Conditional logistic regression with sandwich estimators: Application to a meta-analysis
Conditional logistic regression with sandwich estimators: Application to a meta-analysis
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DOI：
10.2307/2534007
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发表时间：
1998-03-01
期刊：
影响因子：
1.9
通讯作者：
Midthune, DN
中科院分区：
文献类型：
--
作者：
Fay, MP;Graubard, BI;Midthune, DN
出于荟萃分析的动物实验的膳食脂肪和总热量摄入对乳腺肿瘤发生的影响，我们探讨了使用三明治估计方差与条件logistic回归。经典的条件Logistic回归假设参数在所有聚类中都是固定效应，而三明治估计量为固定效应或随机效应提供了适当的推断。然而，使用标准Wald检验和三明治估计量的推断要求使用来自大量聚类的信息来估计每个参数。由于我们的例子违反了这个条件，我们对标准Wald检验做了两个修改。首先，我们通过使用标准化残差来减少经验方差估计量（三明治的中间）的偏差。其次，我们近似占这些估计量的方差使用t分布，而不是正态分布，其中的自由度估计使用Satterthwaite的近似。通过模拟，我们表明，这些三明治估计执行几乎以及经典的估计时，真实的效果是固定的，比经典的估计时，真实的效果是随机的。我们实现模拟标称覆盖这些三明治估计，即使一些参数估计从一个小数目的集群。
Motivated by a meta-analysis of animal experiments on the effect of dietary fat and total caloric intake on mammary tumorigenesis, we explore the use of sandwich estimators of variance with conditional logistic regression. Classical conditional logistic regression assumes that the parameters are fixed effects across all clusters, while the sandwich estimator gives appropriate inferences for either fixed effects or random effects. However, inference using the standard Wald test with the sandwich estimator requires that each parameter is estimated using information from a large number of clusters. Since our example violates this condition, we introduce two modifications to the standard Wald test. First, we reduce the bias of the empirical variance estimator (the middle of the sandwich) by using standardized residuals. Second, we approximately account for the variance of these estimators by using the t-distribution instead of the normal distribution, where the degrees of freedom are estimated using Satterthwaite's approximation. Through simulations, we show that these sandwich estimators perform almost as well as classical estimators when the true effects are fixed and much better than the classical estimators when the true effects are random. We achieve simulated nominal coverage for these sandwich estimators even when some parameters are estimated from a small number of clusters.