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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Power analysis for generalized linear mixed models in ecology and evolution.

Paul C D Johnson1, Sarah J E Barry2, Heather M Ferguson3

  • 1Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow Graham Kerr Building, Glasgow, G12 8QQ, UK ; Robertson Centre for Biostatistics, University of Glasgow Boyd Orr Building, Glasgow, G12 8QQ, UK.

Methods in Ecology and Evolution
|April 21, 2015
PubMed
Summary
This summary is machine-generated.

Researchers in ecology and evolution often neglect study power analysis due to complex models. Simulation from generalized linear mixed models (GLMMs) offers a flexible solution for better research design.

Keywords:
experimental designgeneralized linear mixed modellong-lasting insecticidal netoverdispersionprecisionrandom effectssample sizesimulation

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Area of Science:

  • Ecology and Evolution
  • Statistical Modeling
  • Research Design

Background:

  • Many ecology and evolution researchers do not conduct power analyses to determine adequate study size or precision.
  • This is often due to the unsuitability of simple analytical methods for complex statistical models common in the field.
  • Under- or over-powered studies can lead to wasted resources and unreliable conclusions.

Purpose of the Study:

  • To present simulation from generalized linear mixed models (GLMMs) as a flexible and accessible approach to power analysis.
  • To encourage the use of power analysis in ecology and evolution research.
  • To demonstrate how simulation can account for random effects, overdispersion, and various response distributions.

Main Methods:

  • Simulation-based power analysis using generalized linear mixed models (GLMMs).
  • Application of the method to two case studies: tick burden estimation in grouse chicks and insecticide-treated net efficacy for malaria control.
  • Development and provision of a free R function, sim.glmm, for conducting these simulations.

Main Results:

  • Accounting for random effects and overdispersion in simulations substantially impacts power and precision estimates.
  • Study designs may require up to fivefold increases in sampling effort when realistic factors are considered.
  • Simulation methods revealed potential performance issues with standard GLMM-fitting approaches in certain scenarios.

Conclusions:

  • Standard analytical power analysis methods are inadequate for complex ecological and evolutionary models.
  • Simulation-based power analysis for GLMMs offers a flexible and powerful alternative.
  • Wider adoption of these simulation methods can improve the quality of study design in ecology and evolution.