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Weibull regression for lifetimes measured with error.

C J Skinner1, K Humphreys

  • 1University of Southampton, UK.

Lifetime Data Analysis
|April 24, 1999
PubMed
Summary
This summary is machine-generated.

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This study introduces adjusted estimators for Weibull regression models with measurement error. The adjusted estimator effectively removes bias in the shape parameter, unlike standard estimators.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Lifetime data analysis often involves measurement error, which can bias standard statistical models.
  • Weibull regression models are frequently used for analyzing time-to-event data.

Purpose of the Study:

  • To develop and evaluate adjusted estimators for Weibull regression models when measured lifetimes are subject to error.
  • To compare the bias properties of adjusted and standard estimators.

Main Methods:

  • Modeling true lifetimes using a Weibull regression model.
  • Incorporating measurement error models to simulate observed lifetimes.
  • Theoretical bias analysis using small measurement error asymptotics.
  • Simulation studies to compare estimator performance.

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Main Results:

  • Standard estimators for regression coefficients (except intercept) demonstrate robustness to bias.
  • The proposed adjusted estimator successfully eliminates bias in the shape parameter estimation.
  • Simulation results confirm theoretical findings on bias properties.

Conclusions:

  • Adjusted estimators are crucial for accurate parameter estimation in Weibull regression with measurement error.
  • The bias-robustness of standard coefficients is confirmed, but the shape parameter requires adjustment.
  • This work provides a method to improve the reliability of survival analysis in the presence of measurement errors.