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Real-world data, such as administrative claims and electronic health records,are increasingly used for safety monitoring and to help guide regulatorydecision-making. In these settings, it is important to document analyticdecisions transparently and objectively to ensure that analyses meet theirintended goals. The Causal Roadmap is an established framework that can guideand document analytic decisions through each step of the analytic pipeline,which will help investigators generate high-quality real-world evidence. Inthis paper, we illustrate the utility of the Causal Roadmap using two casestudies previously led by workgroups sponsored by the Sentinel Initiative -- aprogram for actively monitoring the safety of regulated medical products. Eachcase example focuses on different aspects of the analytic pipeline for drugsafety monitoring. The first case study shows how the Causal Roadmap encouragestransparency, reproducibility, and objective decision-making for causalanalyses. The second case study highlights how this framework can guideanalytic decisions beyond inference on causal parameters, improving outcomeascertainment in clinical phenotyping. These examples provide a structuredframework for implementing the Causal Roadmap in safety surveillance and guidetransparent, reproducible, and objective analysis.

An Application of the Causal Roadmap in Two Safety Monitoring Case  Studies: Covariate-Adjustment and Outcome Prediction using Electronic Health  Record Data