A comparison of two bias-corrected covariance estimators for generalized estimating equations
A comparison of two bias-corrected covariance estimators for generalized estimating equations
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DOI：
10.1111/j.1541-0420.2007.00764.x
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发表时间：
2007-09-01
期刊：
影响因子：
1.9
通讯作者：
Wolfson, Mark
中科院分区：
文献类型：
--
作者：
Lu, Bing;Preisser, John S.;Wolfson, Mark
Mancl 和 DeRouen (2001, Biometrics 57, 126-134) 以及 Kauermann 和 Carroll (2001, JASA 96, 1387-1398) 提出了替代偏差校正协方差估计量，用于边际均值回归模型的广义估计方程参数估计。将这些估计器的有限样本属性与低估小样本方差的未校正夹心估计器的有限样本属性进行比较。尽管 Mand 和 DeRouen 的公式通常会高估方差，但它通常会导致接近名义水平的 95% 置信区间的覆盖率，即使在某些簇数只有 10 个的情况下也是如此。对这些看似矛盾的结果的解释是，三明治估计量的巨大变异性导致的覆盖不足的趋势抵消了过度校正偏差的影响。然而，这些积极的结果通常并不成立。对于小簇大小（例如，< 10），它们的估计器通常会导致过度覆盖，并且 Kauermann 和 Carroll 的偏差校正协方差估计器可能是首选。使用来自减少未成年人饮酒的嵌套横断面集群干预试验的数据来说明这些方法。
Mancl and DeRouen (2001, Biometrics 57, 126-134) and Kauermann and Carroll (2001, JASA 96, 1387-1398) proposed alternative bias-corrected covariance estimators for generalized estimating equations parameter estimates of regression models for marginal means. The finite sample properties of these estimators are compared to those of the uncorrected sandwich estimator that underestimates variances in small samples. Although the formula of Mand and DeRouen generally overestimates variances, it often leads to coverage of 95% confidence intervals near the nominal level even in some situations with as few as 10 clusters. An explanation for these seemingly contradictory results is that the tendency to undercoverage resulting from the substantial variability of sandwich estimators counteracts the impact of overcorrecting the bias. However, these positive results do not generally hold; for small cluster sizes (e.g., < 10) their estimator often results in overcoverage, and the bias-corrected covariance estimator of Kauermann and Carroll may be preferred. The methods are illustrated using data from a nested cross-sectional cluster intervention trial on reducing underage drinking.