Laud Peter J
Statistical Services Unit, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield, South Yorkshire S3 7RH, U.K.
Pharm Stat. 2017 Sep;16(5):334-348. doi: 10.1002/pst.1813. Epub 2017 Jun 22.
Several methods are available for generating confidence intervals for rate difference, rate ratio, or odds ratio, when comparing two independent binomial proportions or Poisson (exposure-adjusted) incidence rates. Most methods have some degree of systematic bias in one-sided coverage, so that a nominal 95% two-sided interval cannot be assumed to have tail probabilities of 2.5% at each end, and any associated hypothesis test is at risk of inflated type I error rate. Skewness-corrected asymptotic score methods have been shown to have superior equal-tailed coverage properties for the binomial case. This paper completes this class of methods by introducing novel skewness corrections for the Poisson case and for odds ratio, with and without stratification. Graphical methods are used to compare the performance of these intervals against selected alternatives. The skewness-corrected methods perform favourably in all situations-including those with small sample sizes or rare events-and the skewness correction should be considered essential for analysis of rate ratios. The stratified method is found to have excellent coverage properties for a fixed effects analysis. In addition, another new stratified score method is proposed, based on the t-distribution, which is suitable for use in either a fixed effects or random effects analysis. By using a novel weighting scheme, this approach improves on conventional and modern meta-analysis methods with weights that rely on crude estimation of stratum variances. In summary, this paper describes methods that are found to be robust for a wide range of applications in the analysis of rates.
在比较两个独立的二项比例或泊松(暴露调整后)发病率时,有几种方法可用于生成率差、率比或比值比的置信区间。大多数方法在单侧覆盖方面存在一定程度的系统偏差,因此不能假定名义上的95%双侧区间在两端的尾部概率为2.5%,并且任何相关的假设检验都有I型错误率膨胀的风险。对于二项分布情况,已证明偏度校正渐近得分方法具有优越的等尾覆盖特性。本文通过引入针对泊松情况和比值比的新颖偏度校正方法,包括有无分层的情况,完善了这类方法。使用图形方法将这些区间的性能与选定的替代方法进行比较。偏度校正方法在所有情况下都表现良好,包括样本量小或事件罕见的情况,并且偏度校正对于率比分析应被视为必不可少的。发现分层方法在固定效应分析中具有出色的覆盖特性。此外,基于t分布提出了另一种新的分层得分方法,适用于固定效应或随机效应分析。通过使用新颖的加权方案,这种方法改进了依赖于层方差粗略估计权重的传统和现代荟萃分析方法。总之,本文描述的方法在率分析的广泛应用中被发现是稳健的。