss that decouples batch size from survival loss, increasing the effective batch size and allowing robust train of a model, even when using a very limited batch size (e.g., $batch_{size} \leq 16$). We illustrate the use of momentum for a pseudo training loop in the code + When training a model with a large file, the batch size is greatly limited by computational resources. This impacts the stability of model optimization, especially when rank-based loss is used. Inspired from MoCO [@he2020momentum], we implemented a momentum loss that decouples batch size from survival loss, increasing the effective batch size and allowing robust train of a model, even when using a very limited batch size (e.g., $batch_{size} \leq 16$). We illustrate the use of momentum for a pseudo training loop in the code snippet below. ```python @@ -150,7 +150,7 @@ brier.integral() # integrated brier score ``` -**Additional features.** In \texttt{TorchSurv}, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). +**Additional features.** In `TorchSurv`, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). Lastly, functionalities including the confidence interval, one-sample hypothesis test to determine whether the metric is better than that of a random predictor, and two-sample hypothesis test to compare two evaluation metrics between different models are implemented. For the hypothesis tests, the significance level, typically referred to as $\alpha$, can be modified as needed. The following code snippet exemplifies the aforementioned functionalities for the C-index. ```python From 20918912894ec04d891e5c947467c7ed2daae93d Mon Sep 17 00:00:00 2001 From: corolth1 Date: Fri, 19 Jul 2024 10:06:31 -0400 Subject: [PATCH 10/22] fixed typos --- paper/paper.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 270cbc2..6c4c539 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,14 +37,15 @@ bibliography: paper.bib `TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive survival model parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex input data scenarios. `TorchSurv` has undergone rigorous testing on open source data and synthetically generated survival data that include edge cases. The package is comprehensively documented and contains illustrative examples. The latest documentation of `TorchSurv` can be found on [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). -`TorchSurv` provides a user-friendly workflow for defining a survival model with parameters specified by a `PyTorch`-based (deep) neural network. At the core of `TorchSurv` lies its `PyTorch`-based calculation of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. +`TorchSurv` provides a user-friendly workflow for defining a survival model with parameters specified by a `PyTorch`-based (deep) neural network. At the core of `TorchSurv` lies its `PyTorch`-based calculation of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. + In survival analysis, each observation is associated with survival data denoted by $y$ (comprising the event indicator and the time-to-event or censoring) and covariates denoted by $x$. A survival model that is able to capture the complexity of the survival data $y$, is parametrized by parameters denoted by $\theta$. For instance, in the Cox proportional hazards model, the survival model parameters $\theta$ are the relative hazards. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the survival model parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. Additionally, `TorchSurv` offers evaluation metrics (the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic curve (ROC), the Concordance index (C-index) and the Brier Score) to characterize the predictive performance of survival models. Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: 1. Initialize a `PyTorch`-based neural network that defines the function from the covariates to the survival model's parameters. In the context of the Cox relative hazards model for example, the parameters are the log relative hazards. 2. Initiate training: For each epoch on the training set, - - Draw survival data $y^{\text{train}}$ (i.e., event indicator and time-to-event or censoring) and covariates $x^{\text{train}}$ from the training set. + - Draw survival data $y^{\text{train}}$ (i.e., event indicator and time-to-event or censoring) and covariates $x^{\text{train}}$ from the training set. - Obtain parameters $\theta^{\text{train}}$ based on drawn covariates $x^{\text{train}}$ using `PyTorch`-based neural network. - Calculate the loss given survival data $y^{\text{train}}$ and parameters $\theta^{\text{train}}$ using `TorchSurv`'s loss function. In the context of the Cox relative hazards model for example, the loss function is equal to the negative of the log partial likelihood. - Utilize backpropagation to update parameters $\theta^{\text{train}}$. @@ -52,7 +53,6 @@ Below is an overview of the workflow for model inference and evaluation with `To 4. Evaluate the predictive performance of the model using `TorchSurv`'s evaluation metric functions (e.g., C-index) given parameters $\theta^{\text{test}}$ and survival data from the test set $y^{\text{test}}$. - # Statement of need Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning offers promising avenues for building complex survival models. However, existing libraries often restrict users to predefined parameter forms and limit seamless integration with `PyTorch`. @@ -120,9 +120,9 @@ log_hzs = model_momentum.infer(x) # torch.Size([64, 1]) ## Evaluation Metrics Functions -The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the individual risk score estimated on the test set and the survival data on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. +The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the individual risk score estimated on the test set and the survival data on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. -**AUC.** The AUC measures the discriminatory capacity of a model at a given time $t$, i.e., the model’s ability to provide a reliable ranking of times-to-event based on estimated individual risk scores [@Heagerty2005;@Uno2007;@Blanche2013]. +**AUC.** The AUC measures the discriminatory capacity of a model at a given time $t$, i.e., the model’s ability to provide a reliable ranking of times-to-event based on estimated individual risk scores [@Heagerty2005;@Uno2007;@Blanche2013]. ```python from torchsurv.metrics import Auc @@ -149,7 +149,6 @@ brier(surv, event, time) # Brier score at each time brier.integral() # integrated brier score ``` - **Additional features.** In `TorchSurv`, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). Lastly, functionalities including the confidence interval, one-sample hypothesis test to determine whether the metric is better than that of a random predictor, and two-sample hypothesis test to compare two evaluation metrics between different models are implemented. For the hypothesis tests, the significance level, typically referred to as $\alpha$, can be modified as needed. The following code snippet exemplifies the aforementioned functionalities for the C-index. From de450c6c661bd18d26b7fa7679983d3fd6392692 Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Fri, 19 Jul 2024 16:57:30 +0100 Subject: [PATCH 11/22] add table to paper --- paper/paper.md | 71 ++++++++++++++++++++++++++-------------------- paper/table_1.png | Bin 0 -> 243356 bytes paper/table_2.png | Bin 0 -> 237894 bytes 3 files changed, 41 insertions(+), 30 deletions(-) create mode 100644 paper/table_1.png create mode 100644 paper/table_2.png diff --git a/paper/paper.md b/paper/paper.md index 270cbc2..327f35d 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -35,72 +35,83 @@ bibliography: paper.bib # Summary `TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive survival model parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex input data scenarios. -`TorchSurv` has undergone rigorous testing on open source data and synthetically generated survival data that include edge cases. The package is comprehensively documented and contains illustrative examples. The latest documentation of `TorchSurv` can be found on [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). +`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the[`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). -`TorchSurv` provides a user-friendly workflow for defining a survival model with parameters specified by a `PyTorch`-based (deep) neural network. At the core of `TorchSurv` lies its `PyTorch`-based calculation of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. -In survival analysis, each observation is associated with survival data denoted by $y$ (comprising the event indicator and the time-to-event or censoring) and covariates denoted by $x$. A survival model that is able to capture the complexity of the survival data $y$, is parametrized by parameters denoted by $\theta$. For instance, in the Cox proportional hazards model, the survival model parameters $\theta$ are the relative hazards. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the survival model parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. -Additionally, `TorchSurv` offers evaluation metrics (the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic curve (ROC), the Concordance index (C-index) and the Brier Score) to characterize the predictive performance of survival models. +`TorchSurv` provides a user-friendly workflow for training and evaluating a `PyTorch`-based deep survival model. +At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. +In survival analysis, each observation is associated with survival reponse, denoted by $y$ (comprising the event indicator and the time-to-event or censoring), and covariates denoted by $x$. A survival model is parametrized by parameters, denoted by $\theta$. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. +Additionally, `TorchSurv` offers evaluation metrics, including the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic (ROC) curve, the Concordance index (C-index) and the Brier Score, to characterize the predictive performance of survival models. Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: -1. Initialize a `PyTorch`-based neural network that defines the function from the covariates to the survival model's parameters. In the context of the Cox relative hazards model for example, the parameters are the log relative hazards. +1. Initialize a `PyTorch`-based neural network that defines the function from the covariates $x$ to the parameters $\theta$. In the context of the Cox proportional hazards model for example, the parameters are the log relative hazards. 