Browsing by Author "John P.A. Ioannidis"

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    The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis
    (2020-01-01) Chen He; Brooke Levis; Kira E. Riehm; Nazanin Saadat; Alexander W. Levis; Marleine Azar; Danielle B. Rice; Ankur Krishnan; Yin Wu; Ying Sun; Mahrukh Imran; Jill Boruff; Pim Cuijpers; Simon Gilbody; John P.A. Ioannidis; Lorie A. Kloda; Dean McMillan; Scott B. Patten; Ian Shrier; Roy C. Ziegelstein; Dickens H. Akena; Bruce Arroll; Liat Ayalon; Hamid R. Baradaran; Murray Baron; Anna Beraldi; Charles H. Bombardier; Peter Butterworth; Gregory Carter; Marcos Hortes Nisihara Chagas; Juliana C.N. Chan; Rushina Cholera; Kerrie Clover; Yeates Conwell; Janneke M. De Man-Van Ginkel; Jesse R. Fann; Felix H. Fischer; Daniel Fung; Bizu Gelaye; Felicity Goodyear-Smith; Catherine G. Greeno; Brian J. Hall; Patricia A. Harrison; Martin Härter; Ulrich Hegerl; Leanne Hides; Stevan E. Hobfoll; Marie Hudson; Thomas N. Hyphantis; Masatoshi Inagaki; Khalida Ismail; Nathalie Jetté; Mohammad E. Khamseh; Kim M. Kiely; Yunxin Kwan; Femke Lamers; Shen Ing Liu; Manote Lotrakul; Sonia R. Loureiro; Bernd Löwe; Laura Marsh; Anthony McGuire; Sherina Mohd-Sidik; Tiago N. Munhoz; Kumiko Muramatsu; Flávia L. Osório; Vikram Patel; Brian W. Pence; Philippe Persoons; Angelo Picardi; Katrin Reuter; Alasdair G. Rooney; Iná S. Da Silva Dos Santos; Juwita Shaaban; Abbey Sidebottom; Adam Simning; Lesley Stafford; Sharon Sung; Pei Lin Lynnette Tan; Alyna Turner; Henk C.P.M. Van Weert; Jennifer White; Mary A. Whooley; Kirsty Winkley; Mitsuhiko Yamada; Brett D. Thombs; Andrea Benedetti; Melbourne Institute; Melbourne School of Psychological Sciences; San Francisco VA Health Care System; Mackay Medical College; Calvary Mater Newcastle; Duke-NUS Medical School Singapore; City of Minneapolis; Hunter Medical Research Institute, Australia; Niigata Seiryo University; Bar-Ilan University School of Social Work; Makerere University; Concordia University; University Medical Center Utrecht; Royal Women's Hospital, Carlton; Harvard T.H. Chan School of Public Health; KU Leuven– University Hospital Leuven; University of Queensland; Mackay Memorial Hospital Taiwan; University of New South Wales (UNSW) Australia; University of Edinburgh; Yong Loo Lin School of Medicine; Shimane University; Charité – Universitätsmedizin Berlin; Universiti Putra Malaysia; The University of North Carolina at Chapel Hill; KU Leuven; Iran University of Medical Sciences; Prince of Wales Hospital Hong Kong; University of Rochester Medical Center; University of California, San Francisco; Neuroscience Research Australia; Universidade de Macau; Lady Davis Institute for Medical Research; UNC School of Medicine; Technical University of Munich; Monash University; Deakin University; National Center of Neurology and Psychiatry Kodaira; University of Newcastle, Faculty of Health and Medicine; University of York; Saint Joseph's College of Maine; Faculty of Medicine, Ramathibodi Hospital, Mahidol University; University of Aberdeen; University of Pittsburgh; University of Washington, Seattle; Universidade Federal de Pelotas; Icahn School of Medicine at Mount Sinai; Stanford University; King's College London; Istituto Superiore Di Sanita; Singapore Institute of Mental Health; Australian National University; Vrije Universiteit Amsterdam; Goethe-Universität Frankfurt am Main; Centre universitaire de santé McGill; Johns Hopkins Bloomberg School of Public Health; University of Auckland; Nanyang Technological University; Universitätsklinikum Hamburg-Eppendorf und Medizinische Fakultät; Panepistimion Ioanninon; Chinese University of Hong Kong; Harvard Medical School; School of Medical Sciences - Universiti Sains Malaysia; McGill University; Tan Tock Seng Hospital; Baylor College of Medicine; University of Calgary; Amsterdam UMC - University of Amsterdam; Johns Hopkins School of Medicine; Private Practice for Psychotherapy and Psycho-oncology; STAR-Stress; National Institute of Science and Technology; Allina Health
