Open AccessUsing Complete Enumeration to Derive “One-Size-Fits-All” Versus “Subgroup-Specific” Diagnostic Rules for Substance Use Disorder
Author(s) -
Cassandra L. Boness,
Jordan Stevens,
Douglas Steinley,
Timothy J. Trull,
Kenneth J. Sher
Publication year - 2020
Publication title -
assessment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.59
H-Index - 87
eISSN - 1552-3489
pISSN - 1073-1911
DOI - 10.1177/1073191120903092
Subject(s) - set (abstract data type) , psychology , range (aeronautics) , sample size determination , statistics , diagnostic test , computer science , data mining , artificial intelligence , mathematics , medicine , materials science , composite material , programming language , emergency medicine
The use of fixed diagnostic rules, whereby the same diagnostic algorithms are applied across all individuals regardless of personal attributes, has been the tradition in the Diagnostic and Statistical Manual of Mental Disorders . This practice of "averaging" across individuals inevitably introduces diagnostic error. Furthermore, these average rules are typically derived through expert consensus rather than through data-driven approaches. Utilizing National Survey on Drug Use and Health 2013 ( N = 23, 889), we examined whether subgroup-specific, "customized" alcohol use disorder diagnostic rules, derived using deterministic optimization, perform better than an average, "one-size-fits-all" diagnostic rule. The average solution for the full sample included a set size of six and diagnostic threshold of three. Subgroups had widely varying set sizes ( M = 6.870; range = 5-10) with less varying thresholds ( M = 2.70; range = 2-4). External validation verified that the customized algorithms performed as well, and sometimes better than, the average solution in the prediction of relevant correlates. However, the average solution still performed adequately with respect to external validators.