Real-world evidence involving healthcare database studies is well established for making causal inferences in post-market drug safety studies and methods, data, and research infrastructure for evaluating effectiveness have advanced in recent years. The rapidly expanding field of etiologic research using insurance claims and electronic health records databases is being evaluated for supporting effectiveness claims. One such use case to support regulatory decision-making on effectiveness is for expanding indications beyond existing effectiveness claims. Confidence in the validity of findings from cohort studies conducted using databases (hereafter "database study") to support indication expansions could be increased through a structured benchmarking process of an initial database study against RCT evidence followed by calibration of a subsequent database study based on differences in results observed in the initial RCT-database pair. This paper proposes a benchmark, expand, and calibration (BenchExCal) approach to trial emulation and describes the design and process for evaluating the performance of the approach through both simulation studies
five planned empirical examples are also described. The project will provide insights regarding how a first-stage benchmarking emulation of a completed trial for an existing indication can be used to calibrate, increase confidence, and improve interpretation of the results for a second-stage emulation of a hypothetical trial that could potentially provide evidence for an expanded indication. Although the examples have been selected to provide a variety of learnings, five use cases do not address all clinical and data scenarios that may be encountered when seeking a supplemental indication for a marketed drug.