Survival Models on Unobserved Heterogeneity and their Applications in Analyzing Large-scale Survey Data
Survival Models on Unobserved Heterogeneity and their Applications in Analyzing Large-scale Survey Data
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DOI：
10.4172/2155-6180.1000191
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
X. Liu
中科院分区：
文献类型：
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作者：
X. Liu
在生存分析中，研究人员经常遇到多变量生存时间数据，其中失败时间相关，即使在模型协变量的存在。有人认为，因为观察聚集未观察到的异质性，标准生存模型的应用可能会导致有偏的参数估计和错误的基于模型的预测。在这篇文章中，作者描述和比较四种方法处理未观察到的异质性的生存分析：Andersen-Gill方法，稳健的三明治方差估计，风险模型与个人脆弱性，和再转换方法。实证分析提供了强有力的证据，即在存在强的未观察到的异质性的情况下，标准生存模型的应用可以产生同样稳健的参数估计值和似然比统计量，因为相应的模型添加了一个额外的随机效应参数。然而，在预测生存函数时，多变量生存时间数据的标准模型可能导致严重的预测偏差。再转换方法是有效的，以获得一个调整因子，正确预测的生存函数。
In survival analysis, researchers often encounter multivariate survival time data, in which failure times are correlated even in the presence of model covariates. It is argued that because observations are clustered by unobserved heterogeneity, the application of standard survival models can result in biased parameter estimates and erroneous model-based predictions. In this article, the author describes and compares four methods handling unobserved heterogeneity in survival analysis: the Andersen-Gill approach, the robust sandwich variance estimator, the hazard model with individual frailty, and the retransformation method. An empirical analysis provides strong evidence that in the presence of strong unobserved heterogeneity, the application of a standard survival model can yield equally robust parameter estimates and the likelihood ratio statistic as does a corresponding model adding an additional parameter for random effects. When predicting the survival function, however, a standard model on multivariate survival time data can result in serious prediction bias. The retransformation method is effective to derive an adjustment factor for correctly predicting the survival function.