Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.
Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.
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DOI：
10.1002/hec.3189
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发表时间：
2015-09
期刊：
影响因子：
2.1
通讯作者：
Harrison D
中科院分区：
文献类型：
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作者：
Kreif N;Grieve R;Díaz I;Harrison D
对于连续治疗，广义倾向评分 (GPS) 定义为给定协变量的治疗的条件密度。 GPS 调整可以通过将其作为协变量包含在结果回归中来实现。此处，剂量反应函数的无偏估计假设 GPS 和结果治疗关系均正确指定。本文介绍了一种机器学习方法，即“超级学习器”，以解决这种情况下的模型选择问题。在提出的两阶段估计方法中，超级学习器选择 GPS，然后选择以 GPS 为条件的剂量反应函数，作为候选预测算法的凸组合。我们将这种方法与 GPS 的参数化实现和回归方法进行比较。我们对比了神经重症监护队列研究中的风险调整中的方法，其中我们估计了增加从急诊科转移到专门的神经科学中心的时间对急性创伤性脑损伤患者的边际影响。通过结果的参数模型，我们发现剂量反应曲线根据规格的选择而不同。通过超级学习者方法的回归和 GPS，我们发现传输时间对结果没有统计上显着的边际影响。 © 2015 作者。卫生经济学由 John Wiley & Sons Ltd 出版。
For a continuous treatment, the generalised propensity score (GPS) is defined as the conditional density of the treatment, given covariates. GPS adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose–response function assumes correct specification of both the GPS and the outcome‐treatment relationship. This paper introduces a machine learning method, the ‘Super Learner’, to address model selection in this context. In the two‐stage estimation approach proposed, the Super Learner selects a GPS and then a dose–response function conditional on the GPS, as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the GPS and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for patients with acute traumatic brain injury. With parametric models for the outcome, we find that dose–response curves differ according to choice of specification. With the Super Learner approach to both regression and the GPS, we find that transfer time does not have a statistically significant marginal effect on the outcomes. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd.