Wallace Michael P, Moodie Erica E M, Stephens David A
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
Biometrics. 2019 Dec;75(4):1205-1215. doi: 10.1111/biom.13104. Epub 2019 Sep 12.
Dynamic treatment regimes (DTRs) aim to formalize personalized medicine by tailoring treatment decisions to individual patient characteristics. G-estimation for DTR identification targets the parameters of a structural nested mean model, known as the blip function, from which the optimal DTR is derived. Despite its potential, G-estimation has not seen widespread use in the literature, owing in part to its often complex presentation and implementation, but also due to the necessity for correct specification of the blip. Using a quadratic approximation approach inspired by iteratively reweighted least squares, we derive a quasi-likelihood function for G-estimation within the DTR framework, and show how it can be used to form an information criterion for blip model selection. We outline the theoretical properties of this model selection criterion and demonstrate its application in a variety of simulation studies as well as in data from the Sequenced Treatment Alternatives to Relieve Depression study.
动态治疗方案（DTRs）旨在通过根据个体患者特征定制治疗决策，将个性化医疗形式化。用于DTR识别的G估计针对的是一个结构嵌套均值模型的参数，即所谓的“脉冲函数”，由此可推导出最优DTR。尽管G估计有其潜力，但在文献中尚未得到广泛应用，部分原因在于其表述和实施往往较为复杂，还因为需要正确设定脉冲函数。我们采用受迭代重加权最小二乘法启发的二次近似方法，在DTR框架内推导出用于G估计的拟似然函数，并展示如何用它来形成一个用于脉冲模型选择的信息准则。我们概述了该模型选择准则的理论性质，并在各种模拟研究以及“缓解抑郁症的序贯治疗替代方案”研究的数据中展示了其应用。
