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Maximum approximate Bernstein likelihood estimation in proportional hazard model for interval-censored data.

Zhong Guan1

  • 1Department of Mathematical Sciences, Indiana University South Bend, South Bend, Indiana, USA.

Statistics in Medicine
|November 24, 2020
PubMed
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.

Keywords:
Bernstein polynomial modelCox's proportional hazard regression modelapproximate likelihooddensity estimationinterval censoringmixture beta modelsurvival curve

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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.