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Updated: Dec 12, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Semiparametric mixture cure model analysis with competing risks data: Application to vascular access thrombosis data.

Chyong-Mei Chen1, Pao-Sheng Shen2, Chih-Ching Lin3,4

  • 1Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C.

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Summary

This study introduces a new statistical model for hemodialysis patients to predict the recurrence of vascular access thrombosis, accounting for competing risks and patient recovery. The method helps understand factors influencing thrombosis recurrence and cure rates in nephrology patients.

Keywords:
competing risks datacumulative incidence functioninverse probability censoring weightmixture cure modeltwo-stage estimation

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Area of Science:

  • Nephrology
  • Biostatistics
  • Epidemiology

Background:

  • Vascular access thrombosis is a complication for hemodialysis patients.
  • Thrombosis can recur after treatment, with different types and competing risks.
  • Existing models may not adequately address competing risks and patient cure fractions.

Purpose of the Study:

  • To develop a statistical mixture model for analyzing competing-risks data with a cure fraction.
  • To estimate the cumulative incidence probability of acute thrombosis recurrence after therapy.
  • To investigate factors affecting both cure rates and thrombosis recurrence in hemodialysis patients.

Main Methods:

  • A mixture model approach incorporating logistic regression for cure rates and semiparametric regression for cumulative incidence.
  • Utilized a two-stage method with inverse probability censoring weight techniques for estimation.
  • Developed tests for model adequacy and time-varying effects.

Main Results:

  • The proposed model effectively analyzes competing-risks data with cure fractions.
  • Inference was made on factors influencing cure rates and acute thrombosis recurrence.
  • Simulation studies and real-world data analysis demonstrated the method's utility.

Conclusions:

  • The novel mixture model provides a robust framework for understanding vascular access thrombosis recurrence in hemodialysis.
  • The method allows for separate analysis of cure rates and time-to-event data with competing risks.
  • This approach can inform clinical practice and future research in nephrology.