Comparative Effectiveness Research with Average Hazard for Censored Time-to-Event Outcomes: A Numerical Study

TL;DR

This paper explores the application of the average hazard metric in observational comparative effectiveness research, extending its use from randomized trials to non-randomized settings for better treatment effect assessment.

Contribution

It introduces new methods for applying the average hazard in observational studies, broadening its utility beyond randomized clinical trials.

Findings

Proposes approaches for using average hazard in non-randomized studies

Extends the utility of average hazard to observational comparative effectiveness research

Provides non-parametric estimation methods for average hazard differences and ratios

Abstract

The average hazard (AH), recently introduced by Uno and Horiguchi, represents a novel summary metric of event time distributions, conceptualized as the general censoring-free average person-time incidence rate on a given time window, $[0,\tau].$ This metric is calculated as the ratio of the cumulative incidence probability at $\tau$ to the restricted mean survival time at $\tau$ and can be estimated through non-parametric methods. The AH's difference and ratio present viable alternatives to the traditional Cox's hazard ratio for quantifying the treatment effect on time-to-event outcomes in comparative clinical studies. While the methodology for evaluating the difference and ratio of AH in randomized clinical trials has been previously proposed, the application of the AH-based approach in general comparative effectiveness research (CER), where interventions are not randomly allocated, remains underdiscussed. This paper aims to introduce several approaches for applying the AH in general CER, thereby extending its utility beyond randomized trial settings to observational studies where treatment assignment is non-random.