Abstract
BackgroundCross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio.MethodsWe compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated.ResultsThree outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified.ConclusionsCox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.
Highlights
Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature
That an important proportion of such studies end up reporting odds ratios, the effect measure yielded by logistic regression, rather than prevalence or incidence ratios
The 95%CI was overestimated by 11% by Poisson regression with scale parameter adjusted by χ2
Summary
Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. The odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Epidemiologic studies found in the literature are frequently cross-sectional, as this is a simple, fast and inexpensive design alternative. Often the outcomes are binary, and logistic regression is used for the analysis. This results in the odds ratio being frequently reported in situations where incidence or prevalence ratios are estimable, despite the fact that it is "biologically interpretable only (page number not for citation purposes). That an important proportion of such studies end up reporting odds ratios, the effect measure yielded by logistic regression, rather than prevalence or incidence ratios
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