# Health Utility Survival for Randomized Clinical Trials: Extensions and Statistical Properties

## TL;DR

This paper introduces and extends a composite endpoint called HUS, which combines survival and health utility to improve statistical power in clinical trials.

## Contribution

The paper proposes methodological extensions of HUS and derives the asymptotic distributions of its test statistics.

## Key findings

- HUS increases statistical power and reduces required sample size compared to standard survival endpoints.
- Simulation studies and real data applications show HUS is more efficient and feasible than alternative methods.
- The asymptotic properties of HUS test statistics are rigorously derived and validated.

## Abstract

Overall survival has been used as the primary endpoint for many randomized trials that aim to examine whether a new treatment is non‐inferior to the standard treatment or placebo control. When a new treatment is indeed non‐inferior in terms of survival, it may be important to assess other outcomes including health utility. However, analyzing health utility scores in a secondary analysis may have limited power since the primary objectives of the original study design may not include health utility. To comprehensively consider both survival and health utility, we developed a composite endpoint, HUS (Health Utility‐adjusted Survival), which combines both survival and utility. HUS has been shown to be able to increase statistical power and potentially reduce the required sample size compared to the standard overall survival endpoint. Nevertheless, the asymptotic properties of the test statistics of the HUS endpoint have yet to be fully established. Besides that, the standard version of HUS cannot be applied to or has limited performance in certain scenarios, where extensions are needed. In this manuscript, we propose various methodological extensions of HUS and derive the asymptotic distributions of the test statistics. By comprehensive simulation studies and a data application using retrospective data based on a translational patient cohort in Princess Margaret Cancer Centre, we demonstrate the better efficiency and feasibility of HUS compared to different methods.