Year: 2018
Type: article
Abstract: Increasingly complex statistical models are being used for the analysis of biological data. Recent commentary has focused on the ability to compute the same outcome for a given dataset (reproducibility). We argue that a reproducible statistical analysis is not necessarily valid because of unique patterns of nonindependence in every biological dataset. We advocate that analyses should be evaluated with known-truth simulations that capture biological reality, a process we call "analysis validation." We review the process of validation and suggest criteria that a validation project should meet. We find that different fields of science have historically failed to meet all criteria, and we suggest ways to implement meaningful validation in training and practice.
Source: PLoS Biology
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $5,500
Funders: National Science Foundation, U.S. Department of Agriculture, University of Pennsylvania, North Carolina State University, National Institutes of Health, National Institute of Food and Agriculture, University of North Carolina Wilmington, National Institute of Allergy and Infectious Diseases, U.S. National Library of Medicine
Awards: G2128131297, Testing the tests: a predictive framework to guide genome scans for locally adapted traits, Biomedical Computing and Informatics Strategies for Infectious Disease Research, Bioinformatics Strategies for Genome Wide Association Studies, Biomedical Computing and Informatics Strategies for Precision Medicine
OpenAlex ID: W2904294379