MethodsDefining and Identifying Per-protocol Effects in Randomized TrialsRudolph, Jacqueline E.a; Naimi, Ashley I.a; Westreich, Daniel J.b; Kennedy, Edward H.c; Schisterman, Enrique F.dAuthor Information From the aDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA bDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC cDepartment of Statistics, Carnegie Mellon University, Pittsburgh, PA dEpidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland. Submitted October 29, 2019; accepted June 29, 2020. This work was supported by grant NIH-R01 HD093602 and the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland (contract numbers HHSN267200603423, HHSN267200603424, and HHSN267200603426). The authors report no conflicts of interest. Correspondence: Jacqueline E. Rudolph, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261. E-mail: email@example.com. Epidemiology: September 2020 - Volume 31 - Issue 5 - p 692-694 doi: 10.1097/EDE.0000000000001234 Buy Metrics Abstract In trials with noncompliance to assigned treatment, researchers might be interested in estimating a per-protocol effect—a comparison of two counterfactual outcomes defined by treatment assignment and (often time-varying) compliance with a well-defined treatment protocol. Here, we provide a general counterfactual definition of a per-protocol effect and discuss examples of per-protocol effects that are of either substantive or methodologic interest. In doing so, we seek to make more concrete what per-protocol effects are and highlight that one can estimate per-protocol effects that are more than just a comparison of always taking treatment in two distinct treatment arms. We then discuss one set of identifiability conditions that allow for identification of a causal per-protocol effect, highlighting some potential violations of those conditions that might arise when estimating per-protocol effects. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.