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首页> 外文期刊>Norsk Epidemiologi >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
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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

机译:应用不同的统计方法来估算暴露于有机溶剂和塑料化合物的制鞋厂工人自我报告的健康投诉的相对风险

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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.
机译:目标:患病率比(POR)通常被用作横断面研究中相对风险(RR)的替代物。当患病率很高时,POR可能不是RR的近似值。在评估暴露效果时,患病率(PRs)更容易解释。我们的目标是使用三种不同的统计方法在真实数据集上比较PR的估计值和相应的95%置信区间(CI),并报告在应用这些方法时可能遇到的实际问题。方法:比较了两种统计方法:对数二项回归和Cox回归。我们检查了164位希伯伦制鞋厂工人中选定的高患病率症状:头痛,四肢发麻和呼吸困难,以及它们与溶剂暴露的工作任务的关系。结果:这两种方法估算出的粗略PR估算值相同,而调整后的估算值则非常相似。 Cox回归中的CI较对数二项式回归更宽,例如,对数二项式回归中参与清洁任务和四肢发麻之间的关联性的调整估计值证明了这一点(PR = 1.78; CI = 1.25-2.54),Cox回归( PR = 1.76; CI = 1.01–3.06)。当我们使用具有强方差的Cox回归时,我们获得了较窄的CI(PR = 1.76; CI = 1.19–2.60)。在对数二项式回归分析中,我们必须排除一些预期风险超过一个的受试者。结论:从理论观点来看,对数二项式回归是适当的。但是,某些人的预测风险大于1,这导致计算中止。当针对混杂因素进行调整时,Cox回归可能会产生紧密联系,并产生相当宽的置信区间,但是,通过使用强大的方差,我们将获得较窄的置信区间。总之,两种建议的方法都有一定的局限性和困难。但是,与其他方法相比,Cox回归所遇到的问题没有那么严重,并且可以广泛使用。

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