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Lookup NU author(s): Professor Dawn Teare
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Parsons et al. Many studies in the biomedical research literature report analyses that fail to recognise important data dependencies from multilevel or complex experimental designs. Statistical inferences resulting from such analyses are unlikely to be valid and are often potentially highly misleading. Failure to recognise this as a problem is often referred to in the statistical literature as a unit of analysis (UoA) issue. Here, by analysing two example datasets in a simulation study, we demonstrate the impact of UoA issues on study efficiency and estimation bias, and highlight where errors in analysis can occur. We also provide code (written in R) as a resource to help researchers undertake their own statistical analyses.
Author(s): Parsons NR, Teare MD, Sitch AJ
Publication type: Article
Publication status: Published
Online publication date: 10/01/2018
Acceptance date: 13/12/2017
Date deposited: 11/11/2019
ISSN (electronic): 2050-084X
Publisher: eLife Sciences Publications Ltd
PubMed id: 29319501
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