1 Department of Applied Statistics, Social Science, and Humanities, New York University, United States; Center for the Promotion of Research at the Intersection of Information, Society, and Methodology, New York University, United States. Electronic address: daphna.harel@nyu.edu.
2 Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, United Kingdom.
3 Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
4 Department of Psychosomatic Medicine, Center for Internal Medicine and Dermatology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.
5 Department of Medicine, Stanford University, Stanford, CA, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA.
6 Department of Clinical, Neuro and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, the Netherlands.
7 Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
8 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
9 Department of Biostatistics and Health Informatics, King's College London, London, UK.
10 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada; Department of Medicine, McGill University, Montréal, Québec, Canada; Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montréal, Québec, Canada.
11 Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada; Department of Medicine, McGill University, Montréal, Québec, Canada; Department of Psychiatry, McGill University, Montréal, Québec, Canada; Department of Psychology, McGill University, Montréal, Québec, Canada; Department of Educational and Counselling Psychology, McGill University, Montréal, Québec, Canada; Biomedical Ethics Unit, McGill University, Montréal, Québec, Canada.
12 Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montréal, Québec, Canada.
13 Hull York Medical School and the Department of Health Sciences, University of York, Heslington, York, UK.
14 Library, Concordia University, Montréal, Québec, Canada.
15 Department of Rehabilitation Medicine, University of Washington, Seattle, Washington, USA.
16 Louis and Gabi Weisfeld School of Social Work, Bar Ilan University, Ramat Gan, Israel.
17 Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran.
18 Kbo-Lech-Mangfall-Klinik Garmisch-Partenkirchen, Klinik für Psychiatrie, Psychotherapie & Psychosomatik, Lehrkrankenhaus der Technischen Universität München, Munich, Germany.
19 University of Manitoba IBD Clinical and Research Centre, Winnipeg, Manitoba, Canada.
20 Centre for Rural Health, School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, South Africa.
21 Department of Psychiatry, Hospital Mesra Bukit Padang, Sabah, Malaysia.
22 Department of Neurosciences and Behavior, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto,

Shortened versions of self-reported questionnaires may be used to reduce respondent burden. When shortened screening tools are used, it is desirable to maintain equivalent diagnostic accuracy to full-length forms. This manuscript presents a case study that illustrates how external data and individual participant data meta-analysis can be used to assess the equivalence in diagnostic accuracy between a shortened and full-length form. This case study compares the Patient Health Questionnaire-9 (PHQ-9) and a 4-item shortened version (PHQ-Dep-4) that was previously developed using optimal test assembly methods. Using a large database of 75 primary studies (34,698 participants, 3,392 major depression cases), we evaluated whether the PHQ-Dep-4 cutoff of = 4 maintained equivalent diagnostic accuracy to a PHQ-9 cutoff of = 10. Using this external validation dataset, a PHQ-Dep-4 cutoff of = 4 maximized the sum of sensitivity and specificity, with a sensitivity of 0.88 (95% CI 0.81, 0.93), 0.68 (95% CI 0.56, 0.78), and 0.80 (95% CI 0.73, 0.85) for the semi-structured, fully structured, and MINI reference standard categories, respectively, and a specificity of 0.79 (95% CI 0.74, 0.83), 0.85 (95% CI 0.78, 0.90), and 0.83 (95% CI 0.80, 0.86) for the semi-structured, fully structured, and MINI reference standard categories, respectively. While equivalence with a PHQ-9 cutoff of = 10 was not established, we found the sensitivity of the PHQ-Dep-4 to be non-inferior to that of the PHQ-9, and the specificity of the PHQ-Dep-4 to be marginally smaller than the PHQ-9.