A Comparison of Decision Curve Analysis with Traditional Decision Analysis

Andrew Vickers Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center vickersa@mskcc.org Introduction In a traditional decision analysis, the analyst creates a decision tree and then estimates probabilities and assigns utilities for each possible outcome.

Commentary on Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes

This is a commentary on the paper by Benkeser, Díaz, Luedtke, Segal, Scharfstein, and Rosenblum

Incorrect Covariate Adjustment May Be More Correct than Adjusted Marginal Estimates

This article provides a demonstration that the perceived non-robustness of nonlinear models for covariate adjustment in randomized trials may be less of an issue than the non-transportability of marginal so-called robust estimators.

Avoiding One-Number Summaries of Treatment Effects for RCTs with Binary Outcomes

This article presents an argument that for RCTs with a binary outcome the primary result should be a distribution and not any single number summary. The GUSTO-I study is used to exemplify risk difference distributions.

If You Like the Wilcoxon Test You Must Like the Proportional Odds Model

Since the Wilcoxon test is a special case of the proportional odds (PO) model, if one likes the Wilcoxon test, one must like the PO model. This is made more convincing by showing examples of how one may accurately compute the Wilcoxon statistic from the PO model's odds ratio.