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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Using the ROC curve for gauging treatment effect in clinical trials
Lyndia C Brumback1, Margaret S Pepe, Todd A Alonzo
1Department of Biostatistics, University of Washington, Box 357232, Seattle, Washington 98195-7232, USA. lynb@u.washington.edu
Abstract:
Non-parametric procedures such as the Wilcoxon rank-sum test, or equivalently the Mann-Whitney test, are often used to analyse data from clinical trials. These procedures enable testing for treatment effect, but traditionally do not account for covariates. We adapt recently developed methods for receiver operating characteristic (ROC) curve regression analysis to extend the Mann-Whitney test to accommodate covariate adjustment and evaluation of effect modification. Our approach naturally extends use of the Mann-Whitney statistic in a fashion that is analogous to how linear models extend the t-test. We illustrate the methodology with data from clinical trials of a therapy for Cystic Fibrosis.
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