Bayesian Adjustment for Confounding when Estimating Average Causal Effects for Time-to-event Outcomes
Li Xu, Arnold Stromberg, Chi Wang
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 OPENBUGS. Performance of our method is assessed using simulation studies.
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