Dose-response modeling in mental health using stein-like estimators with instrumental variables.
Dose-response modeling in mental health using stein-like estimators with instrumental variables.
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DOI：
10.1002/sim.7265
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发表时间：
2017-05-20
影响因子：
2
通讯作者：
Landau S
中科院分区：
文献类型：
--
作者：
Ginestet CE;Emsley R;Landau S
使用剂量反应模型分析心理健康试验，其中患者参加的会议次数被认为是心理治疗剂量的指示。在这里，关注的参数是参与不同数量的治疗疗程的亚群之间的因果治疗效果的差异。对于该数据集，随机治疗分配和预后基线变量之间的相互作用提供了必要的工具变量。虽然相应的两阶段最小二乘（TSLS）估计量倾向于比普通最小二乘（OLS）估计量具有更小的偏差;但TSLS具有更大的方差。因此，联合收割机将OLS和TSLS估计量的理想性质结合起来是很有吸引力的。这种权衡是通过这两个估计量的仿射组合来实现的，使用均方误差作为标准。这产生了Judge和Mittelhammer（2004）介绍的半参数类斯坦（SPSL）估计量。SPSL估计器与多重插补和链式方程结合使用，以提供可以利用所有可用信息的估计器。模拟数据也产生说明的SPSL估计的优越性，其OLS和TSLS同行。一个名为SteinIV的实现这些方法的软件包已经通过R平台提供。版权所有© 2017作者.出版社：John Wiley & Sons Ltd
A mental health trial is analyzed using a dose–response model, in which the number of sessions attended by the patients is deemed indicative of the dose of psychotherapeutic treatment. Here, the parameter of interest is the difference in causal treatment effects between the subpopulations that take part in different numbers of therapy sessions. For this data set, interactions between random treatment allocation and prognostic baseline variables provide the requisite instrumental variables. While the corresponding two‐stage least squares (TSLS) estimator tends to have smaller bias than the ordinary least squares (OLS) estimator; the TSLS suffers from larger variance. It is therefore appealing to combine the desirable properties of the OLS and TSLS estimators. Such a trade‐off is achieved through an affine combination of these two estimators, using mean squared error as a criterion. This produces the semi‐parametric Stein‐like (SPSL) estimator as introduced by Judge and Mittelhammer (2004). The SPSL estimator is used in conjunction with multiple imputation with chained equations, to provide an estimator that can exploit all available information. Simulated data are also generated to illustrate the superiority of the SPSL estimator over its OLS and TSLS counterparts. A package entitled SteinIV implementing these methods has been made available through the R platform. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.