Understanding the Use of Negative Controls to Assess the Validity of Non-Interventional Studies of Treatment Using Real-World Evidence

Real-world evidence (RWE) generated from real-world data (RWD) is increasingly being utilized to address scientific and regulatory questions at the Food and Drug Administration (FDA), including both product safety and effectiveness. Evaluating RWE for regulatory use, however, depends on a robust causal inference framework, and there is growing stakeholder interest to understand how methodological advances with negative controls can improve causal inference.

On March 8, 2023, the Duke-Margolis Center for Health Policy, under cooperative agreement with the FDA, will convene a workshop on how negative controls could support new methodological approaches for causal inference in the Sentinel System. Discussion will inform the methods development projects aimed at: 1) establishing empirical methods to automate the negative controls identification in Sentinel and integrate it into the Sentinel Initiative tools, and 2) developing approaches to use a double-negative control adjustment to reduce unmeasured confounding in studying effectiveness of vaccines. This workshop will fulfill a Prescription Drug User Fee Act (PDUFA) VII Commitment.

Suggested Workshop Background Reading

1. Shi X, Miao W, Tchetgen ET. A Selective Review of Negative Control Methods in Epidemiology. Curr Epidemiol Rep. 2020 Dec;7(4):190-202. doi: 10.1007/s40471-020-00243-4. Epub 2020 Oct 15. PMID: 33996381; PMCID: PMC8118596.Shi 2020 Double negative controls

2. Erich Kummerfeld, Jaewon Lim, Xu Shi, Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls

3. Shi X, Miao W, Nelson JC, Tchetgen EJT. Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding. J R Stat Soc Series B Stat Methodol. 2020 Apr;82(2):521-540. doi: 10.1111/rssb.12361. Epub 2020 Jan 22. PMID: 33376449; PMCID: PMC7768794

4. Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Stat Med. 2014 Jan 30;33(2):209-18. doi: 10.1002/sim.5925. Epub 2013 Jul 30. PMID: 23900808; PMCID: PMC4285234.Shuemie 2017

5. Schuemie MJ, Hripcsak G, Ryan PB, Madigan D, Suchard MA. Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. Proc Natl Acad Sci U S A. 2018 Mar 13;115(11):2571-2577. doi: 10.1073/pnas.1708282114. PMID: 29531023; PMCID: PMC5856503

This public workshop is supported by the Food and Drug Administration (FDA) of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award [U19FD006602] totaling $4,241,714 with 100 percent funded by FDA/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by FDA/HHS, or the U.S. Government.