Goodness-of-fit tests for correlated paired binary data

被引：15

作者：

Tang, Man-Lai ^{[2
]}

Pei, Yan-Bo ^{[3
]}

Wong, Weng-Kee ^{[4
]}

Li, Jia-Liang ^{[1
]}

机构：

[1] Natl Univ Singapore, Duke NUS Grad Med Sch, Dept Stat & Appl Probabil, Singapore 119077, Singapore

[2] Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R China

[3] Capital Univ Econ & Business, Sch Stat, Beijing, Peoples R China

[4] Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA USA

来源：

STATISTICAL METHODS IN MEDICAL RESEARCH | 2012年 / 21卷 / 04期

基金：

英国医学研究理事会;

关键词：

Akaike information criterion; bootstrap procedures; correlated binary data; Rosner's and Dallal's models; model selection techniques; DOUBLE-BLIND; EQUALITY; PROPORTIONS; INFERENCE;

D O I：

10.1177/0962280210381176

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

We review a few popular statistical models for correlated binary outcomes, present maximum likelihood estimates for the model parameters, and discuss model selection issues using a variety of goodness-of-fit test statistics. We apply bootstrap strategies that are computationally efficient to evaluate the performance of goodness-of-fit statistics and observe that generally the power and the type I error rate of the goodness-of-fit statistics depend on the model under investigation. Our simulation results show that careful choice of goodness-of-fit statistics is an important issue especially when we have a small sample and the outcomes are highly correlated. Two biomedical applications are included.

引用

页码：331 / 345

页数：15

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