标题：Application of different statistical methods to estimate relative risk for self-reported health complaints among shoe factory workers exposed to organic solvents and plastic compounds
其他标题：Application of different statistical methods to estimate relative risk for self-reported health complaints among shoe factory workers exposed to organic solvents and plastic compounds
出版社：Norsk forening for epidemiologi - The Norwegian Epidemiological Association
摘要：Objectives : 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 a theoretical viewpoint. However, some individuals had a predicted risk larger than one, which caused the computation to abort. Cox regression could produce heavy ties when adjusted for confounders and yielded rather wide CIs, however, by using robust variance we will obtain narrow CIs. In conclusion, the two suggested methods have certain limitations and difficulties. However, Cox regression encountered less serious problems than in the other methods, and is also widely available.
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