论文标题
功能性添加剂模型以优化个性化治疗规则
Functional additive models for optimizing individualized treatment rules
论文作者
论文摘要
提出了一种新型的功能添加剂模型，该模型是唯一修改的，并将其限制在治疗指标与潜在的功能和/或标量预处理协变量之间的非线性相互作用之间。这种方法的主要动机是根据随机临床试验的数据优化个性化的治疗规则。我们通过将特异性组件纳入添加剂效应组件中来概括功能添加剂回归模型。为了提供一类具有主要效果和相互作用效应的添加剂模型，对特定治疗的组件施加了结构性约束。如果主要利益是治疗与协变量之间的相互作用，那么在优化个性化的治疗规则时通常情况，我们可以规避估计协变量的主要影响的需求，从而消除了指定其形式的需求，从而避免了模型错误指定的问题。该方法用抑郁临床试验的数据和脑电图功能数据作为患者的预处理协变量进行了说明。
A novel functional additive model is proposed which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment-specific components into additive effect components. A structural constraint is imposed on the treatment-specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates.
