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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Semi-parametric hybrid empirical likelihood inference for two-sample comparison with censored data
Haiyan Su1, Mai Zhou, Hua Liang
1Department of Mathematical Sciences, Montclair State University, Montclair, NJ 07043, USA. suh@mail.montclair.edu
Lifetime Data Analysis
|March 15, 2011
Summary
This study introduces a novel empirical likelihood method for comparing censored data in semi-parametric hybrid models. The new approach offers improved efficiency over existing pseudo-empirical likelihood methods for two-sample comparisons.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Two-sample comparison problems are common in research, with a focus on semiparametric settings for censored data.
- Existing empirical likelihood methods for censored data may lack full efficiency.
Purpose of the Study:
- To develop a more efficient empirical likelihood-based inference for two-sample semi-parametric hybrid models with censored data.
- To address limitations in existing pseudo-empirical likelihood approaches.
Main Methods:
- Utilized a hazard formulation for censored data within a two-sample semi-parametric hybrid model framework.
- Developed a new empirical likelihood statistic.
Main Results:
- The proposed empirical likelihood statistic converges to a standard chi-squared distribution under the null hypothesis.
- Demonstrated the method's applicability to testing ROC curves with censored data.
Conclusions:
- The new empirical likelihood method provides a statistically sound and potentially more efficient approach for semiparametric two-sample comparisons with censored data.
- The method shows promise for various applications, including ROC curve analysis.
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