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Small proportions: what to report for confidence intervals?

机译:小比例:要报告置信区间的内容?

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PURPOSE: It is generally agreed that a confidence interval (CI) is usually more informative than a point estimate or p-value, but we rarely encounter small proportions with CI in the pharmacoepidemiological literature. When a CI is given it is sporadically reported, how it was calculated. This incorrectly suggests one single method to calculate CIs. To identify the method best suited for small proportions, seven approximate methods and the Clopper-Pearson Exact method to calculate CIs were compared. METHODS: In a simulation study for 90-, 95- and 99%CIs, with sample size 1000 and proportions ranging from 0.001 to 0.01, were evaluated systematically. Main quality criteria were coverage and interval width. The methods are illustrated using data from pharmacoepidemiology studies. RESULTS: Simulations showed that standard Wald methods have insufficient coverage probability regardless of how the desired coverage is perceived. Overall, the Exact method and the Score method with continuity correction (CC) performed best. Real life examples showed the methods to yield different results too. CONCLUSIONS: For CIs for small proportions (pi
机译:目的:通常认为,置信区间(CI)通常比点估计或p值更具信息性,但是在药物流行病学文献中,CI很少遇到。给出CI时,会偶尔报告该CI的计算方式。这错误地建议了一种计算CI的单一方法。为了确定最适合小比例的方法,比较了七个近似方法和Clopper-Pearson Exact方法来计算CI。方法:在针对90%,95%和99%CI的模拟研究中,系统地评估了样本量为1000,比例在0.001至0.01之间的CI。主要质量标准是覆盖范围和间隔宽度。使用来自药物流行病学研究的数据说明了这些方法。结果:仿真表明,标准Wald方法的覆盖率不足,无论如何感知所需的覆盖率。总体而言,采用连续性校正(CC)的精确方法和得分方法表现最佳。现实生活中的例子也说明了产生不同结果的方法。结论:对于小比例的CI(pi

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