Chapter 4

chapter4.qmd

@@ -28,7 +28,7 @@ This decomposition leaves $\lambda_0(t)$ fully unspecified, focusing on the log-
 
 Estimation of $\beta$ proceeds through a partial-likelihood function that isolates each event’s contribution while conditioning on the subjects at risk. Suppose the observed sample has unique failure times $t_1 < \dots < t_m$, each with a single failing subject denoted by $(j)$. Let $\mathcal{R}_j$ be the risk set at $t_j$, meaning the indices of all subjects still under observation just before $t_j$. 
 
-![](images/cox_time_points.png){fig-align="center" width="85%"}
+![](images/cox_time_points.png){fig-align="center" width="80%"}
 
 Then the Cox partial likelihood is
 

docs/chapter4.html

@@ -320,7 +320,7 @@ <h3 class="anchored" data-anchor-id="the-partial-likelihood-approach">The partia
 <p>Estimation of <span class="math inline">\(\beta\)</span> proceeds through a partial-likelihood function that isolates each event’s contribution while conditioning on the subjects at risk. Suppose the observed sample has unique failure times <span class="math inline">\(t_1 &lt; \dots &lt; t_m\)</span>, each with a single failing subject denoted by <span class="math inline">\((j)\)</span>. Let <span class="math inline">\(\mathcal{R}_j\)</span> be the risk set at <span class="math inline">\(t_j\)</span>, meaning the indices of all subjects still under observation just before <span class="math inline">\(t_j\)</span>.</p>
 <div class="quarto-figure quarto-figure-center">
 <figure class="figure">
-<p><img src="images/cox_time_points.png" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:85.0%"></p>
+<p><img src="images/cox_time_points.png" class="img-fluid quarto-figure quarto-figure-center figure-img" style="width:80.0%"></p>
 </figure>
 </div>
 <p>Then the Cox partial likelihood is</p>

docs/sitemap.xml

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-    <lastmod>2025-02-14T00:21:21.078Z</lastmod>
+    <lastmod>2025-02-14T00:23:08.870Z</lastmod>
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