Understanding heterogeneity in phenotypical characteristics, symptoms manifestations and response to treatment of subjects with psychiatric illnesses is a continuing challenge in mental health research. A long-standing goal of medical studies is to identify groups of subjects characterized with a particular trait or quality and to distinguish them from other subjects in a clinically relevant way. This paper develops and illustrates a novel approach to this problem based on a method of optimal-partitioning (clustering) of functional data. The proposed method allows for the simultaneous clustering of different populations (e.g., symptoms of drug and placebo treated patients) in order to identify prototypical outcome profiles that are distinct from one or the other treatment and outcome profiles common to the different treatments. The clustering results are used to discover potential treatment effect modifiers (i.e., moderators), in particular, moderators of specific drug effects and placebo response. A depression clinical trial is used to illustrate the method.

中文翻译：

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分层精神病学通过基于凸性的聚类与调节分析的应用

了解表型特征、症状表现和对精神疾病受试者治疗反应的异质性是心理健康研究中的一个持续挑战。医学研究的一个长期目标是确定具有特定特征或品质的受试者组，并以临床相关的方式将它们与其他受试者区分开来。本文基于功能数据的最佳分区（聚类）方法开发并说明了解决此问题的新方法。所提出的方法允许同时聚类不同的群体（例如，药物和安慰剂治疗患者的症状）以识别不同于一种或其他治疗的原型结果概况以及不同治疗共有的结果概况。聚类结果用于发现潜在的治疗效果调节剂（即调节剂），特别是特定药物作用和安慰剂反应的调节剂。抑郁症临床试验用于说明该方法。