Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context
Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context
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DOI：
10.1002/sim.7266
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发表时间：
2017-06-15
影响因子：
2
通讯作者：
Platt, Robert W.
中科院分区：
文献类型：
--
作者：
Karim, Mohammad Ehsanul;Platt, Robert W.
从边际结构考克斯模型（MSCM）中进行一致性推断，需要对逆概率加权（IPW）模型进行正确的规范。在实际应用中，研究人员通常不知道权重模型的真实规格。尽管如此，IPW通常使用参数模型进行估计，例如主效应逻辑回归模型。在实践中，这些模型的假设可能不成立，数据自适应统计学习方法可能提供一种替代方法。在文献中有许多候选的统计学习方法。然而，给定数据集的最佳方法是不可能预测的。超级学习者（SL）已被提出作为一种工具，选择一个最佳的学习者从一组候选人使用交叉验证。在这项研究中，我们评估的有用性SL估计IPW在四个不同的MSCM模拟方案，其中我们改变了规格的真重模型规格（线性和/或添加剂）。我们的模拟表明，在权重模型错误指定的存在下，具有丰富多样的候选算法集，SL通常可以在MSE以及MSCM中估计效果的覆盖概率方面提供比常用统计学习方法更好的替代方案。模拟研究的结果指导了MSCM在加拿大不列颠哥伦比亚省多发性硬化队列中的应用（1995-2008），以评估β-干扰素治疗对延迟残疾进展的影响。版权所有（C）2017约翰威利父子有限公司
Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995-2008), to estimate the impact of beta-interferon treatment in delaying disability progression. Copyright (C) 2017 John Wiley & Sons, Ltd.