Diao Guoqing, Zeng Donglin, Hu Kuolung, Ibrahim Joseph G
Department of Statistics, George Mason University, Fairfax, VA, U.S.A.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.
Stat Med. 2017 Sep 30;36(22):3475-3494. doi: 10.1002/sim.7351. Epub 2017 May 30.
In 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.
在许多生物医学研究中，对研究期间的事件计数数据进行建模常常是很有意义的。对于一些患者，由于信息性失访，我们可能无法在整个研究期间对他们进行随访。失访时间有可能为事件发生率提供有价值的见解。我们提出了一个针对事件计数数据和信息性失访时间的联合半参数模型，该模型通过伽马脆弱性允许相关性。我们开发了基于似然的高效估计和推断程序。所提出的非参数最大似然估计量被证明是一致的且渐近正态的。此外，有限维参数估计的渐近协方差达到了半参数效率界。大量的模拟研究表明，所提出的方法在实际应用中表现良好。我们通过应用于骨髓增生异常综合征出血和输血事件的一项临床试验来说明所提出的方法。版权所有© 2017约翰·威利父子有限公司。
