Date Available

5-20-2022

Year of Publication

2020

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Arts and Sciences

Department/School/Program

Statistics

Advisor

Dr. Arnold Stromberg

Co-Director of Graduate Studies

Dr. Chi Wang

Abstract

The Bayesian adjustment for confounding (BAC) is a Bayesian model averaging method to select and adjust for confounding factors when evaluating the average causal effect of an exposure on a certain outcome. We extend the BAC method to time-to-event outcomes. Specifically, the posterior distribution of the exposure effect on a time-to-event outcome is calculated as a weighted average of posterior distributions from a number of candidate proportional hazards models, weighing each model by its ability to adjust for confounding factors. The Bayesian Information Criterion based on the partial likelihood is used to compare different models and approximate the Bayes factor. The posterior sample of the exposure effect is obtained using STAN. Performance of our method is assessed using simulation studies and real data applications.

In phase II randomized trials, due to the limited sample size, randomization may not be able to balance the distributions of all baseline characteristics. As a result, some factors may be correlated with the treatment assignment, which could cause the treatment effect estimation to deviate from the underlying true effect value. Another important issue in treatment effect evaluation is to identify factors that are associated with the endpoint and include them in the model, which can reduce the overall variation of the model and lead to more precise estimation of the treatment effect. In this dissertation, we present a Bayesian model selection method to identify and adjust for both unbalanced confounding factors and factors associated with the endpoint. Our method extends the Bayesian adjustment for confounding (BAC) method, which is designed for observational studies, to randomized clinical trials. Simulation studies and real data analysis demonstrate that our method is able to provide an unbiased estimation of the treatment effect and reduce the variation of the estimation.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2020.211

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