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Random Effects Logistic Regression Analysis with Auxiliary Covariates
Author(s) -
Zhou Haibo,
Chen Jianwei,
Cai Jianwen
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00352.x
Subject(s) - covariate , estimator , logistic regression , statistics , computer science , econometrics , biometrics , data set , random effects model , regression analysis , mathematics , artificial intelligence , meta analysis , medicine
Summary. We study a semiparametric estimation method for the random effects logistic regression when there is auxiliary covariate information about the main exposure variable. We extend the semiparametric estimator of Pepe and Fleming (1991, Journal of the American Statistical Association 86 , 108–113) to the random effects model using the best linear unbiased prediction approach of Henderson (1975, Biometrics 31 , 423–448). The method can be used to handle the missing covariate or mismeasured covariate data problems in a variety of real applications. Simulation study results show that the proposed method outperforms the existing methods. We analyzed a data set from the Collaborative Perinatal Project using the proposed method and found that the use of DDT increases the risk of preterm births among US. children.