Cross-design synthesis for extending the applicability of trial evidence when treatment effect is heterogenous: Part II. Application and external validation

Randomized controlled trials (RCTs) generally provide the most reliable evidence. When participants in RCTs are selected with respect to characteristics that are potential treatment effect modifiers, the average treatment effect from the trials may not be applicable to a specific target population. We present an application of the recently developed calibrated risk-adjusted modeling (CRAM) method for projecting the treatment effect from an RCT to a target group that is inadequately represented in the trial when there is heterogeneity in the treatment effect (HTE). The CRAM method allows for integration of RCT and observational data through cross-design synthesis. An essential component of CRAM is to identify HTE and to then compute a calibration factor for unmeasured confounding for the observational study relative to the RCT. The estimate of treatment effect adjusted for unmeasured confounding is projected onto the target sample using G-computation with standardization weights. In this paper, we apply CRAM to estimate the effect of angiotensin converting enzyme inhibition to prevent heart failure hospitalization or death. External validation shows that when there is adequate overlap between the RCT and the target sample, risk-based standardization is less biased than CRAM. However, when there is poor overlap between the trial and the target sample, CRAM provides superior estimates of treatment effect.