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Estimation in semiparametric transition measurement error models for longitudinal data.

Wenqin Pan1, Donglin Zeng, Xihong Lin

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

Biometrics
|January 29, 2009
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Summary
This summary is machine-generated.

This study introduces a new method for analyzing longitudinal data with measurement errors in covariates. The proposed approach provides consistent estimators for regression coefficients, crucial for accurate analysis of complex datasets.

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Longitudinal data analysis is essential for understanding changes over time.
  • Measurement error in covariates can bias statistical models.
  • Transition models are used to study sequential events.

Purpose of the Study:

  • To develop a semiparametric measurement error model for longitudinal data.
  • To address models where covariates are measured with error without distributional assumptions.
  • To propose a robust statistical method for analyzing such data.

Main Methods:

  • Utilizing an estimating equation approach.
  • Employing the pseudo conditional score method.
  • Developing consistent and asymptotically normal estimators.

Main Results:

  • The proposed estimators for regression coefficients are consistent.
  • Asymptotic normality of the estimators is demonstrated.
  • Efficiency loss considerations are discussed.

Conclusions:

  • The developed method effectively handles measurement error in longitudinal transition models.
  • Simulation studies confirm the finite-sample performance of the estimators.
  • The approach is illustrated using the AIDS Costs and Services Utilization Survey data.