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Structural inference in transition measurement error models for longitudinal data.

Wenqin Pan1, Xihong Lin, Donglin Zeng

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, USA. pan00007@mc.duke.edu

Biometrics
|August 22, 2006
PubMed
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We introduce transition measurement error models for longitudinal studies with error-prone covariates. Our method corrects biases in naive estimators, improving analysis of complex response variable effects.

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies are crucial for understanding changes over time.
  • Measurement error in covariates can lead to biased results in traditional models.
  • Existing methods often fail to account for both measurement error and complex response dynamics.

Purpose of the Study:

  • To propose novel transition measurement error models for longitudinal data.
  • To investigate the impact of measurement error on covariate and past response effects.
  • To develop a robust parameter estimation method addressing these challenges.

Main Methods:

  • Development of transition measurement error models.
  • Analysis of asymptotic bias in naive estimators for continuous and discrete outcomes.

Related Experiment Videos

  • Structural modeling using maximum likelihood estimation and an EM algorithm with Monte Carlo simulations.

    • Main Results:

    • Naive estimators of error-prone covariate effects are attenuated.
    • Naive estimators of past response effects are generally inflated.
    • The proposed EM algorithm effectively calculates maximum likelihood estimators.
    • Bayesian information criterion (BIC) aids in selecting correct model transition orders.

    Conclusions:

    • Transition measurement error models provide a valid framework for longitudinal data with measurement error.
    • The developed structural modeling approach offers accurate parameter estimation.
    • The method was successfully applied to a real-world social support study.