2. Initiate training: For each epoch on the training set, - - Draw survival data $y^{\text{train}}$ (i.e., event indicator and time-to-event or censoring) and covariates $x^{\text{train}}$ from the training set. - - Obtain parameters $\theta^{\text{train}}$ based on drawn covariates $x^{\text{train}}$ using `PyTorch`-based neural network. - - Calculate the loss given survival data $y^{\text{train}}$ and parameters $\theta^{\text{train}}$ using `TorchSurv`'s loss function. In the context of the Cox relative hazards model for example, the loss function is equal to the negative of the log partial likelihood. - - Utilize backpropagation to update parameters $\theta^{\text{train}}$. -3. Obtain parameters $\theta^{\text{test}}$ based on covariates from the test set $x^{\text{test}}$ using the trained `PyTorch`-based neural network. -4. Evaluate the predictive performance of the model using `TorchSurv`'s evaluation metric functions (e.g., C-index) given parameters $\theta^{\text{test}}$ and survival data from the test set $y^{\text{test}}$. + - Draw survival response $y^{\text{train}}$ and covariates $x^{\text{train}}$ from the training set. + - Obtain parameters $\theta^{\text{train}}$ given covariates $x^{\text{train}}$ using the neural network. + - Calculate the loss as the negative log-likelihood of survival response $y^{\text{train}}$ given parameters $\theta^{\text{train}}$. This calculation is facilitated by `TorchSurv`'s loss function. + - Utilize backpropagation to update the neural network's parameters. +3. Obtain parameters $\theta^{\text{test}}$ given covariates from the test set $x^{\text{test}}$ using the trained neural network. +4. Evaluate the predictive performance of the model using `TorchSurv`'s evaluation metric functions given parameters $\theta^{\text{test}}$ and survival response from the test set $y^{\text{test}}$. # Statement of need -Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning offers promising avenues for building complex survival models. However, existing libraries often restrict users to predefined parameter forms and limit seamless integration with `PyTorch`. +Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning presents promising opportunities for developing sophisticated survival models, where the parameters depend on covariates through complex functions. However, no existing library provides the flexibility to define survival model parameters using a custom `PyTorch`-based neural network. -Table 1 compares the functionalities of `TorchSurv` with those of +Figure \autoref{tab:bibliography} compares the functionalities of `TorchSurv` with those of `auton-survival` [@nagpal2022auton], `pycox` [@Kvamme2019pycox], `torchlife` [@torchlifeAbeywardana], `scikit-survival` [@polsterl2020scikit], `lifelines` [@davidson2019lifelines], and -`deepsurv` [@katzman2018deepsurv]. -While several libraries offer survival modelling functionalities, as shown in Table 1, no existing library provides the flexibility to use a custom `PyTorch`-based neural network to define the survival model parameters $\theta$ given a set of covariates $x$. In existing libraries, users are limited to specific forms to define $\theta$ (e.g., linear function of covariates) and the log-likelihood functions available cannot be leveraged because they do not allow for seamless integration with `PyTorch`. -Specifically, the limitations on the log-likelihood functions include protected functions, specialized input requirements (format or class type), and reliance on external libraries like `NumPy` or `Pandas`. Dependence on external libraries hinders automatic gradient calculation within `PyTorch`. Additionally, the implementation of likelihood functions instead of log-likelihood functions, as done by some packages, introduces potential numerical instability. +`deepsurv` [@katzman2018deepsurv]. +Existing libraries constrain users to predefined forms for defining the parameters (e.g., linear function of covariates). While there exist log-likelihood functions in the libraries, they cannot be leveraged. +Specifically, the limitations on the log-likelihood functions include protected functions, specialized input requirements (format or class type), and reliance on external libraries like `NumPy` or `Pandas`. Dependence on external libraries hinders automatic gradient calculation within `PyTorch`. Additionally, the implementation of likelihood functions instead of log-likelihood functions, as done by some packages, introduces numerical instability. With respect to the evaluation metrics, `scikit-survival` stands out as a comprehensive library. However, it lacks certain desirable features, including confidence intervals and comparison of the evaluation metric between two different models, and it is implemented with `NumPy`. -Our package, `TorchSurv`, is specifically designed for use in Python, but we also provide a comparative analysis of its functionalities with popular `R` packages for survival analysis in Table 2. `R` packages do not make log-likelihood functions readily accessible and restrict users to specific forms to define $\theta$. However, `R` has extensive libraries for evaluation metrics, such as the `RiskRegression` library [@riskRegressionpackage]. `TorchSurv` offers a comparable range of evaluation metrics, ensuring comprehensive model evaluation regardless of the chosen programming environment. +Our package, `TorchSurv`, is specifically designed for use in Python, but we also provide a comparative analysis of its functionalities with popular `R` packages for survival analysis in Table 2. `R` packages also restrict users to specific forms to define the parameters and do not make log-likelihood functions readily accessible. However, `R` has extensive libraries for evaluation metrics, such as the `RiskRegression` library [@riskRegressionpackage]. `TorchSurv` offers a comparable range of evaluation metrics, ensuring comprehensive model evaluation regardless of the chosen programming environment. + +The outputs of both the log-likelihood functions and the evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is presented in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). + +![**Survival analysis libraries in Python.** +$^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. +For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival `impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time, while it lacks the Efron approximation.\label{tab:bibliography}](table_1.png) + +![**Survival analysis libraries in R.** +$^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. +For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.](table_2.png) + -The outputs of both the log-likelihood functions and the evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is summarized in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). # Functionality ## Loss functions -**Cox loss function.** The Cox loss function is defined as the negative of the Cox proportional hazards model's partial log-likelihood [@Cox1972]. The function requires the subject-specific log relative hazards and the survival data (i.e., event indicator and time-to-event or censoring). The log relative hazards should be obtained from a `PyTorch`-based model pre-specified by the user. In case of ties in the event times, the user can choose between the Breslow [@Breslow1975] and the Efron method [@Efron1977] to approximate the Cox partial log likelihood. We illustrate the use of the Cox loss function for a pseudo training loop in the code snippet below. +**Cox loss function.** The Cox loss function is defined as the negative of the Cox proportional hazards model's partial log-likelihood [@Cox1972]. The function requires the subject-specific log relative hazards and the survival response (i.e., event indicator and time-to-event or censoring). The log relative hazards are obtained from a `PyTorch`-based model pre-specified by the user. In case of ties in the event times, the user can choose between the Breslow [@Breslow1975] and the Efron method [@Efron1977] to approximate the Cox partial log-likelihood. We illustrate the use of the Cox loss function for a pseudo training loop in the code snippet below. ```python from torchsurv.loss import cox my_model = MyPyTorchModel() # PyTorch model for log hazards (1 output) for data in dataloader: - x, event, time = data + x, event, time = data # covariate, event indicator, time log_hzs = my_model(x) # torch.Size([64, 1]), if batch size is 64 loss = cox.neg_partial_log_likelihood(log_hzs, event, time) loss.backward() # native torch backend ``` -**Weibull loss function.** The Weibull loss function is defined as the negative of the Weibull AFT's log-likelihood [@Carroll2003]. The function requires the subject-specific log parameters of the Weibull distribution (i.e., the log scale and the log shape) and the survival data. The log parameters of the Weibull distribution should be obtained from a `PyTorch`-based model pre-specified by the user. We illustrate the use of the Weibull loss function for a pseudo training loop in the code snippet below. +**Weibull loss function.** The Weibull loss function is defined as the negative of the Weibull AFT's log-likelihood [@Carroll2003]. The function requires the subject-specific log parameters of the Weibull distribution (i.e., the log scale and the log shape) and the survival response. The log parameters of the Weibull distribution should be obtained from a `PyTorch`-based model pre-specified by the user. We illustrate the use of the Weibull loss function for a pseudo training loop in the code snippet below. ```python from torchsurv.loss import weibull my_model = MyPyTorchModel() # PyTorch model for log parameters (2 outputs) for data in dataloader: - x, event, time = data + x, event, time = data # covariate, event indicator, time log_params = my_model(x) # torch.Size([64, 2]), if batch size is 64 loss = weibull.neg_log_likelihood(log_params, event, time) loss.backward() # native torch backend ``` **Momentum** - When training a model with a large file, the batch size is greatly limited by computational resources. This impacts the stability of model optimization, especially when rank-based loss is used. Inspired from MoCO [@he2020momentum], we implemented a momentum loss that decouples batch size from survival loss, increasing the effective batch size and allowing robust train of a model, even when using a very limited batch size (e.g., $batch_{size} \leq 16$). We illustrate the use of momentum for a pseudo training loop in the code + When training a model with a large file, the batch size is greatly limited by computational resources. This impacts the stability of model optimization, especially when rank-based loss is used. Inspired from MoCO [@he2020momentum], we implemented a momentum loss that decouples batch size from survival loss, increasing the effective batch size and allowing robust train of a model, even when using a very limited batch size (e.g., $\leq 16$). We illustrate the use of momentum for a pseudo training loop in the code snippet below. ```python @@ -110,12 +121,12 @@ my_loss = cox.neg_partial_log_likelihood # any torchsurv loss momentum = Momentum(backbone=my_model, loss=my_loss) for data in dataloader: - x, event, time = data - loss = model_momentum(x, event, time) # torch.Size([64, 1]) + x, event, time = data # covariate, event indicator, time + loss = model_momentum(x, event, time) # torch.Size([16, 1]) loss.backward() # native torch backend # Inference is computed with target network (k) -log_hzs = model_momentum.infer(x) # torch.Size([64, 1]) +log_hzs = model_momentum.infer(x) # torch.Size([16, 1]) ``` ## Evaluation Metrics Functions @@ -136,7 +147,7 @@ auc(log_hzs, event, time, new_time=torch.tensor(10.)) # AUC at time 10 ```python from torchsurv.metrics import ConcordanceIndex cindex = ConcordanceIndex() -cindex(log_hzs, event, time) # c-index +cindex(log_hzs, event, time) # C-index ``` **Brier Score.