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Related Experiment Video

Updated: Jan 23, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Dynamic regression with recurrent events.

J E Soh1, Yijian Huang1

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia.

Biometrics
|June 22, 2019
PubMed
Summary

This study introduces a dynamic regression model for recurrent events, addressing unrealistic constant covariate effects. The new method accurately targets event frequency, showing promise in real-world applications.

Keywords:
counting processevent history analysismarginal modelingmultiplier bootstrapmultivariate survival datavarying-effects model

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Recurrent events are common in longitudinal follow-up studies.
  • Traditional regression models often assume static covariate effects, which may not reflect reality.
  • Time-varying covariate effects are crucial for accurate modeling of recurrent events.

Purpose of the Study:

  • To develop a dynamic regression model for analyzing recurrent events.
  • To address the limitation of constant covariate effects in existing models.
  • To accurately model the mean frequency of recurrent events over time.

Main Methods:

  • Developed a dynamic regression model tailored for recurrent event data.
  • Proposed an estimation procedure that utilizes all observed data.
  • Established theoretical properties including consistency and weak convergence of the estimator.

Main Results:

  • Simulation studies confirmed the effectiveness of the proposed dynamic regression method.
  • The method demonstrated good performance in analyzing recurrent event data.
  • Real-world data analyses illustrated the practical applicability of the model.

Conclusions:

  • The dynamic regression model effectively handles time-varying covariate effects in recurrent event analysis.
  • The proposed estimation procedure is statistically sound and practically useful.
  • This approach offers a more realistic and accurate way to analyze recurrent event data in follow-up studies.