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Related Experiment Video

Updated: Jan 19, 2026

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A Multi-Center Competing Risks Model and Its Absolute Risk Calculation Approach.

Jintao Wang1,2, Zhongshang Yuan3, Yi Liu4

  • 1Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, China. wangjintao0214@sdu.edu.cn.

International Journal of Environmental Research and Public Health
|September 19, 2019
PubMed
Summary

A new multi-center competing risks model (MCCRM) effectively handles survival data from multiple centers, outperforming the cause-specific hazard model (CSHM) when spatial heterogeneity is present. The MCCRM offers unbiased estimates and better predictive accuracy, especially with significant center variation.

Keywords:
absolute riskarea under the curvecompeting riskmulti-centerrisk assessment

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Cause-specific hazard models (CSHM) analyze time-to-event data but struggle with unobserved heterogeneity in multi-center studies.
  • Latent factors across different centers can lead to significant variation in survival data, challenging existing models.

Purpose of the Study:

  • To propose a novel multi-center competing risks model (MCCRM) for analyzing survival data from multi-center studies.
  • To compare the performance of MCCRM against the traditional CSHM using simulation studies.
  • To introduce a method for calculating absolute risk and assess model calibration.

Main Methods:

  • Developed a multi-center competing risks model (MCCRM) incorporating a center parameter to address spatial heterogeneity.
  • Conducted simulation studies to compare MCCRM with CSHM, evaluating coefficient estimates and Area Under the Curve (AUC).
  • Assessed model calibration by comparing expected (E) and observed (O) event numbers (e.g., strokes).

Main Results:

  • MCCRM provides unbiased and precise coefficient estimates, outperforming CSHM in AUC when heterogeneity is significant.
  • The superiority of MCCRM over CSHM increases with the standard deviation of the center parameter (SDCP).
  • MCCRM and CSHM performance is similar when SDCP < 0.1; MCCRM is significantly better when SDCP ≥ 0.1.

Conclusions:

  • The proposed MCCRM effectively accounts for spatial heterogeneity in multi-center survival data.
  • The standard deviation of the center parameter (SDCP) can guide the selection between MCCRM and CSHM.
  • MCCRM is the recommended model for multi-center studies with substantial between-center variation.