New methods for treatment effect calibration, with applications to non-inferiority trials

In comparative effectiveness research, it is often of interest to calibrate treatment effect estimates from a clinical trial to a target population that differs from the study population. One important application is an indirect comparison of a new treatment with a placebo control on the basis of two separate randomized clinical trials: a non-inferiority trial comparing the new treatment with an active control and a historical trial comparing the active control with placebo. The available methods for treatment effect calibration include an outcome regression (OR) method based on a regression model for the outcome and a weighting method based on a propensity score (PS) model. This article proposes new methods for treatment effect calibration: one based on a conditional effect (CE) model and two doubly robust (DR) methods. The first DR method involves a PS model and an OR model, is asymptotically valid if either model is correct, and attains the semiparametric information bound if both models are correct. The second DR method involves a PS model, a CE model, and possibly an OR model, is asymptotically valid under the union of the PS and CE models, and attains the semiparametric information bound if all three models are correct. The various methods are compared in a simulation study and applied to recent clinical trials for treating human immunodeficiency virus infection.