Nonparametric randomization-based covariate adjustment for stratified analysis of time-to-event or dichotomous outcomes

作者：Hussey Michael A^*; Koch Gary G; Preisser John S; Saville Benjamin R

来源：Journal of Biopharmaceutical Statistics, 2016, 26(3): 579-589.

DOI：10.1080/10543406.2015.1052483

• Analysis of covariance
• clinical trial
• dichotomous outcomes
• nonparametric methods
• time-to-event
• weighted least squares

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.

• 出版日期2016