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Maximum approximate Bernstein likelihood estimation in proportional hazard model for interval-censored data.
1Department of Mathematical Sciences, Indiana University South Bend, South Bend, Indiana, USA.
Statistics in Medicine
|November 24, 2020
Summary
New Bernstein likelihood methods provide faster, smoother survival function estimates for interval-censored data. This approach shows superior performance in simulations and real-world applications compared to existing methods.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Interval-censored data presents unique challenges in survival analysis.
- Existing methods for proportional hazard regression with interval-censored data have limitations in estimation rates.
Purpose of the Study:
- To introduce a novel method for estimating baseline density and regression coefficients in proportional hazard models.
- To improve the accuracy and convergence rate of survival function estimates for interval-censored data.
Main Methods:
- Utilizing maximum approximate Bernstein likelihood estimation.
- Applying the method to interval-censored event time data within proportional hazard regression models.
Main Results:
- Achieved smooth survival function estimates with an n1/2 -rate of convergence.
- Demonstrated superior finite sample performance compared to existing estimation methods via simulation.
- Successfully illustrated the method's application using real-world data examples.
Conclusions:
- The proposed Bernstein likelihood method offers significant improvements in estimating survival functions for interval-censored data.
- The method provides a more efficient and accurate approach for survival analysis in biostatistics and related fields.
Keywords:
Bernstein polynomial modelCox's proportional hazard regression modelapproximate likelihooddensity estimationinterval censoringmixture beta modelsurvival curveMore Related Videos
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