Abstract

Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient’s own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple data domains (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this article, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplementary materials for this article are available online.

 抽象的

个体化治疗规则（ITR）推荐根据每位患者自身特点专门定制的治疗方法。当存在许多特征时，尤其是当这些特征来自多个数据域（例如人口统计、临床测量、神经影像模式）时，估计最佳 ITR 可能具有挑战性。考虑来自互补领域的数据并使用多种相似性度量来捕获特征和治疗之间潜在的复杂关系可以潜在地提高分配治疗的准确性。先前已开发出基于使用预定单核函数的支持向量机的结果加权学习 (OWL) 方法来估计最佳 ITR。在本文中，我们提出了一种估计最佳 ITR 的方法，通过利用多个核函数来描述 OWL 框架内数据域内和跨数据域的受试者之间特征的相似性，而不是预先选择用于所有特征的单个核函数对于所有域。我们的方法考虑了每个数据域的异构性，并最佳地组合多个数据域。我们的学习过程估计最佳 ITR，并确定对于确定 ITR 最重要的数据域。因此，这种方法可用于确定从多个域收集数据的优先顺序，从而可能在不牺牲准确性的情况下降低成本。我们的方法的相对优势通过模拟研究以及从多个数据域收集特征的重度抑郁症随机临床试验的应用得到了证明。本文的补充材料可在线获取。