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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
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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

Avoiding bias in mixed model inference for fixed effects.

Matthew J Gurka1, Lloyd J Edwards, Keith E Muller

  • 1Department of Community Medicine, School of Medicine, West Virginia University, PO Box 9190, Morgantown, WV 26506-9190, USA. mgurka@hsc.wvu.edu

Statistics in Medicine
|July 14, 2011
PubMed
Summary
This summary is machine-generated.

Accurate inference in general linear mixed models relies on correct covariance structure selection. Underspecified models, common in longitudinal studies, cause bias in fixed effects, even with more data.

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • General linear mixed models (GLMMs) are widely used for longitudinal data.
  • Accurate inference for fixed effects in GLMMs is crucial for reliable conclusions.
  • Covariance structure selection significantly impacts GLMM analysis.

Purpose of the Study:

  • To investigate the impact of covariance model misspecification on fixed effects inference in GLMMs.
  • To demonstrate that underspecification leads to biased inference, regardless of sample size.
  • To evaluate strategies for avoiding bias in fixed effects estimation.

Main Methods:

  • Analysis of a large longitudinal study of children.
  • Simulation studies to assess bias under various covariance structures.
  • Mathematical proofs to confirm bias in both small and large samples.
  • Evaluation of backwards selection and sandwich estimators.

Main Results:

  • Underspecified covariance structures, such as compound symmetry, lead to biased fixed effects inference.
  • This bias persists in both small and large sample sizes, inflating Type I error rates.
  • Backwards selection starting with an unstructured pattern offers the best protection against bias.
  • The sandwich estimator is a valid alternative for large samples.

Conclusions:

  • Careful selection of the covariance model is essential for valid fixed effects inference in GLMMs.
  • Researchers must be aware of potential biases arising from covariance model choice.
  • Strategies like backwards selection or using the sandwich estimator can mitigate bias.