PS-SAM: doubly robust propensity-score-integrated self-adapting mixture prior to dynamically borrow information from historical data

Music City Center 

There has been a growing interest in incorporating historical data to enhance the efficiency or reduce the sample size of randomized controlled trials (RCTs). A key challenge is that patient characteristics of historical data may differ from those of the current RCT. To address this issue, one well-known approach is to employ propensity score matching or inverse probability weighting to adjust for baseline heterogeneity, enabling the incorporation of historical data into the inference of the RCT. However, this approach is subject to bias when there are unmeasured confounders. We address this issue by incorporating a self-adapting mixture (SAM) prior with propensity score matching and inverse probability weighting to enable additional adaptation for information borrowing in the presence of unmeasured confounders. The resulting propensity score-integrated SAM (PS-SAM) priors are doubly robust in the sense that if there are no unmeasured confounders, they result in an unbiased causal estimate of the treatment effect; and if there are unmeasured confounders, they provide a notably less biased treatment effect with better-controlled type I error. Simulation studies demonstrate that the PS-SAM prior exhibits desirable operating characteristics, with reasonably controlled type I error rates or substantial power gain, small bias, and low MSE, regardless of the presence of unmeasured confounders.

Information borrowing

historical data

mixture prior