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04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Estimation in a competing risks proportional hazards model under length-biased sampling with censoring
Jean-Yves Dauxois1, Agathe Guilloux, Syed N U A Kirmani
1Université de Toulouse-INSA, IMT, UMR CNRS 5219, 135, Avenue de Rangueil, 31077 , Toulouse cedex 4, France, jean-yves.dauxois@insa-toulouse.fr.
Lifetime Data Analysis
|March 5, 2013
Summary
This study addresses survival data challenges from length-biased samples in duration studies. It develops estimators for survivor functions with competing risks and censoring, crucial for accurate population analysis.
Area of Science:
- Statistics
- Survival Analysis
- Biostatistics
Background:
- Estimating survivor functions requires understanding the sampled population.
- Cross-sectional samples from stationary populations yield length-biased data (t f(t)), not the target density f(t).
- Length-biased data complicates survival analysis, especially with competing risks and censoring.
Purpose of the Study:
- To develop nonparametric estimators for survivor functions from length-biased samples.
- To account for two independent competing risks with proportional hazards.
- To address scenarios with and without independent censoring prior to length-biased sampling.
Main Methods:
- Utilizing a sampling scheme based on a mixed Poisson process.
- Developing nonparametric estimators for the target population's survivor function.
- Proving the weak convergence of the proposed estimator process for both considered cases.
Main Results:
- Nonparametric estimators for survivor functions were developed for length-biased samples.
- The estimators account for competing risks and censoring.
- Weak convergence of the estimator process was demonstrated.
Conclusions:
- The proposed methods provide valid estimators for survivor functions in the presence of length-biased sampling, competing risks, and censoring.
- The study offers a framework for analyzing duration data from complex sampling schemes.
- Illustrative examples and simulation studies support the practical application and finite sample behavior of the estimators.
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