The logistic regression analysis proposed by Schouten et al. (Stat Med. 1993;12:1733-1745) has been a standard method in current statistical analysis of case-cohort studies, and it enables effective estimation of risk ratio from selected subsamples. Schouten et al. (1993) also proposed the standard error estimate of the risk ratio estimator can be calculated by the robust variance estimator. In this article, however, we show that the robust variance estimator does not account for the duplications of case and subcohort samples and generally has certain bias, i.e., inaccurate confidence intervals and P-values are possibly obtained. To address the invalid statistical inference problem, we provide an alternative bootstrap-based valid variance estimator. Through simulation studies, the bootstrap method consistently provided more precise confidence intervals compared with those provided by the robust variance method, while retaining adequate coverage probabilities. The conventional robust variance estimator has certain bias, and inadequate conclusions might be deduced. The bootstrap method would be an alternative effective approach in practice to provide accurate evidence.
翻译:Schouten等人(Stat med. 1993;12:1733-1745)提出的后勤回归分析(Sat med. 1993;12:1733-1745)是目前对案件分组研究进行统计分析的一个标准方法,它使得能够有效估计选定子抽样的风险比率。Schouten等人(1993年)还提议了风险估计比率的标准误差估计,该估计比率可以通过稳健的差异估计器计算出。但是,在本条中,我们表明,稳健的差异估计器没有考虑到案件和亚组样本的重复,而且一般存在某些偏差,即不准确的信任间隔和P值可能获得。为了解决无效的统计推断问题,我们提供了一个基于陷阱的替代有效差异估计器。通过模拟研究,靴套方法始终提供了与稳健差异方法相比的更准确的信任间隔,同时保留了适当的覆盖概率。常规的稳健差异估计器具有某些偏差,而且可能得出不适当的结论。靴套方法是实践中提供准确证据的替代有效方法。