Repeated events survival models: The conditional frailty model
Repeated events survival models: The conditional frailty model
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DOI：
10.1002/sim.2434
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发表时间：
2006-10-30
影响因子：
2
通讯作者：
De Boef, Suzanna
中科院分区：
文献类型：
--
作者：
Box-Steffensmeier, Janet M.;De Boef, Suzanna
重复事件过程在重要的健康、医疗和公共政策应用中无处不在，但这些过程的模型存在严重的局限性。由于偏倚和低效率，替代估计量经常产生关于治疗效果的不同推断。我们建议一个强大的策略，估计在医疗，社会条件，个人行为和公共政策计划的影响，在重复事件生存模型在三个常见的条件下：异质性在个人之间，依赖于事件的数量，异质性和事件依赖。我们比较了几种模型分析经常性的事件数据，表现出异质性和事件依赖性。条件脆弱性模型最好的帐户的异质性和事件依赖性的各种条件，通过使用脆弱性的长期，分层和间隙时间制定的风险集。我们使用Monte Carlo模拟研究了应用工作中常用的复发事件模型的性能，并将研究结果应用于慢性肉芽肿性疾病和囊性纤维化的数据。版权所有（c）2005年约翰威利父子有限公司。
Repeated events processes are ubiquitous across a great range of important health, medical, and public policy applications, but models for these processes have serious limitations. Alternative estimators often produce different inferences concerning treatment effects due to bias and inefficiency. We recommend a robust strategy for the estimation of effects in medical treatments, social conditions, individual behaviours, and public policy programs in repeated events survival models under three common conditions: heterogeneity across individuals, dependence across the number of events, and both heterogeneity and event dependence. We compare several models for analysing recurrent event data that exhibit both heterogeneity and event dependence. The conditional frailty model best accounts for the various conditions of heterogeneity and event dependence by using a frailty term, stratification, and gap time formulation of the risk set. We examine the performance of recurrent event models that are commonly used in applied work using Monte Carlo simulations, and apply the findings to data on chronic granulomatous disease and cystic fibrosis. Copyright (c) 2005 John Wiley & Sons, Ltd.