Ginestet Cedric E, Emsley Richard, Landau Sabine
Biostatistics Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, U.K.
Centre for Biostatistics, Institute of Population Health, University of Manchester, Manchester, U.K.
Stat Med. 2017 May 20;36(11):1696-1714. doi: 10.1002/sim.7265. Epub 2017 Feb 21.
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.
一项心理健康试验使用剂量反应模型进行分析，在该模型中，患者参加的疗程数量被视为心理治疗剂量的指标。这里，感兴趣的参数是参与不同疗程数量的亚人群之间因果治疗效果的差异。对于这个数据集，随机治疗分配与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法（TSLS）估计量往往比普通最小二乘法（OLS）估计量有更小的偏差；但TSLS的方差更大。因此，将OLS和TSLS估计量的理想特性结合起来很有吸引力。这种权衡是通过以均方误差为标准对这两个估计量进行仿射组合来实现的。这就产生了Judge和Mittelhammer（2004年）引入的半参数斯坦因类（SPSL）估计量。SPSL估计量与链式方程多重填补法结合使用，以提供一个能够利用所有可用信息的估计量。还生成了模拟数据来说明SPSL估计量相对于OLS和TSLS估计量的优越性。一个名为SteinIV的实现这些方法的软件包已通过R平台提供。© 2017作者。《医学统计学》由John Wiley & Sons Ltd出版。
