alexa Semiparametric transformation models with random effects for joint analysis of recurrent and terminal events.
Psychiatry

Psychiatry

Journal of Psychiatry

Author(s): Zeng D, Lin DY, Zeng D, Lin DY

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Abstract 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.
This article was published in Biometrics and referenced in Journal of Psychiatry

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