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04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Joint Modeling of Longitudinal and Cure-survival Data
Sehee Kim1, Donglin Zeng, Yi Li
1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
Journal of Statistical Theory and Practice
|August 9, 2013
Summary
This study introduces new statistical models for analyzing patient survival data alongside repeated health measurements, accounting for individuals who may never develop the disease (cure fraction). The method uses nonparametric maximum likelihood estimators (NPMLE) for accurate parameter estimation.
Area of Science:
- Biostatistics
- Survival Analysis
- Longitudinal Data Analysis
Background:
- Analyzing longitudinal measurements and survival data simultaneously is complex.
- Existing models may not adequately address the 'cure fraction' where some subjects are assumed to never experience the event.
Purpose of the Study:
- To develop semiparametric joint models for longitudinal data and survival data with a cure fraction.
- To propose a flexible class of transformations for the cure-survival model.
- To provide efficient estimation methods.
Main Methods:
- Utilized semiparametric joint models incorporating a cure fraction.
- Employed a broad class of transformations for the cure-survival model, including proportional hazards and proportional odds structures.
- Proposed nonparametric maximum likelihood estimators (NPMLE) and implemented using EM algorithms.
Main Results:
- The proposed estimators are asymptotically normal and semiparametrically efficient.
- Demonstrated good performance through extensive simulation studies.
- Validated the method with a real-data application.
Conclusions:
- The developed semiparametric joint models offer a robust framework for analyzing complex survival and longitudinal data.
- The NPMLE with EM algorithms provide an efficient and reliable inference procedure.
- The method is effective in handling data with a cure fraction.
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