This review provides a systematic overview of methods that combine covariate-based clustering of observational units (patients) with outcome models for clinical studies. We distinguish between informed-cluster models, where the outcome contributes to cluster formation, and agnostic-cluster models, where clustering is performed solely on covariates in a separate first step. Informed-cluster models include product partition models with covariates (PPMx), finite mixtures of regression models (FMR), and cluster-aware supervised learning (CluSL). Agnostic-cluster models encompass two-step procedures using either model-based or algorithmic clustering followed by cluster-specific regression models. Following a systematic search of Web of Science and PubMed, 55 records were identified that propose or evaluate such models. We describe the key models, summarise study characteristics, and present applications from biomedical and public health research. Clustering-based outcome models are particularly relevant for settings with high-dimensional covariates (e.g., biomarker panels and "omics") and heterogeneous patient populations. These models can support risk stratification and we discuss extensions to estimate subgroup-specific treatment effects. They are most valuable when the population is clustered in distinct regions of the covariate space that correspond to different outcome distributions. We discuss applications to rare disease research, covariate adjustment and borrowing from historical data, and subgroup-specific treatment effect estimation in clinical trials.

翻译：本综述系统性地概述了将基于协变量的观察单位（患者）聚类与临床研究结局模型相结合的方法。我们区分了信息聚类模型（其中结局对聚类形成有贡献）和无信息聚类模型（其中聚类仅基于协变量在独立的第一步中完成）。信息聚类模型包括含协变量的乘积划分模型（PPMx）、回归模型的有限混合（FMR）以及聚类感知监督学习（CluSL）。无信息聚类模型涵盖使用基于模型或算法聚类后接特定聚类回归模型的两步流程。通过对Web of Science和PubMed的系统检索，共识别出55条提出或评估此类模型的文献记录。我们描述了关键模型，总结了研究特征，并展示了生物医学与公共卫生研究中的应用实例。基于聚类的结局模型特别适用于高维协变量（如生物标志物组合和"组学"数据）及异质性患者群体的研究场景。这些模型可支持风险分层，并讨论了其在估计亚组特异性治疗效果方面的扩展应用。当人群在协变量空间中聚类于对应不同结局分布的不同区域时，这些模型最具价值。我们探讨了其在罕见病研究、协变量调整与历史数据借用以及临床试验中亚组特异性治疗效果估计等方面的应用。