** The Brier score evaluates the accuracy of a model at a given time $t$. It represents the average squared distance between the observed survival status and the predicted survival probability [@Graf_1999]. The Brier score cannot be obtained for the Cox model because the survival function is not available, but it can be obtained for the Weibull model. @@ -146,12 +157,12 @@ from torchsurv.metrics import Brier surv = survival_function(log_params, time) brier = Brier() brier(surv, event, time) # Brier score at each time -brier.integral() # integrated brier score +brier.integral() # Integrated Brier score over time ``` -**Additional features.** In `TorchSurv`, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). -Lastly, functionalities including the confidence interval, one-sample hypothesis test to determine whether the metric is better than that of a random predictor, and two-sample hypothesis test to compare two evaluation metrics between different models are implemented. For the hypothesis tests, the significance level, typically referred to as $\alpha$, can be modified as needed. The following code snippet exemplifies the aforementioned functionalities for the C-index. +**Additional features.** In `TorchSurv`, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally, subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). +Lastly, functionalities including the confidence interval, one-sample hypothesis test to determine whether the metric is better than that of a random predictor, and two-sample hypothesis test to compare two evaluation metrics between different models are implemented. The following code snippet exemplifies the aforementioned functionalities for the C-index. ```python cindex.confidence_interval() # CI, default alpha = .05 diff --git a/paper/table_1.png b/paper/table_1.png new file mode 100644 index 0000000000000000000000000000000000000000..efac5e0f00535b21deadb4b4a2f90c53cacb3249 GIT binary patch literal 243356 zcmcG#Wmr^Q`v*!XB`qKg(yerNBOOB{k^|C13kVX@0s_(Yy3 zbOZzpC|FurT~S(^THVFb3jE#@0YM=wAqiDoYn!O2&1XznN*PU7>5qyxCN+H!k%Xks zXC(!sApCdKOw+MH35_(gWy*7)&!2$uf91T)nI@8bvPWj2p+5OD5G%nWfTsRtvxWcg zcbZ^ZTH4`5`r+W6)!>tlCBN{(M&HUJj0Wqks*7`aL*B4a@S~JVP^y0v{ozud{L)H6 z0axNZ!~Mb{9La`jvEtkKHT>ZL%6xp8Y=n@78${rlJP!#{qrDGg#kWTMDD-jtJjptY zp#j;f_yrhu#V0OLT%F z6(oz+R9I+zgew+Mj;{G>T_OHDGUyNzcVCc9J+|%?XCdO7$lU3J$t4R;OM5tVzI9Yq zi~qf679_x|zeg1=W4WHZpge%~wc?eTGM+kP3AMdrsjic#{B@ZQTZ!v_*4>;9QyR<*E zE(m!tI9YDtRY9jcDQ@~_i?;~|+uPetUP=f3NGu&t!iFcMr*qGVoBm3h_#K&e-o55? z^LtX}*N&{dNo9)+n5NR(FC~*LGns6p#n7u@0$i@nk59NHNLSxUpi2JY 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z{vHrxtO9uu{^M2@fB<>TqB3CJvZap~PV`5i`QLsGWMc#uj%7BH0?e3q{rmP7+Y5ly zt7V*iga80DSJ+N)v9hrxTGmgj0Eo4*!F!F><&L>#B;#jgeZ3C||Dokn(nZicf`-$A zXxK^!2<8<42RtotaW{F?gIz%tyucqSPKI%CZxKh4Vg%?zR{~LetjjA$O z*?pMtV}Im-e{m1sbN;`)jb9c-j1YjFdo2A(^Y=3Rzj!i%F=z`wpcQCxbGV2O_(-oL~{?dD{r_ z{0Bpf9A!fWD2m`jiQu~8f3`5=tC7dxcEM`LUoV&6E7SQE2+$21meRTZdc=@J3LOWf zfb{Cr6e5QIc^3bxpY-s7RqV1I< Date: Fri, 19 Jul 2024 17:08:56 +0100 Subject: [PATCH 12/22] fix typo --- paper/paper.md | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index ed361c5..21e186c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -35,7 +35,7 @@ bibliography: paper.bib # Summary `TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive survival model parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex input data scenarios. -`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the[`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). +`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). `TorchSurv` provides a user-friendly workflow for defining a survival model with parameters specified by a `PyTorch`-based (deep) neural network. At the core of `TorchSurv` lies its `PyTorch`-based calculation of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. In survival analysis, each observation is associated with survival data denoted by $y$ (comprising the event indicator and the time-to-event or censoring) and covariates denoted by $x$. A survival model that is able to capture the complexity of the survival data $y$, is parametrized by parameters denoted by $\theta$. For instance, in the Cox proportional hazards model, the survival model parameters $\theta$ are the relative hazards. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the survival model parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. @@ -71,13 +71,9 @@ Our package, `TorchSurv`, is specifically designed for use in Python, but we als The outputs of both the log-likelihood functions and the evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is presented in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). -![**Survival analysis libraries in Python.** -$^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. -For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival `impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time, while it lacks the Efron approximation.\label{tab:bibliography}](table_1.png) +![**Survival analysis libraries in Python.** $^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival `impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time, while it lacks the Efron approximation.\label{tab:bibliography}](table_1.png) -![**Survival analysis libraries in R.** -$^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. -For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.](table_2.png) +![**Survival analysis libraries in R.** $^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.](table_2.png) From ec6b55dd99a8f948d101b6bb26a7286f77dcc801 Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Fri, 19 Jul 2024 17:15:32 +0100 Subject: [PATCH 13/22] fix typo --- paper/paper.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 21e186c..61195e3 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -57,7 +57,7 @@ Below is an overview of the workflow for model inference and evaluation with `To Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning presents promising opportunities for developing sophisticated survival models, where the parameters depend on covariates through complex functions. However, no existing library provides the flexibility to define survival model parameters using a custom `PyTorch`-based neural network. -Figure \autoref{tab:bibliography} compares the functionalities of `TorchSurv` with those of +\autoref{tab:bibliography} compares the functionalities of `TorchSurv` with those of `auton-survival` [@nagpal2022auton], `pycox` [@Kvamme2019pycox], `torchlife` [@torchlifeAbeywardana], @@ -67,13 +67,13 @@ Figure \autoref{tab:bibliography} compares the functionalities of `TorchSurv` wi Existing libraries constrain users to predefined forms for defining the parameters (e.g., linear function of covariates). While there exist log-likelihood functions in the libraries, they cannot be leveraged. Specifically, the limitations on the log-likelihood functions include protected functions, specialized input requirements (format or class type), and reliance on external libraries like `NumPy` or `Pandas`. Dependence on external libraries hinders automatic gradient calculation within `PyTorch`. Additionally, the implementation of likelihood functions instead of log-likelihood functions, as done by some packages, introduces numerical instability. With respect to the evaluation metrics, `scikit-survival` stands out as a comprehensive library. However, it lacks certain desirable features, including confidence intervals and comparison of the evaluation metric between two different models, and it is implemented with `NumPy`. -Our package, `TorchSurv`, is specifically designed for use in Python, but we also provide a comparative analysis of its functionalities with popular `R` packages for survival analysis in Table 2. `R` packages also restrict users to specific forms to define the parameters and do not make log-likelihood functions readily accessible. However, `R` has extensive libraries for evaluation metrics, such as the `RiskRegression` library [@riskRegressionpackage]. `TorchSurv` offers a comparable range of evaluation metrics, ensuring comprehensive model evaluation regardless of the chosen programming environment. +Our package, `TorchSurv`, is specifically designed for use in Python, but we also provide a comparative analysis of its functionalities with popular `R` packages for survival analysis in \autoref{tab:bibliography_R}. `R` packages also restrict users to specific forms to define the parameters and do not make log-likelihood functions readily accessible. However, `R` has extensive libraries for evaluation metrics, such as the `RiskRegression` library [@riskRegressionpackage]. `TorchSurv` offers a comparable range of evaluation metrics, ensuring comprehensive model evaluation regardless of the chosen programming environment. The outputs of both the log-likelihood functions and the evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is presented in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). -![**Survival analysis libraries in Python.** $^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival `impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time, while it lacks the Efron approximation.\label{tab:bibliography}](table_1.png) +![**Survival analysis libraries in Python.** $^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival `impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time, while it lacks the Efron approximation.\label{tab:bibliography}](table_1.png) -![**Survival analysis libraries in R.