title: Goodness-of-fit tests for correlated paired binary data.
abstract: 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.
authors.affiliation: Department of Mathematics, Hong Kong Baptist University, Hong Kong, China.
authors.forename: Man-Lai
authors.initials: ML
authors.lastname: Tang
authors.forename: Yan-Bo
authors.initials: YB
authors.lastname: Pei
authors.forename: Weng-Kee
authors.initials: WK
authors.lastname: Wong
authors.forename: Jia-Liang
authors.initials: JL
authors.lastname: Li
mesh_terms: D004311:Double-Blind Method; D006801:Humans; D015233:Models, Statistical; D012595:Scleroderma, Systemic / Q000628:therapy