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.

翻译：现实世界数据（如行政索赔数据和电子健康记录）越来越多地被用于安全性监测并辅助监管决策。在此类场景中，透明客观地记录分析决策至关重要，以确保分析达成预期目标。因果关系路线图（Causal Roadmap）是一个成熟框架，可指导并记录分析流程每个环节的决策，帮助研究者生成高质量的现实世界证据。本文通过两项由哨点倡议（Sentinel Initiative）工作组主导的案例研究（该计划旨在主动监测受监管医疗产品的安全性），阐释因果关系路线图的实用性。每项案例聚焦药物安全性监测分析流程的不同方面：第一项案例展示因果关系路线图如何促进因果分析的透明度、可重复性与客观决策；第二项案例突出该框架如何指导超越因果参数推断的分析决策，改善临床表型中的结局判定。这些案例为在安全性监测中实施因果关系路线图提供了结构化框架，并引导实现透明、可重复与客观的分析。