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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Non-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment.

Ruiqian Wu1, Ying Zhang1, Giorgos Bakoyannis2

  • 1Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE.

Statistics in Medicine
|January 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to analyze semi-competing risks data, especially when death records are incomplete. The method accurately assesses the impact of ART interruption on HIV mortality.

Keywords:
EM algorithmillness‐death modelmissing cause of failurenon‐parametric pseudo‐likelihood estimationsemi‐competing risks

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Semi-competing risks data models are crucial for understanding disease progression, linking intermediate events to terminal outcomes like death.
  • Existing computational methods for these models face numerical challenges, particularly with event misascertainment.
  • The Gamma-Frailty conditional Markov model offers an efficient approach for semi-competing risks analysis.

Purpose of the Study:

  • To develop a robust statistical method for analyzing semi-competing risks data with event misascertainment.
  • To evaluate the impact of interrupted Antiretroviral Therapy (ART) care on HIV mortality using a real-world cohort.
  • To demonstrate the validity and numerical stability of the proposed non-parametric pseudo-likelihood method.

Main Methods:

  • Proposed a non-parametric pseudo-likelihood method combined with an Expectation-Maximization (EM)-like algorithm.
  • Utilized a restricted Gamma-Frailty conditional Markov model framework.
  • Conducted a comprehensive simulation study to validate the method's performance and stability.
  • Applied the method to the large HIV cohort study, EA-IeDEA, which has significant death under-reporting.

Main Results:

  • The proposed method demonstrated valid inference and numerical stability in simulations.
  • The application to the EA-IeDEA cohort provided insights into the adverse effects of ART interruption on HIV mortality.
  • Quantified the impact of death under-reporting on the accuracy of survival analyses in HIV cohorts.

Conclusions:

  • The developed method effectively handles semi-competing risks data with event misascertainment.
  • The findings highlight the critical importance of accurate death ascertainment in epidemiological studies.
  • The study provides a valuable tool for public health research, particularly in understanding HIV disease progression and treatment impacts.