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Weibull Regression With Both Measurement Error and Misclassification in Covariates.
1School of Artificial Intelligence, Shenzhen Technology University, Shenzhen, China.
Biometrical Journal. Biometrische Zeitschrift
|October 8, 2025
Summary
This study addresses measurement error and misclassification in nutritional epidemiology using the approximate maximum likelihood estimation (AMLE) method for survival data. The findings offer a way to correct biases in covariate analysis for improved statistical power.
Area of Science:
- Epidemiology
- Biostatistics
- Survival Analysis
Background:
- Measurement error and misclassification in covariates are common issues in nutritional epidemiology.
- These errors can lead to biased estimates and reduced statistical power in analyses.
- Simultaneously addressing both issues, particularly in survival models with censoring, remains a challenge.
Purpose of the Study:
- To investigate biases arising from measurement error and misclassification in covariates within a Weibull accelerated failure time model.
- To explore the application and asymptotic properties of approximate maximum likelihood estimation (AMLE) for correcting these biases.
- To evaluate the performance of the proposed method using simulation studies and real-world data.
Main Methods:
- Utilized the Weibull accelerated failure time model to analyze survival data.
- Applied approximate maximum likelihood estimation (AMLE) to correct for simultaneous measurement error and misclassification in covariates.
- Conducted extensive simulation studies to assess the finite-sample performance of the developed estimator.
- Applied the method to analyze nutrient data from the EPIC-InterAct Study.
Main Results:
- The approximate maximum likelihood estimation (AMLE) method effectively corrects biases caused by both measurement error and misclassification in covariates within the Weibull accelerated failure time model.
- Simulation studies demonstrated the good finite-sample performance of the proposed estimator.
- The method was successfully applied to real-world data, addressing measurement error and misclassification in nutrient intake.
Conclusions:
- The study successfully extends the application of AMLE to survival analysis, offering a robust approach to handle complex covariate error structures.
- The proposed method provides a valuable tool for nutritional epidemiology and other fields facing similar data challenges.
- Accurate covariate adjustment in survival models is crucial for reliable epidemiological findings.
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