Fay M P, Graubard B I
National Cancer Institute, Bethesda, Maryland 20892-8317, USA.
Biometrics. 2001 Dec;57(4):1198-206. doi: 10.1111/j.0006-341x.2001.01198.x.
The sandwich estimator of variance may be used to create robust Wald-type tests from estimating equations that are sums of K independent or approximately independent terms. For example, for repeated measures data on K individuals, each term relates to a different individual. These tests applied to a parameter may have greater than nominal size if K is small, or more generally if the parameter to be tested is essentially estimated from a small number of terms in the estimating equation. We offer some practical modifications to these robust Wald-type tests, which asymptotically approach the usual robust Wald-type tests. We show that one of these modifications provides exact coverage for a simple case and examine by simulation the modifications applied to the generalized estimating equations of Liang and Zeger (1986), conditional logistic regression, and the Cox proportional hazard model.
方差的三明治估计量可用于从由(K)个独立或近似独立项之和构成的估计方程创建稳健的 Wald 型检验。例如，对于(K)个个体的重复测量数据，每个项与不同的个体相关。如果(K)较小，或者更一般地，如果要检验的参数基本上是从估计方程中的少量项估计出来的，那么应用于参数的这些检验可能会有大于名义大小的情况。我们对这些稳健的 Wald 型检验提出了一些实际的修正，这些修正渐近地趋近于通常的稳健 Wald 型检验。我们表明，这些修正之一在一个简单情况下提供了精确的覆盖率，并通过模拟研究了应用于 Liang 和 Zeger（1986）的广义估计方程、条件逻辑回归以及 Cox 比例风险模型的修正。
