Nonparametric randomization-based covariate adjustment for stratified analysis of time-to-event or dichotomous outcomes |
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Authors: | Michael A. Hussey Gary G. Koch John S. Preisser Benjamin R. Saville |
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Affiliation: | 1. Quintiles Transnational, Inc., Durham, North Carolina, USAmichael.hussey@iddi.com;3. University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA;4. Berry Consultants, Austin, Texas, USA |
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Abstract: | Time-to-event or dichotomous outcomes in randomized clinical trials often have analyses using the Cox proportional hazards model or conditional logistic regression, respectively, to obtain covariate-adjusted log hazard (or odds) ratios. Nonparametric Randomization-Based Analysis of Covariance (NPANCOVA) can be applied to unadjusted log hazard (or odds) ratios estimated from a model containing treatment as the only explanatory variable. These adjusted estimates are stratified population-averaged treatment effects and only require a valid randomization to the two treatment groups and avoid key modeling assumptions (e.g., proportional hazards in the case of a Cox model) for the adjustment variables. The methodology has application in the regulatory environment where such assumptions cannot be verified a priori. Application of the methodology is illustrated through three examples on real data from two randomized trials. |
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Keywords: | Analysis of covariance clinical trial dichotomous outcomes nonparametric methods time-to-event weighted least squares |
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