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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Model selection of generalized estimating equations with multiply imputed longitudinal data.

Chung-Wei Shen1, Yi-Hau Chen

  • 1Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan.

Biometrical Journal. Biometrische Zeitschrift
|August 24, 2013
PubMed
Summary
This summary is machine-generated.

For longitudinal data analysis with missing values, multiple imputation (MI) with generalized estimating equations (GEE) requires careful model selection. New criteria extend existing methods, showing proper imputation and sufficient datasets are crucial for reliable results.

Keywords:
Missing dataMultiple imputationVariable selection

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal data frequently exhibit missingness, complicating analysis.
  • Multiple imputation (MI) is a common technique for handling missing longitudinal data.
  • MI combined with Generalized Estimating Equations (MI-GEE) is used for inference, but model selection remains challenging.

Purpose of the Study:

  • To extend existing Generalized Estimating Equations (GEE) model selection criteria (QIC, MLIC) for multiply imputed longitudinal data.
  • To evaluate the performance of these extended criteria for selecting the MI-GEE mean model.

Main Methods:

  • Extension of quasi-likelihood under the independence model criterion (QIC) and missing longitudinal information criterion (MLIC) for multiply imputed datasets.
  • Application of these extended criteria to real-world schizophrenia and AIDS studies.
  • Simulation studies under non-monotone missingness patterns with a moderate proportion of missing data.

Main Results:

  • Stable and reliable model selection in MI-GEE analysis necessitates more than a few imputed datasets.
  • MI-based GEE model selection methods perform well with adequate imputations, outperforming naive methods that ignore missing data.
  • Model selection criteria using improper (frequentist) multiple imputation generally outperform those using proper (Bayesian) multiple imputation.

Conclusions:

  • The proposed MI-based model selection criteria are effective for MI-GEE analysis.
  • The number of imputed datasets significantly impacts the stability and reliability of model selection.
  • Frequentist MI approaches demonstrate superior performance in model selection compared to Bayesian MI.