title: Nonparametric randomization-based covariate adjustment for stratified analysis of time-to-event or dichotomous outcomes.
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.
authors.affiliation: a Quintiles Transnational, Inc. , Durham , North Carolina , USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Hussey
authors.affiliation: b University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA.
authors.forename: Gary G
authors.initials: GG
authors.lastname: Koch
authors.affiliation: b University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA.
authors.forename: John S
authors.initials: JS
authors.lastname: Preisser
authors.affiliation: c Berry Consultants , Austin , Texas , USA.
authors.forename: Benjamin R
authors.initials: BR
authors.lastname: Saville
mesh_terms: D006801:Humans; D016015:Logistic Models; D016016:Proportional Hazards Models; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic; D018709:Statistics, Nonparametric; D016896:Treatment Outcome
keywords: Analysis of covariance; clinical trial; dichotomous outcomes; nonparametric methods; time-to-event; weighted least squares