Navigating the Landscape of Hierarchical Multi-Component Strategies: GPC, DOOR, and MOST

TL;DR

This paper compares three hierarchical multi-component statistical methods—GPC, DOOR, and MOST—highlighting their differences and providing guidance for their application in drug development involving patient perspectives.

Contribution

It offers a comprehensive analysis and comparison of GPC, DOOR, and MOST, clarifying their structural and philosophical distinctions.

Findings

GPC and DOOR are influential frameworks for treatment effect evaluation.

MOST is a new methodology not explicitly linked to GPC or DOOR.

The paper provides guidance and encourages further research in hierarchical multi-component methods.

Abstract

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.