Williamson BD, Wyss R, Stuart EA, Dang LE, Mertens AN, Neugebauer RS, Wilson A, Gruber S. An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data.
J Clin Transl Sci 2023;
7:e208. [PMID:
37900347 PMCID:
PMC10603358 DOI:
10.1017/cts.2023.632]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 10/31/2023] Open
Abstract
Background
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 assess and ensure that analyses meet their intended goals.
Methods
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.
Results
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.
Conclusion
These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.
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