A Propensity Score-Enhanced Sequential Analytic Method for Comparative Drug Safety Surveillance

摘要

We introduce a new sequential monitoring approach to facilitate the use of observational electronic healthcare utilization databases in comparative drug safety surveillance studies comparing the safety between two approved medical products. The new approach enhances the confounder adjustment capabilities of the conditional sequential sampling procedure (CSSP), an existing group sequential method for sequentially monitoring excess risks of adverse events following the introduction of a new medical product. It applies to a prospective cohort setting where information for both treatment and comparison groups accumulates concurrently over time. CSSP adjusts for covariates through stratification and thus it may have limited capacity to control for confounding as it can only accommodate a few categorical covariates. To address this issue, we propose the propensity score (PS)-stratified CSSP, in which we construct strata based on selected percentiles of the estimated PSs. The PS is defined as the conditional probability of being treated given measured baseline covariates and is commonly used in epidemiological studies to adjust for confounding bias. The PS-stratified CSSP approach integrates this more flexible confounding adjustment, PS-stratification, with the sequential analytic approach, CSSP, thus inheriting CSSP’s attractive features: (i) it accommodates varying amounts of person follow-up time, (ii) it uses exact conditional inference, which can be important when studying rare safety outcomes, and (iii) it allows for a large number of interim tests. Further, it overcomes CSSP’s difficulty with adjusting for multiple categorical and continuous confounders.