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December 1, 2021

Data Pollution in Neuropsychiatry—An Under-Recognized but Critical Barrier to Research Progress

Author Affiliations
  • 1Department of Psychology, Texas State University, San Marcos
  • 2Department of Psychology, Beijing Normal University, Beijing, China
  • 3Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla
JAMA Psychiatry. 2022;79(2):97-98. doi:10.1001/jamapsychiatry.2021.2812

Neuropsychiatric research is substantially impeded by issues surrounding data collection and analysis. While these issues have been extensively discussed,1,2 their severe impact on neuropsychiatric effect sizes is not as widely recognized. This may be due in part to the varied backgrounds of quantitative experts in neuropsychiatry, who are drawn from multiple fields, including statistics, epidemiology, computer science, and psychometrics. Individually, none of these fields fully address all threats to the integrity of neuropsychiatric data, which can include (1) unreliable measurement, (2) heterogeneous construct definition, (3) population mixtures with differing biopsychosocial mechanisms, (4) behavioral reporting bias by both patients and clinicians, (5) selection bias, and (6) data that are not missing at random. Successful application of analytical methods can be compromised when methods from a particular quantitative field do not fully account for these properties. This prevents new findings from emerging, substantially increases participant recruitment requirements, and threatens to waste individual and federal investment in research.

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