Unit of analysis issues in laboratory-based research

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...

Full description

Saved in:
Bibliographic Details
Published in:eLife 2018-01, Vol.7
Main Authors: Parsons, Nick R, Teare, M Dawn, Sitch, Alice J
Format: Article
Language:English
Subjects:
Biomedical research
Clinical trials
Epidemiology and Global Health
experimental design
Experiments
Generalized linear models
Human subjects
Laboratories
Medical research
mixed-effects models
Public health
Science
Science Forum
Scientists
Statistical analysis
Statistical methods
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 (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.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.32486