** $^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. ✔ indicates a fully supported feature, ✘ indicates an unsupported feature, ⍻ indicates a partially supported feature. For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.](table_2.png) +![**Survival analysis libraries in R.** $^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.\label{tab:bibliography_R}](table_2.png) From a449e7472e6e6d45450728ea5d41b1e4f6a244f9 Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Fri, 19 Jul 2024 17:35:34 +0100 Subject: [PATCH 14/22] improve tex --- paper/paper.md | 33 +++++++++++++++++---------------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 61195e3..381708b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -34,22 +34,23 @@ bibliography: paper.bib # Summary -`TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive survival model parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex input data scenarios. -`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). +`TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. +`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the[`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). -`TorchSurv` provides a user-friendly workflow for defining a survival model with parameters specified by a `PyTorch`-based (deep) neural network. At the core of `TorchSurv` lies its `PyTorch`-based calculation of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. -In survival analysis, each observation is associated with survival data denoted by $y$ (comprising the event indicator and the time-to-event or censoring) and covariates denoted by $x$. A survival model that is able to capture the complexity of the survival data $y$, is parametrized by parameters denoted by $\theta$. For instance, in the Cox proportional hazards model, the survival model parameters $\theta$ are the relative hazards. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the survival model parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. -Additionally, `TorchSurv` offers evaluation metrics (the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic curve (ROC), the Concordance index (C-index) and the Brier Score) to characterize the predictive performance of survival models. +`TorchSurv` provides a user-friendly workflow for training and evaluating `PyTorch`-based deep survival models. +At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. +In survival analysis, each observation is associated with survival reponse, denoted by $y$ (comprising the event indicator and the time-to-event or censoring), and covariates denoted by $x$. A survival model is parametrized by parameters, denoted by $\theta$. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. +Additionally, `TorchSurv` offers evaluation metrics, including the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic (ROC) curve, the Concordance index (C-index) and the Brier Score, to characterize the predictive performance of survival models. Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: 1. Initialize a `PyTorch`-based neural network that defines the function from the covariates $x$ to the parameters $\theta$. In the context of the Cox proportional hazards model for example, the parameters are the log relative hazards. 2. Initiate training: For each epoch on the training set, - - Draw survival data $y^{\text{train}}$ (i.e., event indicator and time-to-event or censoring) and covariates $x^{\text{train}}$ from the training set. - - Obtain parameters $\theta^{\text{train}}$ based on drawn covariates $x^{\text{train}}$ using `PyTorch`-based neural network. - - Calculate the loss given survival data $y^{\text{train}}$ and parameters $\theta^{\text{train}}$ using `TorchSurv`'s loss function. In the context of the Cox relative hazards model for example, the loss function is equal to the negative of the log partial likelihood. - - Utilize backpropagation to update parameters $\theta^{\text{train}}$. -3. Obtain parameters $\theta^{\text{test}}$ based on covariates from the test set $x^{\text{test}}$ using the trained `PyTorch`-based neural network. -4. Evaluate the predictive performance of the model using `TorchSurv`'s evaluation metric functions (e.g., C-index) given parameters $\theta^{\text{test}}$ and survival data from the test set $y^{\text{test}}$. + - Draw survival response $y^{\text{train}}$ and covariates $x^{\text{train}}$ from the training set. + - Obtain parameters $\theta^{\text{train}}$ given covariates $x^{\text{train}}$ using the neural network. + - Calculate the loss as the negative log-likelihood of survival response $y^{\text{train}}$ given parameters $\theta^{\text{train}}$. This calculation is facilitated by `TorchSurv`'s loss function. + - Utilize backpropagation to update the neural network's parameters. +3. Obtain parameters $\theta^{\text{test}}$ given covariates from the test set $x^{\text{test}}$ using the trained neural network. +4. Evaluate the predictive performance of the model using `TorchSurv`'s evaluation metric functions given parameters $\theta^{\text{test}}$ and survival response from the test set $y^{\text{test}}$. @@ -69,7 +70,7 @@ Specifically, the limitations on the log-likelihood functions include protected With respect to the evaluation metrics, `scikit-survival` stands out as a comprehensive library. However, it lacks certain desirable features, including confidence intervals and comparison of the evaluation metric between two different models, and it is implemented with `NumPy`. Our package, `TorchSurv`, is specifically designed for use in Python, but we also provide a comparative analysis of its functionalities with popular `R` packages for survival analysis in \autoref{tab:bibliography_R}. `R` packages also restrict users to specific forms to define the parameters and do not make log-likelihood functions readily accessible. However, `R` has extensive libraries for evaluation metrics, such as the `RiskRegression` library [@riskRegressionpackage]. `TorchSurv` offers a comparable range of evaluation metrics, ensuring comprehensive model evaluation regardless of the chosen programming environment. -The outputs of both the log-likelihood functions and the evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is presented in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). +`TorchSurv`'s log-likelihood and evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is presented in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). ![**Survival analysis libraries in Python.** $^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival `impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time, while it lacks the Efron approximation.\label{tab:bibliography}](table_1.png) @@ -81,7 +82,7 @@ The outputs of both the log-likelihood functions and the evaluation metrics func ## Loss functions -**Cox loss function.** The Cox loss function is defined as the negative of the Cox proportional hazards model's partial log-likelihood [@Cox1972]. The function requires the subject-specific log relative hazards and the survival response (i.e., event indicator and time-to-event or censoring). The log relative hazards are obtained from a `PyTorch`-based model pre-specified by the user. In case of ties in the event times, the user can choose between the Breslow [@Breslow1975] and the Efron method [@Efron1977] to approximate the Cox partial log-likelihood. We illustrate the use of the Cox loss function for a pseudo training loop in the code snippet below. +**Cox loss function.** The Cox loss function is defined as the negative of the Cox proportional hazards model's partial log-likelihood [@Cox1972]. The function requires the subject-specific log relative hazards and the survival response (i.e., event indicator and time-to-event or censoring). The log relative hazards are obtained from a `PyTorch`-based model pre-specified by the user. In case of ties in the event times, the user can choose between the Breslow method [@Breslow1975] and the Efron method [@Efron1977] to approximate the Cox partial log-likelihood. We illustrate the use of the Cox loss function for a pseudo training loop in the code snippet below. ```python from torchsurv.loss import cox @@ -126,7 +127,7 @@ log_hzs = model_momentum.infer(x) # torch.Size([16, 1]) ## Evaluation Metrics Functions -The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the individual risk score estimated on the test set and the survival data on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. +The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the individual risk score estimated on the test set and the survival response on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. **AUC.** The AUC measures the discriminatory capacity of a model at a given time $t$, i.e., the model’s ability to provide a reliable ranking of times-to-event based on estimated individual risk scores [@Heagerty2005;@Uno2007;@Blanche2013]. @@ -137,7 +138,7 @@ auc(log_hzs, event, time) # AUC at each time auc(log_hzs, event, time, new_time=torch.tensor(10.)) # AUC at time 10 ``` -**C-index.** The C-index is a generalization of the AUC that represents the assessment of the discriminatory capacity of the model over time [@Harrell1996;@Uno_2011]. +**C-index.** The C-index is a generalization of the AUC that represents the assessment of the discriminatory capacity of the model across the time period [@Harrell1996;@Uno_2011]. ```python from torchsurv.metrics import ConcordanceIndex @@ -145,7 +146,7 @@ cindex = ConcordanceIndex() cindex(log_hzs, event, time) # C-index ``` -**Brier Score.** The Brier score evaluates the accuracy of a model at a given time $t$. It represents the average squared distance between the observed survival status and the predicted survival probability [@Graf_1999]. The Brier score cannot be obtained for the Cox model because the survival function is not available, but it can be obtained for the Weibull model. +**Brier Score.** The Brier score evaluates the accuracy of a model at a given time $t$. It represents the average squared distance between the observed survival status and the predicted survival probability [@Graf_1999]. The Brier score cannot be obtained for the Cox proportional hazards model because the survival function is not available, but it can be obtained for the Weibull ATF model. ```python from torchsurv.metrics import Brier From aabed1c1bf335ef07aceb6348f43598b8c2dd620 Mon Sep 17 00:00:00 2001 From: corolth1 Date: Wed, 24 Jul 2024 10:00:53 -0400 Subject: [PATCH 15/22] edits --- paper/paper.md | 37 +++++++++++++++++++------------------ 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 381708b..62954dd 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -38,7 +38,7 @@ bibliography: paper.bib `TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the[`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). `TorchSurv` provides a user-friendly workflow for training and evaluating `PyTorch`-based deep survival models. -At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. +At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. In survival analysis, each observation is associated with survival reponse, denoted by $y$ (comprising the event indicator and the time-to-event or censoring), and covariates denoted by $x$. A survival model is parametrized by parameters, denoted by $\theta$. