The widely acknowledged homogeneity assumption limits progress in refining clinical diagnosis, understanding mechanisms, and developing new treatments for mental health disorders. This homogeneity assumption drives both a comorbidity and a heterogeneity problem, where two different approaches tackle the problems. One, a unifying approach, tackles the comorbidity problem by assuming that a single general psychopathology factor underlies multiple disorders. Another, a multifactorial approach, tackles the heterogeneity problem by assuming that disorders comprise multiple subtypes driven by multiple discrete factors. We show how each of these approaches can make useful contributions to mental health-related research and clinical practice. For example, the unifying approach can develop a rapid assessment tool that may be clinically valuable for triaging cases. The multifactorial approach can reveal subtypes that are differentially responsive to treatments and highlight distinct mechanisms leading to similar phenotypes. Because both approaches tackle different problems, both have different limitations. We describe the statistical frameworks that incorporate and adjudicate between both approaches (e.g., the bifactor model, normative modeling, and the functional random forest). Such frameworks can identify whether sets of disorders are more affected by heterogeneity or comorbidity. Therefore, future studies that incorporate such frameworks can provide further insight into the nature of psychopathology.

中文翻译：

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评估异质性的方法和挑战


广泛认可的同质性假设限制了改进临床诊断、理解机制和开发精神健康疾病新疗法的进展。这种同质性假设导致了共病和异质性问题，其中有两种不同的方法可以解决这些问题。其中一种是统一的方法，通过假设单一的一般精神病理学因素是多种疾病的基础来解决共病问题。另一种方法是多因素方法，通过假设疾病包含由多个离散因素驱动的多种亚型来解决异质性问题。我们展示了这些方法如何为心理健康相关的研究和临床实践做出有益的贡献。例如，统一方法可以开发一种快速评估工具，该工具对于病例分类可能具有临床价值。多因素方法可以揭示对治疗有不同反应的亚型，并突出导致相似表型的不同机制。由于这两种方法解决不同的问题，因此都有不同的局限性。我们描述了合并和裁决两种方法的统计框架（例如，双因子模型、规范建模和功能随机森林）。这样的框架可以确定一组疾病是否更容易受到异质性或共病的影响。因此，纳入此类框架的未来研究可以进一步深入了解精神病理学的本质。