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Censored Median Regression and Profile Empirical Likelihood
1Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey,
Statistical Methodology
|December 30, 2008
Summary
We developed a profile empirical likelihood method for censored median regression. This approach provides reliable confidence intervals for key effects, validated using lung cancer data.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Censored median regression is crucial for analyzing time-to-event data.
- Traditional methods may face challenges with nuisance parameters and complex distributions.
Purpose of the Study:
- To implement profile empirical likelihood for censored median regression.
- To develop a robust method for inference on sub-vectors of parameters.
- To compare bootstrap-based critical values with existing methods.
Main Methods:
- Profile empirical likelihood ratio function.
- Asymptotic distribution analysis.
- Bootstrap resampling for critical values.
- Application to lung cancer data.
Main Results:
- The profile empirical likelihood method provides a viable approach for inference.
- Bootstrap critical values are effective for constructing confidence intervals.
- Confidence intervals for age and treatment effects were obtained for lung cancer data.
Conclusions:
- Profile empirical likelihood offers a flexible framework for censored median regression.
- The bootstrap method addresses the intractable covariance structure of the asymptotic distribution.
- The approach is practically demonstrated on a real-world medical dataset.
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