Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees.
Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees.
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DOI：
10.1177/0962280217746191
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发表时间：
2019-04
影响因子：
2.3
通讯作者：
Laud PW
中科院分区：
文献类型：
--
作者：
Logan BR;Sparapani R;McCulloch RE;Laud PW
个体化治疗规则（ITR）可以通过认识到患者对治疗的反应可能不同，并为每个人分配最理想的预测结果来改善健康结果。灵活和有效的预测模型是理想的基础，这样的ITR处理患者因素和治疗之间的潜在复杂的相互作用。现代贝叶斯半参数和非参数回归模型在这方面提供了一个有吸引力的途径，因为这些模型允许对患者特定治疗决策的自然后验不确定性进行量化，以及基于预测的ITR的人群范围值。此外，通过使用这些模型，还可以对最佳ITR的值进行推断。我们提出了这样一种方法，并使用贝叶斯加性回归树（BART）实现它，因为该模型已被证明在将非参数回归函数拟合到连续和二进制响应时表现良好，即使有许多协变量。它在实际应用中也具有计算效率。BART研究了一种治疗策略，该策略利用BART模型对患者结局进行个性化预测。每种治疗下患者结局的后验分布用于分配最大化预期后验效用的治疗。我们还描述了如何近似这样的治疗政策与临床上可解释的ITR，并量化其预期的结果。与现有的几种方法相比，所提出的方法在广泛的仿真研究中表现得非常好。我们说明了使用所提出的方法来确定接受造血细胞移植的患者的预处理方案的个性化选择，并量化这种选择方法的最佳ITR以及非个性化治疗策略的价值。
Individualized treatment rules (ITR) can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such ITRs to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based ITR. In addition, via the use of such models, inference is also available for the value of the Optimal ITR. We propose such an approach and implement it using Bayesian Additive Regression Trees (BART) as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With BART we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from BART models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable ITR, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the Optimal ITR as well as non-individualized treatment strategies.
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