MethodsAn Alternative Perspective on the Robust Poisson Method for Estimating Risk or Prevalence RatiosTalbot, Denisa,b; Mésidor, Micelinea,b; Chiu, Yohannc; Simard, Marca,d; Sirois, Carolineb,c,d Author Information From the aDépartement de médecine sociale et préventive, Université Laval, Québec, Canada bUnité santé des populations et pratiques optimales en santé, CHU de Québec – Université Laval research center, Québec, Québec, Canada cFaculté de pharmacie, Université Laval, Québec, Québec, Canada dInstitut National de Santé Publique du Québec. Submitted October 17, 2021; accepted September 13, 2022 This work was supported by a grant from the Fonds de recherche du Québec – Santé [#265385 to DT]. D.T. and C.S. were supported by a career award from the Fonds de recherche du Québec – Santé. The data are the propriety of Ministère de la santé et des services sociaux du Québec and the Régie de l’assurance maladie du Québec. Access to these data is limited to authorized personnel of the Chronic Disease and Injury Surveillance Unit. The authors report no conflicts of interest. Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). Correspondence: Denis Talbot, Département de médecine sociale et préventive, Faculté de médecine, Université Laval, 1050, avenue de la Médecine, Pavillon Ferdinand-Vandry, room 2454, Québec (Québec) G1V 0A6, Canada. E-mail: [email protected] Epidemiology 34(1):p 1-7, January 2023. | DOI: 10.1097/EDE.0000000000001544 Buy SDC Metrics Abstract The robust Poisson method is becoming increasingly popular when estimating the association of exposures with a binary outcome. Unlike the logistic regression model, the robust Poisson method yields results that can be interpreted as risk or prevalence ratios. In addition, it does not suffer from frequent nonconvergence problems such as the most common implementations of maximum likelihood estimators of the log-binomial model. However, using a Poisson distribution to model a binary outcome may seem counterintuitive. Methodologic papers have often presented this as a good approximation to the more natural binomial distribution. In this article, we provide an alternative perspective to the robust Poisson method based on the semiparametric theory. This perspective highlights that the robust Poisson method does not require assuming a Poisson distribution for the outcome. In fact, the method only assumes a log-linear relation between the risk or prevalence of the outcome and the explanatory variables. This assumption and the consequences of its violation are discussed. We also provide suggestions to reduce the risk of violating the modeling assumption. Additionally, we discuss and contrast the robust Poisson method with other approaches for estimating exposure risk or prevalence ratios. See video abstract at, https://links.lww.com/EDE/B987. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.