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. 2023 Jun 30;42(14):2394-2408.
doi: 10.1002/sim.9728. Epub 2023 Apr 10.

Simultaneous hypothesis testing for multiple competing risks in comparative clinical trials

Jiyang Wen  1 , Mei-Cheng Wang  1 , Chen Hu  1   2
Affiliations

Simultaneous hypothesis testing for multiple competing risks in comparative clinical trials

Jiyang Wen et al. Stat Med. .

Abstract

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.

Keywords: COVID-19; clinical trials; competing risks; cumulative incidence; multiple endpoints; restricted mean time.

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Figures

FIGURE 1
FIGURE 1
Restricted mean time gained (RMTG) and restricted mean time lost (RMTL).
FIGURE 2
FIGURE 2
Underlying cumulative incidences: Scenario I & II.
FIGURE 3
FIGURE 3
Type I error analysis of proposed tests.
FIGURE 4
FIGURE 4
Power analysis of proposed tests.
FIGURE 5
FIGURE 5
RMTG/RMTL plot in the two arms.
See this image and copyright information in PMC

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