Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records

Electronic health records (EHR), containing rich clinical histories of large patient populations, can provide evidence for clinical decisions when evidence from trials and literature is absent. To enable such observational studies from EHR in real time, particularly in emergencies, rapid confounder control methods that can handle numerous variables and adjust for biases are imperative. This study compares the performance of 18 automatic confounder control methods.

Methods include propensity scores, direct adjustment by machine learning, similarity matching and resampling in two simulated and one real-world EHR datasets.

Direct adjustment by lasso regression and ensemble models involving multiple resamples have performance comparable to expert-based propensity scores and thus, may help provide real-time EHR-based evidence for timely clinical decisions.