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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

Competing risks regression for stratified data.

Bingqing Zhou1, Aurelien Latouche, Vanderson Rocha

  • 1Division of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut 06520, USA. bingqing.zhou@yale.edu

Biometrics
|December 16, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a stratified competing risks regression model to address limitations of the Fine-Gray model when proportional hazards are not met. The new method provides reliable analysis for complex clinical trial data.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Trials

Background:

  • The Fine-Gray proportional hazards model is widely used for competing risks data.
  • Proportional hazards assumption is often violated in real-world applications like multicenter trials.
  • Varying baseline subdistribution hazards across strata complicate standard analysis.

Purpose of the Study:

  • To develop and evaluate a stratified competing risks regression model.
  • To relax the proportional hazards assumption in the Fine-Gray model.
  • To provide consistent estimators and asymptotic properties for regression parameters under stratification.

Main Methods:

  • A stratified competing risks regression framework is proposed.
  • Two stratification regimes are considered based on the number and size of strata.
  • Partial likelihood and weighting techniques are employed for estimation.
  • Asymptotic properties are derived for each regime.

Main Results:

  • Consistent estimators for regression parameters are obtained.
  • The stratified model accommodates varying baseline hazards across strata.
  • Demonstrated utility in breast cancer and bone marrow transplant data.

Conclusions:

  • The stratified Fine-Gray model offers a flexible approach for competing risks analysis.
  • It effectively handles situations where proportional hazards are violated.
  • The method enhances the analysis of complex clinical trial and registry data.