Abstract:
In this dissertation, we apply randomization tests in the context of regression models to detect treatment effects for clinical trial data. This application allows us to compute randomization tests for a wide variety of outcomes, including covariate adjusted treatment effects, general response functions, survival data, and longitudinal data. We used score residuals as the outcome of the randomization test under the generalized linear model. For the proportional hazards and accelerated linear model, we used the martingales residuals as the outcome of the randomization test. For the generalized linear mixed model, to detect whether there is a time-varying treatment effect, we compute the predicted random slope from the regression as the outcome of the randomization test.
