Causal inference for longitudinal data based on historical controls

Use of historical data has become a hot topic recently, considered to provide a way to reduce patient burden, lower drug development cost, and make innovative therapies available to patients earlier. In a single-arm study designed to examine the benefit of an experimental treatment, there is often a desire to compare the outcomes of patients receiving the new intervention with those receiving a control treatment, which can be extracted from sources such as historical trials or electronic medical records. Since the treatment is not randomly assigned, there is a need to adjust for the potential imbalance in key patient characteristics between the current study and historical controls. If the outcome of interest is measured longitudinally and subject to random missing, the required adjustment becomes more complicated. In this paper, we propose a doubly robust adjustment procedure specifically designed for longitudinal data analysis with missing data. The proposed method yields valid analysis results, if either the propensity score model or the mixed effects model for repeated measures (MMRM) regression model is correctly specified. An extensive numerical study is conducted to examine the performance of the proposed method. Data from a real clinical trial comparing with historical data are analyzed as an example applying the proposed procedure.