Joint Inference of Competing Risks Data Using Multiple Endpoints

• Activity Number: 419 - Section on Risk Analysis Student Paper Award Session
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Jiyang Wen* and Chen Hu and Mei-Cheng Wang
• Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins School of Medicine and Johns Hopkins Bloomberg School of Public Health
• Keywords: clinical trial; competing risks; COVID-19; cumulative incidence; multiple endpoints; restricted mean time

Abstract

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations. Sometimes more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be opposite to each other. In this paper we develop estimation and joint inferential methods for multiple cumulative inference functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from hospital, two competing events with completely different clinical implications.