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. Additionally, `TorchSurv` offers evaluation metrics, including the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic (ROC) curve, the Concordance index (C-index) and the Brier Score, to characterize the predictive performance of survival models. Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: @@ -56,15 +56,15 @@ Below is an overview of the workflow for model inference and evaluation with `To # Statement of need -Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning presents promising opportunities for developing sophisticated survival models, where the parameters depend on covariates through complex functions. However, no existing library provides the flexibility to define survival model parameters using a custom `PyTorch`-based neural network. +Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning presents promising opportunities for developing sophisticated survival models, where the parameters depend on covariates through complex functions. However, no existing library provides the flexibility to define survival model parameters using a custom `PyTorch`-based neural network. -\autoref{tab:bibliography} compares the functionalities of `TorchSurv` with those of -`auton-survival` [@nagpal2022auton], -`pycox` [@Kvamme2019pycox], +\autoref{tab:bibliography} compares the functionalities of `TorchSurv` with those of +`auton-survival` [@nagpal2022auton], +`pycox` [@Kvamme2019pycox], `torchlife` [@torchlifeAbeywardana], `scikit-survival` [@polsterl2020scikit], -`lifelines` [@davidson2019lifelines], and -`deepsurv` [@katzman2018deepsurv]. +`lifelines` [@davidson2019lifelines], and +`deepsurv` [@katzman2018deepsurv]. Existing libraries constrain users to predefined forms for defining the parameters (e.g., linear function of covariates). While there exist log-likelihood functions in the libraries, they cannot be leveraged. Specifically, the limitations on the log-likelihood functions include protected functions, specialized input requirements (format or class type), and reliance on external libraries like `NumPy` or `Pandas`. Dependence on external libraries hinders automatic gradient calculation within `PyTorch`. Additionally, the implementation of likelihood functions instead of log-likelihood functions, as done by some packages, introduces numerical instability. With respect to the evaluation metrics, `scikit-survival` stands out as a comprehensive library. However, it lacks certain desirable features, including confidence intervals and comparison of the evaluation metric between two different models, and it is implemented with `NumPy`. @@ -77,7 +77,6 @@ Our package, `TorchSurv`, is specifically designed for use in Python, but we als ![**Survival analysis libraries in R.** $^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.\label{tab:bibliography_R}](table_2.png) - # Functionality ## Loss functions @@ -86,11 +85,12 @@ Our package, `TorchSurv`, is specifically designed for use in Python, but we als ```python from torchsurv.loss import cox -my_model = MyPyTorchModel() # PyTorch model for log hazards (1 output) +my_model = MyPyTorchCoxModel() # PyTorch model outputs one (1) log hazards for Cox model + for data in dataloader: - x, event, time = data # covariate, event indicator, time + x, event, time = data # covariate, event indicator, time log_hzs = my_model(x) # torch.Size([64, 1]), if batch size is 64 - loss = cox.neg_partial_log_likelihood(log_hzs, event, time) + loss = cox.neg_partial_log_likelihood(log_hzs, event, time) loss.backward() # native torch backend ``` @@ -98,11 +98,12 @@ for data in dataloader: ```python from torchsurv.loss import weibull -my_model = MyPyTorchModel() # PyTorch model for log parameters (2 outputs) +my_model = MyPyTorcWeibullhModel() # PyTorch model outputs two (2) log parameters for Weibull model + for data in dataloader: - x, event, time = data # covariate, event indicator, time + x, event, time = data # covariate, event indicator, time log_params = my_model(x) # torch.Size([64, 2]), if batch size is 64 - loss = weibull.neg_log_likelihood(log_params, event, time) + loss = weibull.neg_log_likelihood(log_params, event, time) loss.backward() # native torch backend ``` @@ -112,9 +113,9 @@ snippet below. ```python from torchsurv.loss import Momentum -my_model = MyPyTorchModel() # PyTorch model for log hazards (1 output) -my_loss = cox.neg_partial_log_likelihood # any torchsurv loss -momentum = Momentum(backbone=my_model, loss=my_loss) +my_model = MyPyTorchXCoxModel() # PyTorch model outputs one (1) log hazards for Cox model +my_loss = cox.neg_partial_log_likelihood # Works with any TorchSurv loss +momentum = Momentum(backbone=my_model, loss=my_loss) for data in dataloader: x, event, time = data # covariate, event indicator, time @@ -153,7 +154,7 @@ from torchsurv.metrics import Brier surv = survival_function(log_params, time) brier = Brier() brier(surv, event, time) # Brier score at each time -brier.integral() # Integrated Brier score over time +brier.integral() # Integrated Brier score over time ``` **Additional features.** In `TorchSurv`, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally, subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). From 60b3f54ecad9467de7f2dbacfab4436a68aff739 Mon Sep 17 00:00:00 2001 From: corolth1 Date: Wed, 24 Jul 2024 10:03:22 -0400 Subject: [PATCH 16/22] added links to TorchSurv intro --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 62954dd..3c6fd15 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -34,7 +34,7 @@ bibliography: paper.bib # Summary -`TorchSurv` (available on GitHub and PyPI) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. +`TorchSurv` ([GitHub](https://github.com/Novartis/torchsurv) and [PyPI](https://pypi.org/project/torchsurv/)) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. `TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the[`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). `TorchSurv` provides a user-friendly workflow for training and evaluating `PyTorch`-based deep survival models. From 32963b4fbe07efcafb661199f3da72fc9303c923 Mon Sep 17 00:00:00 2001 From: melodiemonod Date: Wed, 24 Jul 2024 15:35:42 +0100 Subject: [PATCH 17/22] ready for submission --- paper/paper.md | 30 +++++++++++++++++------------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 3c6fd15..fc0c339 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -35,12 +35,12 @@ bibliography: paper.bib # Summary `TorchSurv` ([GitHub](https://github.com/Novartis/torchsurv) and [PyPI](https://pypi.org/project/torchsurv/)) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. -`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the[`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). +`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). `TorchSurv` provides a user-friendly workflow for training and evaluating `PyTorch`-based deep survival models. At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. -In survival analysis, each observation is associated with survival reponse, denoted by $y$ (comprising the event indicator and the time-to-event or censoring), and covariates denoted by $x$. A survival model is parametrized by parameters, denoted by $\theta$. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation facilitated by backpropagation. -Additionally, `TorchSurv` offers evaluation metrics, including the time-dependent Area Under the cure (AUC) under the Receiver operating characteristic (ROC) curve, the Concordance index (C-index) and the Brier Score, to characterize the predictive performance of survival models. +In survival analysis, each observation is associated with a survival reponse, denoted by $y$ (comprising the event indicator and the time-to-event or censoring), and covariates, denoted by $x$. A survival model is parametrized by parameters $\theta$. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation. +Additionally, `TorchSurv` offers evaluation metrics, including the time-dependent Area Under under the Receiver operating characteristic (ROC) curve (AUC), the Concordance index (C-index) and the Brier Score, to characterize the predictive performance of survival models. Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: 1. Initialize a `PyTorch`-based neural network that defines the function from the covariates $x$ to the parameters $\theta$. In the context of the Cox proportional hazards model for example, the parameters are the log relative hazards. @@ -56,7 +56,7 @@ Below is an overview of the workflow for model inference and evaluation with `To # Statement of need -Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning presents promising opportunities for developing sophisticated survival models, where the parameters depend on covariates through complex functions. However, no existing library provides the flexibility to define survival model parameters using a custom `PyTorch`-based neural network. +Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning presents promising opportunities for developing sophisticated survival models, where the parameters depend on covariates through complex functions. However, no existing library provides the flexibility to define the survival model's parameters using a custom `PyTorch`-based neural network. \autoref{tab:bibliography} compares the functionalities of `TorchSurv` with those of `auton-survival` [@nagpal2022auton], @@ -72,9 +72,9 @@ Our package, `TorchSurv`, is specifically designed for use in Python, but we als `TorchSurv`'s log-likelihood and evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is presented in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). -![**Survival analysis libraries in Python.** $^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival `impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time, while it lacks the Efron approximation.\label{tab:bibliography}](table_1.png) +![**Survival analysis libraries in Python.** $^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival` impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time.