A 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. Issue 1 (6th November 2018)

• Record Type: Journal Article
• Authors: Cotton, Joanne; Baker, Sara T.
• Is Part Of: International journal of methods in psychiatric research. Volume 28:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-11-06
• Subjects: ADHD -- BCS70 -- data mining -- IRT; Psychiatry -- Research -- Methodology -- Periodicals; Psychiatry -- Periodicals; 616.890072
• DOI: 10.1002/mpr.1753
• Languages: English
• ISSNs: 1049-8931

Abstract

Abstract: 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.