Simultaneous hypothesis testing for multiple competing risks in comparative clinical trials

Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are han...

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Bibliographic Details
Published in:Statistics in medicine 2023-06, Vol.42 (14), p.2394-2408
Main Authors: Wen, Jiyang, Wang, Mei‐Cheng, Hu, Chen
Format: Article
Language:English
Subjects:
Clinical trials
competing risks
COVID-19
cumulative incidence
Humans
Hypothesis testing
multiple endpoints
Proportional Hazards Models
Research Design
restricted mean time
Risk Factors
Survival Analysis
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Summary:Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID‐19 in‐patient treatment trials, the outcomes of COVID‐19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID‐19 in‐patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.9728