Related Experiment Video
Updated: May 8, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.0K
Time-dependent prognostic accuracy measures for recurrent event data
R Dey1, D E Schaubel2, J A Hanley1
1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 0G3, Canada.
Biometrics
|December 26, 2024
Summary
This study introduces new methods to assess how well a biomarker predicts recurrent events, like repeated illnesses. These methods, using a specific statistical model, show good performance in simulations and are applied to cystic fibrosis patients.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Medical Biomarkers
Background:
- Recurrent events are common in clinical practice, necessitating models that account for multiple occurrences per patient.
- While recurrent event models using biomarkers exist, evaluating their prognostic accuracy remains underexplored.
Purpose of the Study:
- To propose novel measures for characterizing the prognostic accuracy of baseline biomarkers in the context of recurrent events.
- To assess the performance of these novel accuracy estimators.
Main Methods:
- Development of estimators based on a semiparametric frailty model.
- The model accounts for marker informativeness and unobserved patient heterogeneity.
- Investigation of asymptotic properties and simulation studies for finite sample performance.
Main Results:
- Proposed estimators demonstrate minimal bias and appropriate coverage in simulations.
- The methods are validated for their finite sample performance.
- The estimators are successfully applied to a real-world case study.
Conclusions:
- Novel measures for prognostic accuracy in recurrent event settings are introduced.
- The proposed estimators are statistically sound and perform well.
- The methodology is applicable for evaluating biomarkers like lung function in chronic diseases such as cystic fibrosis.
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