  Real-world data, such as administrative claims and electronic health records,
are increasingly used for safety monitoring and to help guide regulatory
decision-making. In these settings, it is important to document analytic
decisions transparently and objectively to ensure that analyses meet their
intended goals. The Causal Roadmap is an established framework that can guide
and document analytic decisions through each step of the analytic pipeline,
which will help investigators generate high-quality real-world evidence. In
this paper, we illustrate the utility of the Causal Roadmap using two case
studies previously led by workgroups sponsored by the Sentinel Initiative -- a
program for actively monitoring the safety of regulated medical products. Each
case example focuses on different aspects of the analytic pipeline for drug
safety monitoring. The first case study shows how the Causal Roadmap encourages
transparency, reproducibility, and objective decision-making for causal
analyses. The second case study highlights how this framework can guide
analytic decisions beyond inference on causal parameters, improving outcome
ascertainment in clinical phenotyping. These examples provide a structured
framework for implementing the Causal Roadmap in safety surveillance and guide
transparent, reproducible, and objective analysis.
