Molenberghs Geert, Verbeke Geert, Efendi Achmad, Braekers Roel, Demétrio Clarice G B
I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium I-BioStat, Katholieke Universiteit Leuven, Leuven, Belgium
I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium I-BioStat, Katholieke Universiteit Leuven, Leuven, Belgium.
Stat Methods Med Res. 2015 Aug;24(4):434-52. doi: 10.1177/0962280214520730. Epub 2014 Feb 12.
This paper presents, extends, and studies a model for repeated, overdispersed time-to-event outcomes, subject to censoring. Building upon work by Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010), gamma and normal random effects are included in a Weibull model, to account for overdispersion and between-subject effects, respectively. Unlike these authors, censoring is allowed for, and two estimation methods are presented. The partial marginalization approach to full maximum likelihood of Molenberghs et al. (2010) is contrasted with pseudo-likelihood estimation. A limited simulation study is conducted to examine the relative merits of these estimation methods. The modeling framework is employed to analyze data on recurrent asthma attacks in children on the one hand and on survival in cancer patients on the other.
本文提出、扩展并研究了一个针对重复出现的、过度分散的事件发生时间结果且受删失影响的模型。在莫伦贝格斯、韦贝克和德梅特里奥（2007年）以及莫伦贝格斯等人（2010年）的工作基础上，伽马随机效应和正态随机效应被纳入威布尔模型，分别用于解释过度分散和个体间效应。与这些作者不同的是，本文允许删失情况存在，并提出了两种估计方法。莫伦贝格斯等人（2010年）的全最大似然的部分边际化方法与伪似然估计形成对比。进行了一项有限的模拟研究以检验这些估计方法的相对优点。该建模框架一方面用于分析儿童复发性哮喘发作的数据，另一方面用于分析癌症患者的生存数据。
