Nonparametric survival analysis using Bayesian Additive Regression Trees (BART).
Nonparametric survival analysis using Bayesian Additive Regression Trees (BART).
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DOI：
10.1002/sim.6893
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发表时间：
2016-07-20
影响因子：
2
通讯作者：
Laud PW
中科院分区：
文献类型：
--
作者：
Sparapani RA;Logan BR;McCulloch RE;Laud PW
贝叶斯加性回归树（BART）为协变量与结果之间的关系提供了一个灵活的非参数建模框架。最近，BART模型已被证明对连续和二元结果都提供了出色的预测性能，并且超过了其竞争对手。软件也很容易得到这样的结果。在本文中，我们介绍了通过解决生存分析中出现的需求来扩展BART在医疗应用中的有用性的建模。通过对单样本和双样本场景的仿真研究，与长期存在的传统方法进行比较，验证了新方法的表面有效性。然后，我们通过对具有交叉生存函数的非比例风险场景的模拟研究，以及在风险被协变量的高度非线性函数相乘修改的场景中的生存函数估计，证明了该模型能够容纳来自复杂回归模型的数据。利用最近发表的一项关于接受造血干细胞移植的患者的研究数据，我们说明了该方法在医学调查中的使用和一些优点。
Bayesian additive regression trees (BART) provide a framework for flexible nonparametric modeling of relationships of covariates to outcomes. Recently, BART models have been shown to provide excellent predictive performance, for both continuous and binary outcomes, and exceeding that of its competitors. Software is also readily available for such outcomes. In this article we introduce modeling that extends the usefulness of BART in medical applications by addressing needs arising in survival analysis. Simulation studies of one-sample and two-sample scenarios, in comparison with long-standing traditional methods, establish face validity of the new approach. We then demonstrate the model’s ability to accommodate data from complex regression models with a simulation study of a nonproportional hazards scenario with crossing survival functions, and survival function estimation in a scenario where hazards are multiplicatively modified by a highly nonlinear function of the covariates. Using data from a recently published study of patients undergoing hematopoietic stem cell transplantation, we illustrate the use and some advantages of the proposed method in medical investigations.