• Guoqing Diao, Donglin Zeng, Kuolung Hu, and Joseph G Ibrahim.
• Department of Statistics, George Mason University, Fairfax, VA, U.S.A.
• Stat Med. 2017 Sep 30; 36 (22): 3475-3494.

AbstractIn many biomedical studies, it is often of interest to model event count data over the study period. For some patients, we may not follow up them for the entire study period owing to informative dropout. The dropout time can potentially provide valuable insight on the rate of the events. We propose a joint semiparametric model for event count data and informative dropout time that allows for correlation through a Gamma frailty. We develop efficient likelihood-based estimation and inference procedures. The proposed nonparametric maximum likelihood estimators are shown to be consistent and asymptotically normal. Furthermore, the asymptotic covariances of the finite-dimensional parameter estimates attain the semiparametric efficiency bound. Extensive simulation studies demonstrate that the proposed methods perform well in practice. We illustrate the proposed methods through an application to a clinical trial for bleeding and transfusion events in myelodysplastic syndrome. Copyright © 2017 John Wiley & Sons, Ltd.Copyright © 2017 John Wiley & Sons, Ltd.

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