Open AccessA data mining and item response mixture modeling method to retrospectively measure Diagnostic and Statistical Manual of Mental Disorders ‐5 attention deficit hyperactivity disorder in the 1970 British Cohort Study
Author(s) -
Cotton Joanne,
Baker Sara T.
Publication year - 2019
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.1753
Subject(s) - categorical variable , attention deficit hyperactivity disorder , psychology , item response theory , clinical psychology , cohort , psychiatry , psychometrics , statistics , mathematics
Objective To facilitate future outcome studies, we aimed to develop a robust and replicable method for estimating a categorical and dimensional measure of Diagnostic and Statistical Manual of Mental Disorders ‐5 (DSM‐5) attention deficit hyperactivity disorder (ADHD) in the 1970 British Cohort Study (BCS70). Method Following a data mining framework, we mapped DSM‐5 ADHD symptoms to age 10 BCS70 data ( N = 11,426) and derived a 16‐item scale ( α = 0.85). Mapping was validated by an expert panel. A categorical subgroup was derived ( n = 594, 5.2%), and a zero‐inflated item response theory (IRT) mixture model fitted to estimate a dimensional measure. Results Subgroup composition was comparable with other ADHD samples. Relative risk ratios (ADHD/not ADHD) included boys = 1.38, unemployed fathers = 2.07, below average reading = 2.58, and depressed parent = 3.73. Our estimated measures correlated with two derived reference scales: Strengths and Difficulties Questionnaire hyperactivity ( r = 0.74) and a Rutter/Conners‐based scale ( r = 0.81), supporting construct validity. IRT model items (symptoms) had moderate to high discrimination (0.90–2.81) and provided maximum information at average to moderate theta levels of ADHD (0.5–1.75). Conclusion We extended previous work to identify ADHD in BCS70, derived scales from existing data, modeled ADHD items with IRT, and adjusted for a zero‐inflated distribution. Psychometric properties were promising, and this work will enable future studies of causal mechanisms in ADHD.