Using OCD as its example, a review argues that big data in psychiatry must separate etiology from pathophysiology and stay hypothesis-driven to answer a specific disorder's three core questions
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar