Mantel-Haenszel estimators of odds ratios for stratified dependent binomial data

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• 作者：Thomas Suesse^a ; ^{tsuesse@uow.edu.au} ; Ivy Liu^b
• 关键词：Dual consistency ; Generalized estimating equations ; Mantel&ndash ; Haenszel estimator ; Marginal models ; Subject-specific effects
• 刊名：Computational Statistics and Data Analysis
• 出版年：2012
• 期刊代码：19_01679473
• 类别：cp
• 出版时间：September, 2012
• 卷：56
• 期：9
• 页码：2705-2717
• 文件大小：338 K

摘要

A standard approach to analyzing binary matched pairs usually represented in 聽2脳2 tables is to apply a subject-specific model; for the simplest situation it is the so-called Rasch model. An alternative population-averaged approach is to apply a marginal model to the single 2脳2 table formed by subjects. For the situation of having an additional stratification variable with 聽levels forming 聽2脳2 tables, standard fitting approaches, such as generalized estimating equations and maximum likelihood, or, alternatively, the standard Mantel-Haenszel (MH) estimator, can be applied. However, while all these standard approaches are consistent under a large-stratum limiting model, they are not consistent under a sparse-data limiting model. In this paper, we propose a new MH estimator and a variance estimator that are both dually consistent: consistent under both large-stratum and sparse-data limiting situations. In a simulation study, the properties of the proposed estimators are confirmed, and the estimator is compared with standard marginal methods. The simulation study also considers the case when the homogeneity assumption of the odds ratios does not hold, and the asymptotic limit of the proposed MH estimator under this situation is derived. The results show that the proposed MH estimator is generally better than the standard estimator, and the same can be said about the associated Wald-type confidence intervals.