Abstract
Pharmaceutical development is a time-consuming, very expensive, and highly risky process. Most of budget for research and development goes to clinical trials in clinical development. Therefore, there are urgent needs of statistical strategies to enhance clinical research design, reduce a required sample size, shorten a duration of drug development, and increase a success rate of new drug development. Three new statistical methodologies of clinical trials are proposed in this dissertation, including establishing non-inferiority efficacy of a new treatment with binary outcomes in a three-arm trial; a weighted combination approach combining information from original region and local bridging region in a bridging study; and assessing a consistency of a known or a unknown treatment effect under a discrete random effects model in multiregional clinical trials (MRCTs). Sample size requirements for the proposed methodologies are derived. Simulation studies of type I error rate and power based on the proposed methods are given. Simulation results show that the methodologies are robust and well-controlled in terms of true-positive and false positive rates. We illustrate the methods using real data sets and simulated data for examples, and then we present applications from both a statistical and an economic point of view. Furthermore, all programs of proposed methods are executed from the R environment and the programs are available by request to the authors.