Contributions to the practical application of Bayesian methods to survival analysis in clinical trials.

This dissertation is composed of three chapters that deal with fairly distinct concepts. In the first chapter, we compare and contrast the major Bayesian computational platforms accessible in the R statistical computing environment using large-scale simulations across a diverse collection of modeling scenarios. In the second chapter, we assess the performance of several model selection criteria for a complex family of network meta-analysis models for survival data. We also propose a technique for study outlier detection and present simulation results that demonstrate its effectiveness. Finally, the third chapter covers methods for constructing prediction intervals for forecasts for various machine learning algorithms. After describing existing strategies, we propose a new technique for measuring forecast uncertainty that can be used on a wide set of machine learning models. This final chapter was the result of a collaboration with researchers at the Institute for Defense Analyses (IDA).