  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.
