Academic Article


  • 2005

Prevalence odds ratio (POR) is commonly used as a surrogate for relative risk (RR) in crosssectional studies. When prevalences are high, POR may be a poor approximation for RR. Prevalence ratios (PRs) are more easily interpretable when evaluating exposure effects. Our objectives were to compare estimates of PRs and corresponding 95% confidence intervals (CIs) using three different statistical methods on a real data set, furthermore, to report possible practical problems in applying the methods. Methods: Two statistical methods were compared: log-binomial regression and Cox regression. We examined selected high prevalence symptoms: headache, tingling of limbs, and breathing difficulty, and their association with solvent-exposed work tasks in 164 Hebron shoe factory workers. Results: The two methods estimated identical crude point PR estimates and quite similar adjusted estimates. CIs were wider in Cox regression than in log-binominal regression, as exemplified by adjusted estimates for the association between participation in cleaning tasks and tingling of limbs in log-binomial regression (PR=1.78; CI=1.25–2.54), Cox regression (PR=1.76; CI=1.01–3.06). When we used Cox regression with robust variance we obtained narrower CIs (PR=1.76; CI=1.19–2.60). In the log-binomial regression analysis we had to exclude a few subjects with a predicted risk exceeding one. Conclusions: Log-binomial regression is appropriate from...

Nijem, Khaldoun Issa; Kristensen, Petter; al-Khatib, A.; Bjertness, Espen
Norsk Epidemiologi Norsk forening for epidemiologi, Norsk Epidemiologi 15(1): 111–116
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