Obtaining adjusted prevalence ratios from logistic regression model in cross-sectional studies

TL;DR

This paper demonstrates how to directly estimate adjusted prevalence ratios from logistic regression in cross-sectional studies, offering a stable and straightforward alternative to traditional methods like log-binomial and Poisson regression.

Contribution

It introduces a method to derive prevalence ratios from logistic regression estimates, overcoming convergence issues and simplifying implementation.

Findings

Prevalence ratios can be accurately estimated from logistic regression.

The proposed method avoids numerical instability common in other models.

It is easy to implement using standard statistical software.

Abstract

In the last decades, it has been discussed the use of epidemiological prevalence ratio (PR) rather than odds ratio as a measure of association to be estimated in cross-sectional studies. The main difficulties in use of statistical models for the calculation of PR are convergence problems, availability of adequate tools and strong assumptions. The goal of this study is to illustrate how to estimate PR and its confidence interval directly from logistic regression estimates. We present three examples and compare the adjusted estimates of PR with the estimates obtained by use of log-binomial, robust Poisson regression and adjusted prevalence odds ratio (POR). The marginal and conditional prevalence ratios estimated from logistic regression showed the following advantages: no numerical instability; simple to implement in a statistical software; and assumes the adequate probability distribution for the outcome.