Propensity Score Analysis With Baseline and Follow‐Up Measurements of the Outcome Variable

Abstract

ABSTRACTA common feature in cohort studies is when there is a baseline measurement of the continuous follow‐up or outcome variable. Common examples include baseline measurements of physiological characteristics such as blood pressure or heart rate in studies where the outcome is post‐baseline measurement of the same variable. Methods incorporating the propensity score are increasingly being used to estimate the effects of treatments using observational studies. We examined six methods for incorporating the baseline value of the follow‐up variable when using propensity score matching or weighting. These methods differed according to whether the baseline value of the follow‐up variable was included or excluded from the propensity score model, whether subsequent regression adjustment was conducted in the matched or weighted sample to adjust for the baseline value of the follow‐up variable, and whether the analysis estimated the effect of treatment on the follow‐up variable or on the change from baseline. We used Monte Carlo simulations with 750 scenarios. While no analytic method had uniformly superior performance, we provide the following recommendations: first, when using weighting and the ATE is the target estimand, use an augmented inverse probability weighted estimator or include the baseline value of the follow‐up variable in the propensity score model and subsequently adjust for the baseline value of the follow‐up variable in a regression model. Second, when the ATT is the target estimand, regardless of whether using weighting or matching, analyze change from baseline using a propensity score that excludes the baseline value of the follow‐up variable.