  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 non-convergence problems like 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.
Methodological papers have often presented this as a good approximation to the
more natural binomial distribution. In this paper, 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 relationship between the risk/prevalence of the outcome
and the explanatory variables. This assumption and consequences of its
violation are discussed. Suggestions to reduce the risk of violating the
modeling assumption are also provided. Additionally, we discuss and contrast
the robust Poisson method with other approaches for estimating exposure risk or
prevalence ratios.
