Wednesday, November 13, 2019 - 3:30pm to 4:30pm
Title: Bayesian Estimation of Case-Crossover Designs with Repeated Events
The case-crossover design is commonly implemented in epidemiological studies seeking to quantify the association between common exposures and rare events. The power of this design stems from the inherent matching of within-subject invariant covariates, thus allowing for control of these potential confounding factors by design. In practice, conditional logistic regression parameter estimates are computed by noting the equivalence between the conditional logistic likelihood and the partial likelihood arising from Cox's proportional hazards model. Model fitting proceeds by constructing outcome and event indicators to construct appropriate risk sets for each matched set in order to allow for the retrospective comparison between case and control exposures. However, in many case-crossover studies a subject may experience the event of interest repeatedly over the course of follow, resulting in multiple matched case and control observations per subject or cluster. In this talk we propose the direct estimation of cluster-specific covariate effects via a novel semi-parametric hierarchical Bayesian approach. Our approach allows for estimation and comparison of marginal covariate effects as well as cluster-specific random effects via posterior estimates. The research is motivated by, and applied to, data obtained from a case-crossover study of N=7751 children sampled from Orange County, CA seeking to quantify the effect of air pollution exposure on the risk of asthma-related emergency room admissions.
October 29, 2019 - 1:57pm