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Full-Text Articles in Statistics and Probability
Inequality In Treatment Benefits: Can We Determine If A New Treatment Benefits The Many Or The Few?, Emily Huang, Ethan Fang, Daniel Hanley, Michael Rosenblum
Inequality In Treatment Benefits: Can We Determine If A New Treatment Benefits The Many Or The Few?, Emily Huang, Ethan Fang, Daniel Hanley, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
The primary analysis in many randomized controlled trials focuses on the average treatment effect and does not address whether treatment benefits are widespread or limited to a select few. This problem affects many disease areas, since it stems from how randomized trials, often the gold standard for evaluating treatments, are designed and analyzed. Our goal is to learn about the fraction who benefit from a treatment, based on randomized trial data. We consider the case where the outcome is ordinal, with binary outcomes as a special case. In general, the fraction who benefit is a non-identifiable parameter, and the best …
C-Learning: A New Classification Framework To Estimate Optimal Dynamic Treatment Regimes, Baqun Zhang, Min Zhang
C-Learning: A New Classification Framework To Estimate Optimal Dynamic Treatment Regimes, Baqun Zhang, Min Zhang
The University of Michigan Department of Biostatistics Working Paper Series
Personalizing treatment to accommodate patient heterogeneity and the evolving nature of a disease over time has received considerable attention lately. A dynamic treatment regime is a set of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual’s own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential classification problem and is equivalent to sequentially minimizing a weighted expected misclassification error. This general classification perspective targets the exact goal of optimally individualizing treatments and is new and fundamentally …