Nonparametric methods for inference after variable selection, comparisons of survival distributions, and random effects meta-analysis, and reporting of subgroup analyses

The chapters of this thesis focus on developing novel statistical methodologies to address issues arising from clinical trials and other association studies. In the first chapter, we develop testing and interval estimation methods for parameters reflecting the marginal association between the selected covariates and response variable, based on the same data set used for variable selection. We provide theoretical justification for the proposed methods, present results to guide their implementation, use simulations to assess and compare their performance to a sample-splitting approach, and illustrate the methods with data from a recent AIDS study. The second chapter addresses two-group comparisons with a time-to-event endpoint when sample sizes are small and censoring rates may differ between the two groups. We propose two approximate tests, based on first imputing survival and censoring times and then applying permutation methods, that have good properties over a range of settings. Furthermore, the new approaches can be used to obtain point and interval estimates of the parameter characterizing the treatment difference in a semi-parametric accelerated failure model. The proposed met