An Alternative Perspective on the Robust Poisson Method for Estimating Risk or Prevalence Ratios

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 fre...

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Bibliographic Details
Published in:Epidemiology (Cambridge, Mass.) Vol. 34; no. 1; p. 1
Main Authors: Talbot, Denis, Mésidor, Miceline, Chiu, Yohann, Simard, Marc, Sirois, Caroline
Format: Journal Article
Language:English
Published: United States 01.01.2023
Subjects:
Humans
Logistic Models
Models, Statistical
Poisson Distribution
Prevalence
ISSN:1531-5487, 1531-5487
Online Access:Get more information
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Summary: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, http://links.lww.com/EDE/B987 .
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ISSN:1531-5487
1531-5487
DOI:10.1097/EDE.0000000000001544