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Approximate maximum likelihood estimation for logistic regression with covariate measurement error.

Zhiqiang Cao1,2, Man Yu Wong2

  • 1College of Big Data and Internet, Shenzhen Technology University, Shenzhen, P. R. China.

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|September 11, 2020
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Summary
This summary is machine-generated.

This study introduces an approximate maximum likelihood estimation (AMLE) to address measurement error in dietary intake data from nutritional epidemiology. The method improves statistical power and reduces bias in analyses using food frequency questionnaires.

Keywords:
approximate maximum likelihoodlogistic regressionmeasurement errornutritional epidemiology

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Area of Science:

  • Nutritional Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Dietary intake assessment using food frequency questionnaires (FFQs) in nutritional epidemiology is susceptible to measurement error.
  • Uncorrected measurement error in covariates can lead to biased estimates and reduced statistical power in epidemiological studies.
  • The European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct Study provides a relevant dataset characterized by such measurement errors.

Purpose of the Study:

  • To develop and validate a statistical method to correct for measurement error in dietary intake data within logistic regression models.
  • To propose an Approximate Maximum Likelihood Estimation (AMLE) method that accounts for additive error models.
  • To provide an improved estimator compared to standard methods, addressing bias and power loss.

Main Methods:

  • Development of an Approximate Maximum Likelihood Estimation (AMLE) method for covariates with measurement error.
  • Utilizing an additive error model tailored to the data characteristics of the EPIC-InterAct Study.
  • Establishing asymptotic normality of the AMLE estimator under specified regularity conditions.
  • Conducting simulation studies to evaluate the finite sample performance of the AMLE method.

Main Results:

  • The proposed AMLE method offers an adjusted approach to regression calibration, providing an approximately consistent estimator.
  • Simulation studies demonstrated the effectiveness of AMLE in addressing measurement error compared to uncorrected methods.
  • Asymptotic normality of the estimator was theoretically established, supporting its statistical validity.

Conclusions:

  • The AMLE method is a valuable tool for handling measurement error in dietary intake data in nutritional epidemiology.
  • Application of AMLE to the EPIC-InterAct Study data allows for more accurate estimation of nutrient-disease associations.
  • The sensitivity analysis framework further enhances the robustness of findings when dealing with measurement error.