Data-driven atypical profiles of depressive symptoms: Identification and validation in a large cohort

Background: Atypical response behavior on depression questionnaires may invalidate depression severity measurements. This study aimed to identify and investigate atypical profiles of depressive symptoms using a data-driven approach based on the item response theory (IM.). Methods: A large cohort of participants completed the Inventory of Depressive Symptomatology selfreport (IDS-SR) at baseline (n=2329) and two-year follow-up (n=1971). Person-fit statistics were used to quantify how strongly each patient's observed symptom profile deviated from the expected profile given the group-based IRT model. Identified atypical profiles were investigated in terms of reported symptoms, external correlates and temporal consistency. Results: Compared to others, atypical responders (6.8%) showed different symptom profiles, with higher 'mood reactivity' and 'suicidal ideation' and lower levels of mild symptoms like 'sad mood'. Atypical responding was associated with more medication use (especially tricyclic antidepressants: OR=1.5), less somatization (OR=0.8), anxiety severity (OR=0.8) and anxiety diagnoses (OR=0.8-0.9), and was shown relatively stable (29.0%) over time. Limitations: This is a methodological proof-of-principal based on the IDS-SR in outpatients. Implementation studies are needed. Conclusion: Person-fit statistics can be used to identify patients who report atypical patterns of depressive symptoms. In research and clinical practice, the extra diagnostic information provided by person-fit statistics could help determine if respondents' depression severity scores are interpretable or should be augmented with additional information. (C) 2015 Elsevier B.V. All rights reserved,