Conditional Transformation Models for Survivor Function Estimation
Conditional Transformation Models for Survivor Function Estimation
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DOI：
10.1515/ijb-2014-0006
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Lisa Möst;T. Hothorn
中科院分区：
文献类型：
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作者：
Lisa Möst;T. Hothorn
摘要在生存分析中，以患者特征为条件的患者特异性生存函数的估计是特别感兴趣的。一般而言，了解患者在所有相关时间点的条件生存概率，可以比汇总统计（如中位生存时间）更好地评估患者的风险。然而，分析生存数据的标准方法很少直接估计生存函数。因此，我们提出了应用条件转换模型（CTM）的生存时间的条件分布函数的估计给定的一组患者的特征。我们使用截尾加权方法的逆概率来解释右截尾观测。我们提出的建模方法允许预测患者特定的幸存者功能。此外，CTM构成了一个灵活的模型类，能够处理成比例以及非成比例的风险。著名的考克斯模型作为一种特殊情况被包括在CTM类中。我们在一个模拟中研究了CTM在生存数据分析中的性能，该模拟包括比例和非比例风险设置以及解释变量的不同场景。此外，我们重新分析了慢性粒细胞白血病患者的生存时间，并研究了比例风险假设对先前发表的结果的影响。
Abstract In survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patient’s risk than summary statistics, such as median survival time. Nevertheless, standard methods for analysing survival data seldom estimate the survivor function directly. Therefore, we propose the application of conditional transformation models (CTMs) for the estimation of the conditional distribution function of survival times given a set of patient characteristics. We used the inverse probability of censoring weighting approach to account for right-censored observations. Our proposed modelling approach allows the prediction of patient-specific survivor functions. In addition, CTMs constitute a flexible model class that is able to deal with proportional as well as non-proportional hazards. The well-known Cox model is included in the class of CTMs as a special case. We investigated the performance of CTMs in survival data analysis in a simulation that included proportional and non-proportional hazard settings and different scenarios of explanatory variables. Furthermore, we re-analysed the survival times of patients suffering from chronic myelogenous leukaemia and studied the impact of the proportional hazards assumption on previously published results.