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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Doubly robust generalized estimating equations for longitudinal data.

Shaun Seaman1, Andrew Copas

  • 1Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK. shaun.seaman@mrc-bsu.cam.ac.uk

Statistics in Medicine
|January 21, 2009
PubMed
Summary
This summary is machine-generated.

Doubly robust generalized estimating equations (DR GEE) offer consistent analysis for missing data. These methods combine missingness and imputation models, improving upon weighted GEE and imputation alone for repeated-measures data.

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Clinical Trials

Background:

  • Generalized Estimating Equations (GEE) are widely used for analyzing repeated-measures data.
  • Handling missing data in GEE, particularly when missing at random (MAR), requires specialized methods like inverse-probability weighting (WGEE) or imputation.
  • Both WGEE and imputation methods rely on the correct specification of their respective models (missingness or imputation) for consistency.

Purpose of the Study:

  • To introduce and describe Doubly Robust Generalized Estimating Equations (DR GEE) as an advancement for analyzing repeated-measures data with missing observations.
  • To evaluate the performance and consistency of DR GEE under missing at random (MAR) assumptions.
  • To apply DR GEE to analyze simulated data and a real-world clinical trial (INITIO HIV therapy).

Main Methods:

  • Development and description of Doubly Robust Generalized Estimating Equations (DR GEE).
  • DR GEE integrate both a model for the probability of missingness and an imputation model for the expectation of missing observations.
  • Application of DR GEE to simulated datasets and the INITIO randomized clinical trial data for HIV therapy.

Main Results:

  • DR GEE are shown to be consistent when either the missingness model or the imputation model is correctly specified, offering robustness.
  • Illustrative analyses on simulated data demonstrate the practical application and potential benefits of DR GEE.
  • Analysis of the INITIO clinical trial using DR GEE provides insights into HIV therapy outcomes with MAR dropout.

Conclusions:

  • Doubly Robust Generalized Estimating Equations (DR GEE) provide a more reliable approach for analyzing longitudinal data with missingness compared to traditional WGEE or imputation methods alone.
  • The robustness of DR GEE enhances the validity of statistical inferences in the presence of missing at random data.
  • DR GEE are a valuable tool for biostatisticians and researchers conducting analyses of repeated-measures data, especially in clinical trials.