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Semiparametric Transformation Models with Random Effects for Joint Analysis of Recurrent and Terminal Events
Author(s) -
Zeng Donglin,
Lin D. Y.
Publication year - 2009
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.1541-0420.2008.01126.x
Subject(s) - estimator , transformation (genetics) , inference , mathematics , nonparametric statistics , semiparametric regression , semiparametric model , random effects model , computer science , estimating equations , statistics , artificial intelligence , medicine , biochemistry , chemistry , meta analysis , gene
Summary We propose a broad class of semiparametric transformation models with random effects for the joint analysis of recurrent events and a terminal event. The transformation models include proportional hazards/intensity and proportional odds models. We estimate the model parameters by the nonparametric maximum likelihood approach. The estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Simple and stable numerical algorithms are provided to calculate the parameter estimators and to estimate their variances. Extensive simulation studies demonstrate that the proposed inference procedures perform well in realistic settings. Applications to two HIV/AIDS studies are presented.