\label{tab:bibliography}](table_1.png) -![**Survival analysis libraries in R.** $^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.\label{tab:bibliography_R}](table_2.png) +![**Survival analysis libraries in R.** $^1$[@survivalpackage], $^{2}$[@survAUCpackage], $^{3}$[@timeROCpackage], $^{4}$[@risksetROCpackage], $^{5}$[@survcomppackage], $^{6}$[@survivalROCpackage], $^{7}$[@riskRegressionpackage], $^{8}$[@SurvMetricspackage], $^{9}$[@pecpackage]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For obtaining the evaluation metrics, packages `survival`, `riskRegression`, `SurvMetrics` and `pec` require the fitted model object as input (a specific object format) and `RisksetROC` imposes to use a smoothing method. Packages `timeROC`, `riskRegression` and `pec` force the user to choose a form for subject-specific weights (e.g., inverse probability of censoring weighting (IPCW)). Packages `survcomp` and `SurvivalROC` do not implement the general AUC but the censoring-adjusted AUC estimator proposed by @Heagerty2000.\label{tab:bibliography_R}](table_2.png) # Functionality @@ -85,7 +85,9 @@ Our package, `TorchSurv`, is specifically designed for use in Python, but we als ```python from torchsurv.loss import cox -my_model = MyPyTorchCoxModel() # PyTorch model outputs one (1) log hazards for Cox model + +# PyTorch model outputs one log hazard by observation +my_model = MyPyTorchCoxModel() for data in dataloader: x, event, time = data # covariate, event indicator, time @@ -98,7 +100,9 @@ for data in dataloader: ```python from torchsurv.loss import weibull -my_model = MyPyTorcWeibullhModel() # PyTorch model outputs two (2) log parameters for Weibull model + +# PyTorch model outputs two Weibull parameters by observation +my_model = MyPyTorcWeibullhModel() for data in dataloader: x, event, time = data # covariate, event indicator, time @@ -113,7 +117,7 @@ snippet below. ```python from torchsurv.loss import Momentum -my_model = MyPyTorchXCoxModel() # PyTorch model outputs one (1) log hazards for Cox model +my_model = MyPyTorchXCoxModel() my_loss = cox.neg_partial_log_likelihood # Works with any TorchSurv loss momentum = Momentum(backbone=my_model, loss=my_loss) @@ -128,9 +132,9 @@ log_hzs = model_momentum.infer(x) # torch.Size([16, 1]) ## Evaluation Metrics Functions -The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the individual risk score estimated on the test set and the survival response on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. +The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the subject-specific risk score estimated on the test set and the survival response on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. -**AUC.** The AUC measures the discriminatory capacity of a model at a given time $t$, i.e., the model’s ability to provide a reliable ranking of times-to-event based on estimated individual risk scores [@Heagerty2005;@Uno2007;@Blanche2013]. +**AUC.** The AUC measures the discriminatory capacity of the survival model at a given time $t$, i.e., the ability to provide a reliable ranking of times-to-event based on estimated subject-specific risk scores [@Heagerty2005;@Uno2007;@Blanche2013]. ```python from torchsurv.metrics import Auc @@ -139,7 +143,7 @@ auc(log_hzs, event, time) # AUC at each time auc(log_hzs, event, time, new_time=torch.tensor(10.)) # AUC at time 10 ``` -**C-index.** The C-index is a generalization of the AUC that represents the assessment of the discriminatory capacity of the model across the time period [@Harrell1996;@Uno_2011]. +**C-index.** The C-index is a generalization of the AUC that represents the assessment of the discriminatory capacity of the survival model across the entire time period [@Harrell1996;@Uno_2011]. ```python from torchsurv.metrics import ConcordanceIndex @@ -147,7 +151,7 @@ cindex = ConcordanceIndex() cindex(log_hzs, event, time) # C-index ``` -**Brier Score.** The Brier score evaluates the accuracy of a model at a given time $t$. It represents the average squared distance between the observed survival status and the predicted survival probability [@Graf_1999]. The Brier score cannot be obtained for the Cox proportional hazards model because the survival function is not available, but it can be obtained for the Weibull ATF model. +**Brier Score.** The Brier score evaluates the accuracy of a model at a given time $t$ [@Graf_1999]. It represents the average squared distance between the observed survival status and the predicted survival probability. The Brier score cannot be obtained for the Cox proportional hazards model because the survival function is not available, but it can be obtained for the Weibull ATF model. ```python from torchsurv.metrics import Brier From 4280ee83da75da2a4a35c3b3a85022932e642008 Mon Sep 17 00:00:00 2001 From: corolth1 Date: Mon, 29 Jul 2024 09:21:44 -0400 Subject: [PATCH 18/22] fixed DOIs --- paper/paper.bib | 30 ++++++++++++++++++++++++------ paper/paper.md | 6 ++---- 2 files changed, 26 insertions(+), 10 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 549469a..dbe2944 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -3,6 +3,7 @@ @article{davidson2019lifelines author={Davidson-Pilon, Cameron}, journal={Journal of Open Source Software}, volume={4}, + doi={https://doi.org/10.21105/joss.01317}, number={40}, pages={1317}, year={2019} @@ -13,17 +14,21 @@ @article{therneau2015survival author={Therneau, Terry M and Lumley, Thomas}, journal={R Top Doc}, volume={128}, + doi={10.32614/CRAN.package.survival}, number={10}, pages={28--33}, year={2015} } -@article{paszke2019pytorch, - title={Pytorch: An imperative style, high-performance deep learning library}, - author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others}, - journal={Advances in neural information processing systems}, - volume={32}, - year={2019} +@misc{paszke2019pytorch, + title={PyTorch: An Imperative Style, High-Performance Deep Learning Library}, + author={Adam Paszke and Sam Gross and Francisco Massa and Adam Lerer and James Bradbury and Gregory Chanan and Trevor Killeen and Zeming Lin and Natalia Gimelshein and Luca Antiga and Alban Desmaison and Andreas Köpf and Edward Yang and Zach DeVito and Martin Raison and Alykhan Tejani and Sasank Chilamkurthy and Benoit Steiner and Lu Fang and Junjie Bai and Soumith Chintala}, + year={2019}, + eprint={1912.01703}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + doi={https://doi.org/10.48550/arXiv.1912.01703}, + url={https://arxiv.org/abs/1912.01703}, } @article{polsterl2020scikit, @@ -42,6 +47,7 @@ @article{potapov2023packagesurvAUC author={Potapov, Sergej and Adler, Werner and Schmid, Matthias and Potapov, Maintainer Sergej}, journal={Statistics in Medicine}, volume={25}, + doi={10.32614/CRAN.package.survAUC}, pages={3474--3486}, year={2023} } @@ -50,6 +56,7 @@ @inproceedings{he2020momentum title={Momentum contrast for unsupervised visual representation learning}, author={He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, + doi={10.1109/cvpr42600.2020.00975}, pages={9729--9738}, year={2020} } @@ -60,6 +67,7 @@ @article{katzman2018deepsurv journal={BMC medical research methodology}, volume={18}, number={1}, + doi={10.1186/s12874-018-0482-1}, pages={1--12}, year={2018}, publisher={BioMed Central} @@ -82,6 +90,7 @@ @inproceedings{nagpal2022auton booktitle={Machine Learning for Healthcare Conference}, pages={585--608}, year={2022}, + doi={https://doi.org/10.48550/arXiv.2204.07276}, organization={PMLR} } @@ -94,6 +103,7 @@ @article{Cox1972 publisher = {Wiley}, author = {Cox, D. R.}, year = {1972}, + doi={10.1007/978-1-4612-4380-9_37}, month = jan, pages = {187–202} } @@ -139,6 +149,7 @@ @Manual{torchlifeAbeywardana author = {Sachinthaka Abeywardana}, year = {2021}, url = {https://sachinruk.github.io/torchlife//index.html}, + doi={10.32614/CRAN.package.survival}, } @Manual{survAUCpackage, @@ -146,6 +157,7 @@ @Manual{survAUCpackage author = {Sergej Potapov and Werner Adler and Matthias Schmid}, year = {2023}, note = {R package version 1.2-0}, + doi={10.32614/CRAN.package.survAUC}, url = {https://CRAN.R-project.org/package=survAUC}, } @@ -155,6 +167,7 @@ @Manual{timeROCpackage year = {2019}, note = {R package version 0.4}, url = {https://CRAN.R-project.org/package=timeROC}, + doi={10.32614/CRAN.package.timeROC} } @Manual{risksetROCpackage, @@ -163,6 +176,7 @@ @Manual{risksetROCpackage year = {2022}, note = {R package version 1.0.4.1}, url = {https://CRAN.R-project.org/package=risksetROC}, + doi={10.32614/CRAN.package.risksetROC} } @Manual{survivalROCpackage, @@ -171,6 +185,7 @@ @Manual{survivalROCpackage year = {2022}, note = {R package version 1.0.3.1}, url = {https://CRAN.R-project.org/package=survivalROC}, + doi={10.32614/CRAN.package.survivalROC} } @article{survcomppackage, @@ -194,6 +209,7 @@ @Manual{riskRegressionpackage year = {2023}, note = {R package version 2023.12.21}, url = {https://CRAN.R-project.org/package=riskRegression}, + doi={10.32614/CRAN.package.riskRegression} } @Manual{SurvMetricspackage, @@ -202,6 +218,7 @@ @Manual{SurvMetricspackage year = {2022}, note = {R package version 0.5.0}, url = {https://CRAN.R-project.org/package=SurvMetrics}, + doi={10.32614/CRAN.package.SurvMetrics} } @Manual{pecpackage, @@ -210,6 +227,7 @@ @Manual{pecpackage year = {2023}, note = {R package version 2023.04.12}, url = {https://CRAN.R-project.org/package=pec}, + doi={10.32614/CRAN.package.pec} } @article{Heagerty2000, diff --git a/paper/paper.md b/paper/paper.md index fc0c339..3b291d8 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -34,11 +34,9 @@ bibliography: paper.bib # Summary -`TorchSurv` ([GitHub](https://github.com/Novartis/torchsurv) and [PyPI](https://pypi.org/project/torchsurv/)) is a Python package that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. -`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/). +`TorchSurv` is a Python [package](https://pypi.org/project/torchsurv/) that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. +`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on our [website](https://opensource.nibr.com/torchsurv/). -`TorchSurv` provides a user-friendly workflow for training and evaluating `PyTorch`-based deep survival models. -At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. In survival analysis, each observation is associated with a survival reponse, denoted by $y$ (comprising the event indicator and the time-to-event or censoring), and covariates, denoted by $x$. A survival model is parametrized by parameters $\theta$. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation. Additionally, `TorchSurv` offers evaluation metrics, including the time-dependent Area Under under the Receiver operating characteristic (ROC) curve (AUC), the Concordance index (C-index) and the Brier Score, to characterize the predictive performance of survival models. Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: From 842828e30675f2677b165b8cdcee442b50681fb8 Mon Sep 17 00:00:00 2001 From: corolth1 Date: Mon, 29 Jul 2024 10:27:44 -0400 Subject: [PATCH 19/22] new DOIs, trimmed text --- paper/paper.bib | 10 +++++--- paper/paper.md | 67 ++++++++++++++++--------------------------------- 2 files changed, 27 insertions(+), 50 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index dbe2944..ca86c9d 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -3,7 +3,7 @@ @article{davidson2019lifelines author={Davidson-Pilon, Cameron}, journal={Journal of Open Source Software}, volume={4}, - doi={https://doi.org/10.21105/joss.01317}, + doi={10.21105/joss.01317}, number={40}, pages={1317}, year={2019} @@ -27,7 +27,7 @@ @misc{paszke2019pytorch eprint={1912.01703}, archivePrefix={arXiv}, primaryClass={cs.LG}, - doi={https://doi.org/10.48550/arXiv.1912.01703}, + doi={10.48550/arXiv.1912.01703}, url={https://arxiv.org/abs/1912.01703}, } @@ -39,7 +39,8 @@ @article{polsterl2020scikit number={1}, pages={8747--8752}, year={2020}, - publisher={JMLRORG} + publisher={JMLRORG}, + doi={10.5281/zenodo.3352342} } @article{potapov2023packagesurvAUC, @@ -90,7 +91,7 @@ @inproceedings{nagpal2022auton booktitle={Machine Learning for Healthcare Conference}, pages={585--608}, year={2022}, - doi={https://doi.org/10.48550/arXiv.2204.07276}, + doi={10.48550/arXiv.2204.07276}, organization={PMLR} } @@ -142,6 +143,7 @@ @Manual{survivalpackage year = {2024}, note = {R package version 3.5-8}, url = {https://CRAN.R-project.org/package=survival}, + doi={10.32614/CRAN.package.survival}, } @Manual{torchlifeAbeywardana, diff --git a/paper/paper.md b/paper/paper.md index 3b291d8..d5d6b67 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -28,33 +28,18 @@ authors: index: 2 - name: Novartis Pharmaceuticals Corporation, NJ, USA index: 3 -date: 18 July 2024 +date: 29 July 2024 bibliography: paper.bib --- # Summary -`TorchSurv` is a Python [package](https://pypi.org/project/torchsurv/) that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. At its core, `TorchSurv` features `PyTorch`-based calculations of log-likelihoods for prominent survival models, including the Cox proportional hazards model [@Cox1972] and the Weibull Accelerated Time Failure (AFT) model [@Carroll2003]. -`TorchSurv` has been rigorously tested using both open-source and synthetically generated survival data. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on our [website](https://opensource.nibr.com/torchsurv/). - -In survival analysis, each observation is associated with a survival reponse, denoted by $y$ (comprising the event indicator and the time-to-event or censoring), and covariates, denoted by $x$. A survival model is parametrized by parameters $\theta$. Within the `TorchSurv` framework, a `PyTorch`-based neural network is defined to act as a flexible function that takes the covariates $x$ as input and outputs the parameters $\theta$. Estimation of the parameters $\theta$ is achieved via maximum likelihood estimation. -Additionally, `TorchSurv` offers evaluation metrics, including the time-dependent Area Under under the Receiver operating characteristic (ROC) curve (AUC), the Concordance index (C-index) and the Brier Score, to characterize the predictive performance of survival models. -Below is an overview of the workflow for model inference and evaluation with `TorchSurv`: - -1. Initialize a `PyTorch`-based neural network that defines the function from the covariates $x$ to the parameters $\theta$. In the context of the Cox proportional hazards model for example, the parameters are the log relative hazards. -2. Initiate training: For each epoch on the training set, - - Draw survival response $y^{\text{train}}$ and covariates $x^{\text{train}}$ from the training set. - - Obtain parameters $\theta^{\text{train}}$ given covariates $x^{\text{train}}$ using the neural network. - - Calculate the loss as the negative log-likelihood of survival response $y^{\text{train}}$ given parameters $\theta^{\text{train}}$. This calculation is facilitated by `TorchSurv`'s loss function. - - Utilize backpropagation to update the neural network's parameters. -3. Obtain parameters $\theta^{\text{test}}$ given covariates from the test set $x^{\text{test}}$ using the trained neural network. -4. Evaluate the predictive performance of the model using `TorchSurv`'s evaluation metric functions given parameters $\theta^{\text{test}}$ and survival response from the test set $y^{\text{test}}$. - - +`TorchSurv` is a Python [package](https://pypi.org/project/torchsurv/) that serves as a companion tool to perform deep survival modeling within the `PyTorch` environment [@paszke2019pytorch]. With its lightweight design, minimal input requirements, full `PyTorch` backend, and freedom from restrictive parameterizations, `TorchSurv` facilitates efficient deep survival model implementation and is particularly beneficial for high-dimensional and complex data scenarios. At its core, `TorchSurv` features calculations of log-likelihoods for prominent survival models (Cox proportional hazards model [@Cox1972], Weibull Accelerated Time Failure (AFT) model [@Carroll2003]) and offers evaluation metrics, including the time-dependent Area Under under the Receiver operating characteristic (ROC) curve (AUC), the Concordance index (C-index) and the Brier Score. +`TorchSurv` has been rigorously [tested](https://opensource.nibr.com/torchsurv/benchmarks.html) using both open-source and synthetically generated survival data, against R and python packages. The package is thoroughly documented and includes illustrative examples. The latest documentation for TorchSurv can be found on our [website](https://opensource.nibr.com/torchsurv/). # Statement of need -Survival analysis plays a crucial role in various domains, such as medicine and engineering. Deep learning presents promising opportunities for developing sophisticated survival models, where the parameters depend on covariates through complex functions. However, no existing library provides the flexibility to define the survival model's parameters using a custom `PyTorch`-based neural network. +Survival analysis plays a crucial role in various domains, such as medicine, economics or engineering. Thus, sophisticated survival models sugin deep learning opens new opportunities to leverage complex dataset and relationships. However, no existing library provides the flexibility to define the survival model's parameters using a custom `PyTorch`-based neural network. \autoref{tab:bibliography} compares the functionalities of `TorchSurv` with those of `auton-survival` [@nagpal2022auton], @@ -64,11 +49,7 @@ Survival analysis plays a crucial role in various domains, such as medicine and `lifelines` [@davidson2019lifelines], and `deepsurv` [@katzman2018deepsurv]. Existing libraries constrain users to predefined forms for defining the parameters (e.g., linear function of covariates). While there exist log-likelihood functions in the libraries, they cannot be leveraged. -Specifically, the limitations on the log-likelihood functions include protected functions, specialized input requirements (format or class type), and reliance on external libraries like `NumPy` or `Pandas`. Dependence on external libraries hinders automatic gradient calculation within `PyTorch`. Additionally, the implementation of likelihood functions instead of log-likelihood functions, as done by some packages, introduces numerical instability. -With respect to the evaluation metrics, `scikit-survival` stands out as a comprehensive library. However, it lacks certain desirable features, including confidence intervals and comparison of the evaluation metric between two different models, and it is implemented with `NumPy`. -Our package, `TorchSurv`, is specifically designed for use in Python, but we also provide a comparative analysis of its functionalities with popular `R` packages for survival analysis in \autoref{tab:bibliography_R}. `R` packages also restrict users to specific forms to define the parameters and do not make log-likelihood functions readily accessible. However, `R` has extensive libraries for evaluation metrics, such as the `RiskRegression` library [@riskRegressionpackage]. `TorchSurv` offers a comparable range of evaluation metrics, ensuring comprehensive model evaluation regardless of the chosen programming environment. - -`TorchSurv`'s log-likelihood and evaluation metrics functions have undergone thorough comparison with benchmarks generated with Python packages and R packages on open-source data and synthetic data. High agreement between the outputs is consistently observed, providing users with confidence in the accuracy and reliability of `TorchSurv`'s functionalities. The comparison is presented in the [`TorchSurv`'s website](https://opensource.nibr.com/torchsurv/benchmarks.html). +The limitations on the log-likelihood functions include protected functions (locked model architecture), specialized input requirements (format or class type), and reliance on external libraries like `NumPy` or `Pandas`. Dependence on external libraries hinders automatic gradient calculation within `PyTorch`. Additionally, the implementation of likelihood functions instead of log-likelihood functions, as done by some packages, introduces numerical instability. ![**Survival analysis libraries in Python.** $^1$[@nagpal2022auton], $^{2}$[@Kvamme2019pycox], $^{3}$[@torchlifeAbeywardana], $^{4}$[@polsterl2020scikit], $^{5}$[@davidson2019lifelines], $^{6}$[@katzman2018deepsurv]. A green tick indicates a fully supported feature, a red cross indicates an unsupported feature, a blue crossed tick indicates a partially supported feature. For computing the concordance index, `pycox` requires the use of the estimated survival function as the risk score and does not support other types of time-dependent risk scores. `scikit-survival` does not support time-dependent risk scores in both the concordance index and AUC computation. Additionally, both `pycox` and `scikit-survival` impose the use of inverse probability of censoring weighting (IPCW) for subject-specific weights. `scikit-survival` only offers the Breslow approximation of the Cox partial log-likelihood in case of ties in the event time.\label{tab:bibliography}](table_1.png) @@ -79,13 +60,12 @@ Our package, `TorchSurv`, is specifically designed for use in Python, but we als ## Loss functions -**Cox loss function.