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Approximate profile likelihood estimation for Cox regression with covariate measurement error.
1College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
Statistics in Medicine
|January 24, 2022
Summary
Measurement error in dietary intake data is a challenge in nutritional epidemiology. This study introduces approximate profile likelihood estimation (APLE) for Cox regression, offering a new method to address correlated errors in covariates.
Area of Science:
- Nutritional Epidemiology
- Biostatistics
- Statistical Modeling
Background:
- Measurement error in self-reported dietary intakes is a pervasive issue in nutritional epidemiology.
- Additive error models with highly correlated error variables are common in this field.
- Existing methods for handling measurement error in Cox regression have limitations.
Purpose of the Study:
- To propose a novel statistical method, approximate profile likelihood estimation (APLE), for addressing measurement error in covariates within Cox regression models.
- To establish the theoretical properties of the APLE method, including its asymptotic normality.
- To compare the performance of APLE with existing methods through simulation studies and real-world data application.
Main Methods:
- Development of the approximate profile likelihood estimation (APLE) method for Cox regression with additive error models.
- Theoretical analysis to establish the asymptotic normality of the APLE estimator under regularity conditions.
- Simulation studies to evaluate the finite sample performance of APLE.
- Application of APLE to analyze nutrient data from the EPIC-InterAct Study using a sensitivity analysis framework.
Main Results:
- The proposed APLE method is shown to be asymptotically normal under stated regularity conditions.
- Simulation studies demonstrate the empirical performance of the APLE estimator in finite samples.
- Regression calibration, a widely used method, is identified as a specific instance of APLE.
- APLE was successfully applied to handle measurement error in nutrient data within the EPIC-InterAct Study.
Conclusions:
- APLE provides a robust and theoretically sound approach for handling measurement error in covariates within Cox regression, particularly when error variables are correlated.
- The method offers advantages over existing techniques and is applicable to real-world epidemiological data.
- Further research can explore extensions of APLE to more complex error structures or different regression models.
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