ArticlePLoS computational biology2021
The meaning of significant mean group differences for biomarker discovery.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 5 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 5 syntheses or guidelines pooled it.
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- Diagnostic accuracy of miRNAs in attention-deficit/hyperactivity disorder: a systematic review and meta-analysis.Frontiers in public health · 2025Pooled it
- Social motor synchrony in autism spectrum conditions: A systematic review.Autism : the international journal of research and practice · 2024Pooled it
- Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation.Schizophrenia bulletin · 2023Pooled it
- fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review.NeuroImage. Clinical · 2023Pooled it
- Label-free optical biomarker for prostate cancer diagnosis.Molecular medicine (Cambridge, Mass.) · 2026Article
- Autism data sharing: Benefits, challenges, and recommendations.PLOS digital health · 2026Review
- μ Opioid Modulation of Sensorimotor Functional Connectivity in Autism: Insights From a Pharmacological Neuroimaging Investigation Using Tianeptine.Biological psychiatry global open science · 2026Article
- Novel therapeutics in autism spectrum disorder.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- Beyond beta rhythms: subthalamic aperiodic broadband power scales with Parkinson's disease severity-a cross-sectional multicentre study.EBioMedicine · 2025Article
- Mapping the complexity of motor variability: From individual space of variability to motor fingerprints.Behavior research methods · 2025Article
- Latest clinical frontiers related to autism diagnostic strategies.Cell reports. Medicine · 2025Review
- Clarifying the reliability paradox: poor measurement reliability attenuates group differences.Frontiers in psychology · 2025Article
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Eye-Tracking Biomarkers and Autism Diagnosis in Primary Care.JAMA network open · 2024Article
- Brain glutathione and GABA+ levels in autistic children.Autism research : official journal of the International Society for Autism Research · 2024Article
- Evidence for embracing normative modeling.eLife · 2023Article
- A Neurobiological Profile Underlying Comorbidity Load and Prospective Increases in Dysphoria in a Focal Fear Sample.Biological psychiatry · 2023Article
- Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2023Article
- Restoring the missing person to personalized medicine and precision psychiatry.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past decade, biomarker 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 within-group 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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Registered trials
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