** The Cox loss function is defined as the negative of the Cox proportional hazards model's partial log-likelihood [@Cox1972]. The function requires the subject-specific log relative hazards and the survival response (i.e., event indicator and time-to-event or censoring). The log relative hazards are obtained from a `PyTorch`-based model pre-specified by the user. In case of ties in the event times, the user can choose between the Breslow method [@Breslow1975] and the Efron method [@Efron1977] to approximate the Cox partial log-likelihood. We illustrate the use of the Cox loss function for a pseudo training loop in the code snippet below. - +**Cox loss function** is defined as the negative of the Cox proportional hazards model's partial log-likelihood [@Cox1972]. The function requires the subject-specific log relative hazards and the survival response (i.e., event indicator and time-to-event or censoring). In case of ties in the event times, the user can choose between the Breslow method [@Breslow1975] and the Efron method [@Efron1977] to approximate the Cox partial log-likelihood. ```python from torchsurv.loss import cox -# PyTorch model outputs one log hazard by observation -my_model = MyPyTorchCoxModel() +# PyTorch model outputs ONE log hazard per observation +my_model = MyPyTorchCoxModel() for data in dataloader: x, event, time = data # covariate, event indicator, time @@ -94,24 +74,22 @@ for data in dataloader: loss.backward() # native torch backend ``` -**Weibull loss function.** The Weibull loss function is defined as the negative of the Weibull AFT's log-likelihood [@Carroll2003]. The function requires the subject-specific log parameters of the Weibull distribution (i.e., the log scale and the log shape) and the survival response. The log parameters of the Weibull distribution should be obtained from a `PyTorch`-based model pre-specified by the user. We illustrate the use of the Weibull loss function for a pseudo training loop in the code snippet below. +**Weibull loss function** is defined as the negative of the Weibull AFT's log-likelihood [@Carroll2003]. The function requires the subject-specific log parameters of the Weibull distribution (i.e., the log scale and the log shape) and the survival response. The log parameters of the Weibull distribution should be obtained from a `PyTorch`-based model pre-specified by the user. ```python from torchsurv.loss import weibull -# PyTorch model outputs two Weibull parameters by observation +# PyTorch model outputs TWO Weibull parameters per observation my_model = MyPyTorcWeibullhModel() for data in dataloader: - x, event, time = data # covariate, event indicator, time + x, event, time = data log_params = my_model(x) # torch.Size([64, 2]), if batch size is 64 loss = weibull.neg_log_likelihood(log_params, event, time) - loss.backward() # native torch backend + loss.backward() ``` -**Momentum** - When training a model with a large file, the batch size is greatly limited by computational resources. This impacts the stability of model optimization, especially when rank-based loss is used. Inspired from MoCO [@he2020momentum], we implemented a momentum loss that decouples batch size from survival loss, increasing the effective batch size and allowing robust train of a model, even when using a very limited batch size (e.g., $\leq 16$). We illustrate the use of momentum for a pseudo training loop in the code -snippet below. +**Momentum** helps training model when the batch size is greatly limited by computational resources (i.e. large files). This impacts the stability of model optimization, especially when rank-based loss is used. Inspired from MoCO [@he2020momentum], we implemented a momentum loss that decouples batch size from survival loss, increasing the effective batch size and allowing robust train of a model, even when using a very limited batch size (e.g., $\leq 16$). ```python from torchsurv.loss import Momentum @@ -120,9 +98,9 @@ my_loss = cox.neg_partial_log_likelihood # Works with any TorchSurv loss momentum = Momentum(backbone=my_model, loss=my_loss) for data in dataloader: - x, event, time = data # covariate, event indicator, time + x, event, time = data loss = model_momentum(x, event, time) # torch.Size([16, 1]) - loss.backward() # native torch backend + loss.backward() # Inference is computed with target network (k) log_hzs = model_momentum.infer(x) # torch.Size([16, 1]) @@ -130,7 +108,7 @@ log_hzs = model_momentum.infer(x) # torch.Size([16, 1]) ## Evaluation Metrics Functions -The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the AUC, C-index, and Brier score. The inputs of the evaluation metrics functions are the subject-specific risk score estimated on the test set and the survival response on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. +The `TorchSurv` package offers a comprehensive set of metrics to evaluate the predictive performance of survival models, including the `AUC`, `C-index`, and `Brier score`. The inputs of the evaluation metrics functions are the subject-specific risk score estimated on the test set and the survival response on the test set. The risk score measures the risk (or a proxy thereof) that a subject has an event. We provide definitions for each metric and demonstrate their use through illustrative code snippets. **AUC.** The AUC measures the discriminatory capacity of the survival model at a given time $t$, i.e., the ability to provide a reliable ranking of times-to-event based on estimated subject-specific risk scores [@Heagerty2005;@Uno2007;@Blanche2013]. @@ -146,7 +124,7 @@ auc(log_hzs, event, time, new_time=torch.tensor(10.)) # AUC at time 10 ```python from torchsurv.metrics import ConcordanceIndex cindex = ConcordanceIndex() -cindex(log_hzs, event, time) # C-index +cindex(log_hzs, event, time) ``` **Brier Score.** The Brier score evaluates the accuracy of a model at a given time $t$ [@Graf_1999]. It represents the average squared distance between the observed survival status and the predicted survival probability. The Brier score cannot be obtained for the Cox proportional hazards model because the survival function is not available, but it can be obtained for the Weibull ATF model. @@ -159,19 +137,16 @@ brier(surv, event, time) # Brier score at each time brier.integral() # Integrated Brier score over time ``` -**Additional features.** In `TorchSurv`, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally, subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). -Lastly, functionalities including the confidence interval, one-sample hypothesis test to determine whether the metric is better than that of a random predictor, and two-sample hypothesis test to compare two evaluation metrics between different models are implemented. The following code snippet exemplifies the aforementioned functionalities for the C-index. +**Additional features.** In `TorchSurv`, the evaluation metrics can be obtained for time-dependent and time-independent risk scores (e.g., for proportional and non-proportional hazards). Additionally, subjects can be optionally weighted (e.g., by the inverse probability of censoring weighting (IPCW)). Lastly, functionalities including the confidence interval, one-sample hypothesis test to determine whether the metric is better than that of a random predictor, and two-sample hypothesis test to compare two evaluation metrics between different models are implemented. The following code snippet exemplifies the aforementioned functionalities for the C-index. ```python -cindex.confidence_interval() # CI, default alpha = .05 -cindex.p_value(alternative='greater') # pvalue, H0: c = 0.5, HA: c > 0.5 -cindex.compare(cindex_other) # pvalue, H0: c1 = c2, HA: c1 > c2 +cindex.confidence_interval() # CI, default alpha=.05 +cindex.p_value(alternative='greater') # pvalue, H0:c=0.5, HA:c>0.5 +cindex.compare(cindex_other) # pvalue, H0:c1=c2, HA:c1>c2 ``` # Conflicts of interest MM, PK, DO and TC are employees and stockholders of Novartis, a global pharmaceutical company. - # References - From 29e35c87ae3d78f6b8263ec73b3af5406944ddeb Mon Sep 17 00:00:00 2001 From: corolth1 Date: Mon, 29 Jul 2024 13:48:43 -0400 Subject: [PATCH 20/22] JOSS tag --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 48ee8f3..e7d69ca 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ ![Docs](https://github.com/Novartis/torchsurv/actions/workflows/docs.yml/badge.svg?branch=main) [![PyPI - Version](https://img.shields.io/pypi/v/torchsurv)](https://pypi.org/project/torchsurv/) [![arXiv](https://img.shields.io/badge/arXiv-2404.10761-f9f107.svg)](https://arxiv.org/abs/2404.10761) +[![status](https://camo.githubusercontent.com/22fa65b2a659780cddfac609463c5fe719e3ea82a28eb7a61e24b7c4e40eb56d/68747470733a2f2f6a6f73732e7468656f6a2e6f72672f7061706572732f30326437343936646132623963633334663961366530346361626632323938642f7374617475732e737667)](https://joss.theoj.org/papers/02d7496da2b9cc34f9a6e04cabf2298d) [![Documentation](https://img.shields.io/badge/GithubPage-Sphinx-blue)](https://opensource.nibr.com/torchsurv/) [![Downloads](https://static.pepy.tech/badge/torchsurv)](https://pepy.tech/project/torchsurv) From 5cac910c96d7d43994b6f1e279094017fc5f17da Mon Sep 17 00:00:00 2001 From: corolth1 Date: Wed, 31 Jul 2024 09:41:15 -0400 Subject: [PATCH 21/22] fixing affiliation issue --- paper/paper.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index d5d6b67..62f1aed 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,27 +1,27 @@ --- title: 'TorchSurv: A Lightweight Package for Deep Survival Analysis' tags: - - Python package - - Deep Survival Analysis + - Python + - Deep Learning + - Survival Analysis - PyTorch - - TorchSurv authors: - name: Mélodie Monod orcid: 0000-0001-6448-2051 - affiliation: "1" + affiliation: 1 - name: Peter Krusche - affiliation: "1" + affiliation: 1 - name: Qian Cao - affiliation: "2" + affiliation: 2 - name: Berkman Sahiner - affiliation: "2" + affiliation: 2 - name: Nicholas Petrick - affiliation: "2" + affiliation: 2 - name: David Ohlssen - affiliation: "3" + affiliation: 3 - name: Thibaud Coroller orcid: 0000-0001-7662-8724 - affiliation: "3" + affiliation: 3 - name: Novartis Pharma AG, Switzerland index: 1 - name: Center for Devices and Radiological Health, Food and Drug Administration, MD, USA From ba6ffb5f68d4cc4d4b7445a0239139cb502974e5 Mon Sep 17 00:00:00 2001 From: corolth1 Date: Wed, 31 Jul 2024 09:44:16 -0400 Subject: [PATCH 22/22] fixing affiliation issue --- paper/paper.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/paper/paper.md b/paper/paper.md index 62f1aed..cc21d58 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -21,7 +21,9 @@ authors: affiliation: 3 - name: Thibaud Coroller orcid: 0000-0001-7662-8724 + corresponding: true affiliation: 3 +affiliations: - name: Novartis Pharma AG, Switzerland index: 1 - name: Center for Devices and Radiological Health, Food and Drug Administration, MD, USA