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The meaning of significant mean group differences for biomarker discovery

Research output: Contribution to journalArticlepeer-review

Eva Loth, Jumana Ahmad, Chris Chatham, Beatriz Lopez, Ben Carter, Daisy Crawley, Beth Oakley, Hannah Hayward, Jennifer Cooke, Antonia San José Cáceres, Danilo Bzdok, Emily Jones, Tony Charman, Christian F. Beckmann, Thomas Bourgeron, Roberto Toro, Jan Buitelaar, Declan Murphy, Guillame Dumas

Original languageEnglish
Article numbere1009477
JournalPLoS Computational Biology
Issue number11
Published18 Nov 2021

Bibliographical note

Funding Information: EL, JA, BL, BC, DC, BO, HH, JC, ASJC, EJ, TC, CB, TB, RT, JB, DM, and GD have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking is a joint support from the European Union's Horizon 2020 research and innovation programme, EFPIA, AUTISM SPEAKS, Autistica, and SFARI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2021 Loth et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

King's Authors


Over the past decade, biomaker discovery has become a key goal in psychiatry to aid in the more reliable diagnosis and prognosis of heterogeneous psychiatric conditions and the development of tailored therapies. Nevertheless, the prevailing statistical approach is still the mean group comparison between "cases"and "controls,"which tends to ignore withingroup variability. In this educational article, we used empirical data simulations to investigate how effect size, sample size, and the shape of distributions impact the interpretation of mean group differences for biomarker discovery. We then applied these statistical criteria to evaluate biomarker discovery in one area of psychiatric research - autism research. Across the most influential areas of autism research, effect size estimates ranged from small (d = 0.21, anatomical structure) to medium (d = 0.36 electrophysiology, d = 0.5, eye-tracking) to large (d = 1.1 theory of mind). We show that in normal distributions, this translates to approximately 45% to 63% of cases performing within 1 standard deviation (SD) of the typical range, i.e., they do not have a deficit/atypicality in a statistical sense. For a measure to have diagnostic utility as defined by 80% sensitivity and 80% specificity, Cohen's d of 1.66 is required, with still 40% of cases falling within 1 SD. However, in both normal and nonnormal distributions, 1 (skewness) or 2 (platykurtic, bimodal) biologically plausible subgroups may exist despite small or even nonsignificant mean group differences. This conclusion drastically contrasts the way mean group differences are frequently reported. Over 95% of studies omitted the "on average"when summarising their findings in their abstracts ("autistic people have deficits in X"), which can be misleading as it implies that the group-level difference applies to all individuals in that group. We outline practical approaches and steps for researchers to explore mean group comparisons for the discovery of stratification biomarkers.

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