论文标题
病例研究中逻辑回归的差异估计
Variance estimation for logistic regression in case-cohort studies
论文作者
论文摘要
Schouten等人提出的逻辑回归分析。 （StatMed。1993; 12：1733-1745）在当前对病例研究的统计分析中一直是一种标准方法，并且可以有效地估算从选定子样本中的风险比率。 Schouten等。 （1993年）还提出了可以通过稳健方差估计器计算的风险比估计器的标准误差估计值。但是，在本文中，我们表明，稳健的差异估计器未考虑病例和子体育样本的重复，并且通常可能会获得某些偏见，即可能获得不准确的置信区间和p值。为了解决无效的统计推断问题，我们提供了基于自举的替代有效方差估计器。通过仿真研究，Bootstrap方法一致地提供了与可靠方差方法提供的更精确的置信区间，同时保留了足够的覆盖率概率。常规的鲁棒差异估计器具有一定的偏见，并且可能得出的结论不足。在实践中，Bootstrap方法将是提供准确证据的另一种有效方法。
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.
