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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Moment Adjusted Imputation for Multivariate Measurement Error Data with Applications to Logistic Regression.

Laine Thomas1, Leonard A Stefanski, Marie Davidian

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, U.S.A.

Computational Statistics & Data Analysis
|September 28, 2013
PubMed
Summary

Measurement error in clinical covariates can skew study results. Moment Adjusted Imputation (MAI) offers a practical solution for scalar latent variables and is extended for correlated multivariate measurement error.

Keywords:
Logistic RegressionMoment adjusted imputationMultivariate measurement errorRegression calibration

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Statistical Modeling

Background:

  • Covariates in clinical studies are susceptible to measurement error from biological variability and device inaccuracies.
  • Mismeasured data can lead to biased summary statistics and regression models compared to analyses using true covariate values.
  • Existing statistical methods for measurement error involve trade-offs between implementation ease and analytical performance.

Purpose of the Study:

  • To address measurement error in scalar latent variables using Moment Adjusted Imputation (MAI).
  • To extend MAI methodology to handle correlated multivariate measurement error in clinical covariates.
  • To evaluate alternative strategies for multivariate measurement error, focusing on computational feasibility and performance.

Main Methods:

  • Application of Moment Adjusted Imputation (MAI) for scalar latent variable measurement error.
  • Development and adaptation of MAI for multivariate latent variables with correlated errors.
  • Exploration of alternative computational strategies for addressing multivariate measurement error.

Main Results:

  • MAI demonstrates ease of implementation and robust performance in various settings for scalar latent variables.
  • Extension of MAI to multivariate settings presents unique statistical and computational challenges.
  • Proposed alternative strategies, including a computationally feasible option, show promising performance in handling correlated multivariate measurement error.

Conclusions:

  • Moment Adjusted Imputation (MAI) is an effective method for correcting measurement error in scalar latent variables.
  • Addressing correlated multivariate measurement error requires specialized approaches beyond simple extensions of scalar methods.
  • Computationally feasible strategies exist for managing multivariate measurement error, offering practical solutions for clinical studies.