Safety evaluation is a continual and iterative process throughout the drug development life cycle and requires long time horizons and large amounts of data to fully understand the safety profile of a medical product. Although randomized clinical trials (RCT) provide high-quality data for an initial assessment of safety signals, the safety signals may not all have been known at the time of approval because safety data collected from RCT only involve a relatively small number of subjects during a relatively short follow-up period. The increased accumulation of post-marketing real-world data (RWD) presents an opportunity to utilize them for safety decision-making; these include identifying new safety signals, further characterization of safety concerns that are raised in pre-marketing RCT, and further generalization of RCT findings to the broader patient populations not previously studied in RCT. In this paper, we use cardiovascular safety outcome trial for antidiabetic therapies as an illustrative example and discuss how integrative analysis of RCT and observational study data can answer regulatory concerns about cardiovascular risk in a post-marketing setting. A novel statistical analysis strategy is proposed to combine both sources of safety data in a data fusion approach. The proposed approach includes three stages: (1) feasibility analysis that uses an RCT to validate an observational study, applying estimand framework and emulating RCT with RWD; (2) integrative analysis that combines evidence from the RCT and observational study data cooperatively; and (3) sensitivity analysis that examines the consistency of the previous analyses. Two potential utilities of the proposed integrative analysis for the cardiovascular safety outcome trial are discussed.
Keywords: Cardiovascular outcome trial; Causal inference; Observational study; Randomized clinical trial; Real-world data; Type 2 diabetes mellitus.
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