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Bayesian nonparametric nonproportional hazards survival modeling.

机译:贝叶斯非参数非比例风险生存模型。

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摘要

We develop a dependent Dirichlet process model for survival analysis data. A major feature of the proposed approach is that there is no necessity for resulting survival curve estimates to satisfy the ubiquitous proportional hazards assumption. An illustration based on a cancer clinical trial is given, where survival probabilities for times early in the study are estimated to be lower for those on a high-dose treatment regimen than for those on the low dose treatment, while the reverse is true for later times, possibly due to the toxic effect of the high dose for those who are not as healthy at the beginning of the study.
机译:我们为生存分析数据开发了一个依赖的Dirichlet过程模型。所提出的方法的主要特点是,不必为得出普遍存在的比例危害假设而得出的生存曲线估计值。给出了一个基于癌症临床试验的例证,其中高剂量治疗方案的存活率估计比低剂量方案低,而后来的情况则相反。次,可能是由于高剂量对研究开始时不健康的人产生了毒性作用。

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