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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Optimal designs for discrete-time survival models with competing risks.

XiaoDong Zhou1, YunJuan Wang2, RongXian Yue3

  • 1School of Statistics and Data Science, Shanghai University of International Business and Economics, Shanghai, 201620, China.

Lifetime Data Analysis
|February 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces optimal designs for randomized survival trials with multiple competing events. Equal group allocation is generally best for discrete-time-to-event trials with competing risks.

Keywords:
Competing risksDiscrete-time survival modelLongitudinal studyParametric competing risks modelTime-varying treatment effects.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Randomized controlled trial (RCT) design research often overlooks multiple competing endpoints.
  • Many clinical trials face challenges with multiple target events, lacking optimal design strategies.
  • Existing statistical literature has not systematically addressed optimal designs for survival trials with competing events.

Purpose of the Study:

  • To develop design methodologies for randomized discrete-time-to-event trials with competing endpoints.
  • To address the gap in optimal design strategies for trials with multiple competing target events.
  • To identify optimal designs for estimating treatment effects in such complex trial settings.

Main Methods:

  • Derived the Fisher information matrix for the discrete-time survival model (DTSM) by transforming data into multinomial responses.
  • Introduced a cost-based generalized optimal design criterion to identify optimal designs.
  • Assumed a parametric competing risks model for the underlying survival process.

Main Results:

  • Optimal treatment allocation schemes are significantly influenced by parameter values in the competing risks model.
  • Demonstrated that equal subject allocation is generally favorable in two-arm DTSM trials with competing risks.
  • Identified exceptions where equal allocation may not be optimal, specifically when hazard rates are low.

Conclusions:

  • The developed methodology provides optimal design strategies for discrete-time-to-event trials with competing endpoints.
  • The findings offer practical guidance for designing clinical trials with multiple, competing risks.
  • The study highlights the importance of considering competing events in trial design and allocation strategies.