An IPCW Adjusted Win Statistics Approach in Clinical Trials Incorporating Equivalence Margins to Define Ties

TL;DR

This paper introduces a new IPCW-adjusted win statistic method for time-to-event data in clinical trials, incorporating equivalence margins to better handle ties and censoring, with demonstrated effectiveness through simulations and real data.

Contribution

It proposes a novel class of win statistics for time-to-event endpoints that account for equivalence margins and right-censoring, with new estimation and inference procedures.

Findings

The proposed method effectively handles ties and censoring in clinical trial data.

Simulation studies show accurate estimation and inference properties.

Application to a real oncology trial demonstrates practical utility.

Abstract

In clinical trials, multiple outcomes of different priorities commonly occur as the patient's response may not be adequately characterized by a single outcome. Win statistics are appealing summary measures for between-group difference at more than one endpoint. When defining the result of pairwise comparisons of a time-to-event endpoint, it is desirable to allow ties to account for incomplete follow-up and not clinically meaningful difference in endpoints of interest. In this paper, we propose a class of win statistics for time-to-event endpoints with a user-specified equivalence margin. These win statistics are identifiable in the presence of right-censoring and do not depend on the censoring distribution. We then develop estimation and inference procedures for the proposed win statistics based on inverse-probability-of-censoring {weighting} (IPCW) adjustment to handle right-censoring. We conduct extensive simulations to investigate the operational characteristics of the proposed procedure in the finite sample setting. A real oncology trial is used to illustrate the proposed approach.