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首页> 外文期刊>Journal of the royal statistical society >Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown
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Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown

机译：当治疗效果的调节者未知时，基于标量和功能预测器构造治疗决策规则

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摘要

Treatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To understand this heterogeneity better and to help in determining the best patient-specific treatments for a given disease, many clinical trials are collecting large amounts of patient level data before administering treatment in the hope that some of these data can be used to identify moderators of treatment effect. These data can range from simple scalar values to complex functional data such as curves or images. Combining these various types of baseline data to discover'biosignatures' of treatment response is crucial for advancing precision medicine. Motivated by the problem of selecting optimal treatment for subjects with depression based on clinical and neuroimaging data, we present an approach that both identifies covari-ates associated with differential treatment effect and estimates a treatment decision rule based on these covariates. We focus on settings where there is a potentially large collection of candidate biomarkers consisting of both scalar and functional data. The validity of the approach proposed is justified via extensive simulation experiments and illustrated by using data from a placebo-controlled clinical trial investigating antidepressant treatment response in subjects with depression.
机译：治疗反应的异质性为选择多种疾病的治疗提出了严峻的挑战。为了更好地理解这种异质性并帮助确定特定疾病的最佳患者特异性治疗方法，许多临床试验在进行治疗前收集了大量患者水平的数据，希望其中一些数据可用于确定治疗的主持人治疗效果。这些数据的范围从简单的标量值到复杂的功能数据（例如曲线或图像）。结合这些各种类型的基准数据以发现治疗反应的“生物特征”对于推进精密医学至关重要。基于根据临床和神经影像数据为抑郁症患者选择最佳治疗的问题，我们提出了一种方法，该方法既可以识别与差异治疗效果相关的协变量，又可以根据这些协变量估算治疗决策规则。我们着重于可能包含大量标量和功能数据的候选生物标志物集合的设置。通过广泛的模拟实验证明了所提出方法的有效性，并通过使用安慰剂对照临床试验研究抑郁症患者抗抑郁治疗反应的数据进行了说明。

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