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Phylogenetic multilevel meta-analysis: A simulation study on the importance of modeling the phylogeny

Author

Listed:
  • Cinar, Ozan
  • Nakagawa, Shinichi

    (University of New South Wales)

  • Viechtbauer, Wolfgang

    (Maastricht University)

Abstract

Meta-analyses in ecology and evolution typically include multiple estimates from the same study and based on multiple species. The resulting dependencies in the data can be addressed by using a phylogenetic multilevel meta-analysis model. However, the complexity of the model poses challenges for accurately estimating model parameter. We therefore carried out a simulation study to investigate the performance of models with different degrees of complexities. While the overall mean was estimated with little to no bias irrespective of the model, only the model that accounted for the multilevel structure and that incorporates both a non-phylogenetic and a phylogenetic variance component provided confidence intervals with approximately nominal coverage rates. We therefore suggest that meta-analysts in ecology and evolution use the phylogenetic multilevel meta-analysis model as the de facto standard when analyzing multi-species datasets.

Suggested Citation

  • Cinar, Ozan & Nakagawa, Shinichi & Viechtbauer, Wolfgang, 2020. "Phylogenetic multilevel meta-analysis: A simulation study on the importance of modeling the phylogeny," EcoEvoRxiv su4zv, Center for Open Science.
    • Handle: RePEc:osf:ecoevo:su4zv
    DOI: 10.32942/osf.io/su4zv
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    References listed on IDEAS

    as
    1. Konstantopoulos, Spyros, 2011. "Fixed Effects and Variance Components Estimation in Three-Level Meta-Analysis," IZA Discussion Papers 5678, IZA Network @ LISER.
    2. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    3. Mark Pagel, 1999. "Inferring the historical patterns of biological evolution," Nature, Nature, vol. 401(6756), pages 877-884, October.
    4. Jessica Gurevitch & Julia Koricheva & Shinichi Nakagawa & Gavin Stewart, 2018. "Meta-analysis and the science of research synthesis," Nature, Nature, vol. 555(7695), pages 175-182, March.
    5. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    Full references (including those not matched with items on IDEAS)

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