Fitting marginal accelerated failure time models to clustered survival data with potentially informative cluster size
Fitting marginal accelerated failure time models to clustered survival data with potentially informative cluster size
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DOI：
10.1016/j.csda.2011.06.015
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发表时间：
2011-12-01
影响因子：
1.8
通讯作者：
Datta, Somnath
中科院分区：
文献类型：
--
作者：
Fan, Jie;Datta, Somnath
聚类生存数据的分析方法在生物医学研究中越来越受欢迎。当聚类的大小与某些聚类特定的潜在因子或一个或多个聚类水平协变量在统计学上相关时，天真地尝试将边缘模型拟合到这样的数据可能会导致有偏估计和误导性推断。一个简单的调整，以纠正潜在的信息集群大小是通过反集群大小重新加权。我们给出了一种方法，采用这种技术在拟合的加速失效时间边际模型聚类生存数据。此外，右删失处理的逆概率删失重新加权通过使用一个灵活的模型的删失风险。由此产生的方法进行检查，通过一个彻底的模拟研究。还提供了使用真实的数据集的说明性示例，其检查登记时的年龄和吸烟对牙齿存活的影响。(C)2011 Elsevier B.V.保留所有权利。
Methods for analyzing clustered survival data are gaining popularity in biomedical research. Naive attempts to fitting marginal models to such data may lead to biased estimators and misleading inference when the size of a cluster is statistically correlated with some cluster specific latent factors or one or more cluster level covariates. A simple adjustment to correct for potentially informative cluster size is achieved through inverse cluster size reweighting. We give a methodology that incorporates this technique in fitting an accelerated failure time marginal model to clustered survival data. Furthermore, right censoring is handled by inverse probability of censoring reweighting through the use of a flexible model for the censoring hazard. The resulting methodology is examined through a thorough simulation study. Also an illustrative example using a real dataset is provided that examines the effects of age at enrollment and smoking on tooth survival. (C) 2011 Elsevier B.V. All rights reserved.