MethodsImpact of Regression to the Mean on the Synthetic Control Method Bias and Sensitivity AnalysisIllenberger, Nicholas A.a; Small, Dylan S.b; Shaw, Pamela A.aAuthor Information From the aDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA bDepartment of Statistics, University of Pennsylvania, Philadelphia, PA. Submitted September 10, 2019; accepted July 29, 2020. The work of P.A.S. and D.S.S. was supported in part by R01 NIH grant R01-AI131771. The other authors have no conflicts to report. Data and Code: All code for replicating the results of this article can be found in the eAppendix; http://links.lww.com/EDE/B721. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Correspondence: Nicholas Illenberger, Department of Biostatistics, University of Pennsylvania, 108/109 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104. E-mail: email@example.com. Epidemiology: November 2020 - Volume 31 - Issue 6 - p 815-822 doi: 10.1097/EDE.0000000000001252 Buy SDC Metrics Abstract To make informed policy recommendations from observational panel data, researchers must consider the effects of confounding and temporal variability in outcome variables. Difference-in-difference methods allow for estimation of treatment effects under the parallel trends assumption. To justify this assumption, methods for matching based on covariates, outcome levels, and outcome trends—such as the synthetic control approach—have been proposed. While these tools can reduce bias and variability in some settings, we show that certain applications can introduce regression to the mean (RTM) bias into estimates of the treatment effect. Through simulations, we show RTM bias can lead to inflated type I error rates and bias toward the null in typical policy evaluation settings. We develop a novel correction for RTM bias that allows for valid inference and show how this correction can be used in a sensitivity analysis. We apply our proposed sensitivity analysis to reanalyze data concerning the effects of California’s Proposition 99, a large-scale tobacco control program, on statewide smoking rates. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.