There is a growing recognition of the importance to involve patients in every stage of drug development. This shift acknowledges that patients' perspectives, experiences, and preferences are essential for ensuring that treatments meet real-world needs. In this context, a new body of statistical literature has emerged, focusing not only on the simultaneous consideration of multiple outcomes that reflect patients' overall experiences, but also on their structured prioritization. We refer to this class of approaches as hierarchical multi-component statistical methods. Among these, two influential frameworks - generalized pairwise comparisons (GPC) and desirability of outcome ranking (DOOR) - have emerged in the last decade, each aiming to offer a comprehensive approach to evaluating treatment effects. A new methodology, referred to here as the Markov ordinal state transition model (MOST), has recently been introduced without focusing on an explicit link with GPC nor DOOR. This paper seeks to fill this gap by offering a comprehensive and comparative analysis of the three approaches. Through examples and an exploration of the structural and philosophical differences between the methods, our aim is to provide guidance and encourage lines of research in the rapidly-evolving landscape of hierarchical multi-component statistical methodologies.

翻译：在药物开发的每个阶段纳入患者参与的重要性日益受到认可。这一转变承认患者的观点、经验和偏好对确保治疗方法满足现实需求至关重要。在此背景下，统计学文献中涌现出一类新的研究成果，不仅关注同时考量反映患者整体体验的多重结局，还注重这些结局的结构化优先级排序。我们将这类方法统称为层次化多组分统计方法。其中，两个具有影响力的框架——广义成对比较（GPC）和结局排序合意性（DOOR）——在过去十年中相继问世，两者均旨在提供评估治疗效应的综合性方法。近期提出的一项新方法论，即马尔可夫有序状态转换模型（MOST），并未明确建立与GPC或DOOR的关联。本文旨在填补这一空白，对这三种方法进行全面比较分析。通过实例探讨及方法间结构与哲学差异的剖析，本文旨在为快速演变的层次化多组分统计方法论格局提供指导，并激发相关研究方向的探索。