Incorporating Historical Data via Bayesian Analysis Based on The Logit Model

Abstract

This thesis presents a Bayesian approach to incorporate historical data. Usually, in statistical inference, a large data size is required to establish a strong evidence. However, in most bioassay experiments, dataset is of limited size. Here, we proposed a method that is able to incorporate control groups data from historical studies. The approach is framed in the context of testing whether an increased dosage of the chemical is associated with increased probability of the adverse event. To test whether such a relationship exists, the proposed approach compares two logit models via Bayes factor. In particular, we eliminate the effect of survival time by using poly-k test. We test the performance of the proposed approach by applying it to six simulated scenarios.