    © 2019 S. Karger AG, Basel. All rights reserved. Background: Screening for major depression with the Patient Health Questionnaire-9 (PHQ-9) can be done using a cutoff or the PHQ-9 diagnostic algorithm. Many primary studies publish results for only one approach, and previous meta-analyses of the algorithm approach included only a subset of primary studies that collected data and could have published results. Objective: To use an individual participant data meta-analysis to evaluate the accuracy of two PHQ-9 diagnostic algorithms for detecting major depression and compare accuracy between the algorithms and the standard PHQ-9 cutoff score of ≥10. Methods: Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, Web of Science (January 1, 2000, to February 7, 2015). Eligible studies that classified current major depression status using a validated diagnostic interview. Results: Data were included for 54 of 72 identified eligible studies (n participants = 16,688, n cases = 2,091). Among studies that used a semi-structured interview, pooled sensitivity and specificity (95% confidence interval) were 0.57 (0.49, 0.64) and 0.95 (0.94, 0.97) for the original algorithm and 0.61 (0.54, 0.68) and 0.95 (0.93, 0.96) for a modified algorithm. Algorithm sensitivity was 0.22-0.24 lower compared to fully structured interviews and 0.06-0.07 lower compared to the Mini International Neuropsychiatric Interview. Specificity was similar across reference standards. For PHQ-9 cutoff of ≥10 compared to semi-structured interviews, sensitivity and specificity (95% confidence interval) were 0.88 (0.82-0.92) and 0.86 (0.82-0.88). Conclusions: The cutoff score approach appears to be a better option than a PHQ-9 algorithm for detecting major depression.
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    A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests
    (2017-11-01) Gabrielle Simoneau; Brooke Levis; Pim Cuijpers; John P.A. Ioannidis; Scott B. Patten; Ian Shrier; Charles H. Bombardier; Flavia de Lima Osório; Jesse R. Fann; Dwenda Gjerdingen; Femke Lamers; Manote Lotrakul; Bernd Löwe; Juwita Shaaban; Lesley Stafford; Henk C.P.M. van Weert; Mary A. Whooley; Karin A. Wittkampf; Albert S. Yeung; Brett D. Thombs; Andrea Benedetti; McGill University; Lady Davis Institute for Medical Research; Vrije Universiteit Amsterdam; Stanford University; University of Calgary; University of Washington, Seattle; Universidade de Sao Paulo - USP; University of Minnesota Twin Cities; VU University Medical Center; Mahidol University; Universitätsklinikum Hamburg-Eppendorf und Medizinische Fakultät; School of Medical Sciences - Universiti Sains Malaysia; Royal Women's Hospital, Carlton; Academic Medical Centre, University of Amsterdam; VA Medical Center; Massachusetts General Hospital; Centre universitaire de santé McGill
    © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings.
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    Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: A systematic review and individual participant data meta-analysis
    (2020-06-01) Yin Wu; Yin Wu; Yin Wu; Brooke Levis; Kira E. Riehm; Nazanin Saadat; Alexander W. Levis; Marleine Azar; Danielle B. Rice; Danielle B. Rice; Jill Boruff; Pim Cuijpers; Simon Gilbody; John P.A. Ioannidis; Lorie A. Kloda; Dean McMillan; Scott B. Patten; Scott B. Patten; Ian Shrier; Ian Shrier; Roy C. Ziegelstein; Dickens H. Akena; Bruce Arroll; Liat Ayalon; Hamid R. Baradaran; Hamid R. Baradaran; Murray Baron; Murray Baron; Charles H. Bombardier; Peter Butterworth; Peter Butterworth; Gregory Carter; Marcos H. Chagas; Juliana C.N. Chan; Juliana C.N. Chan; Juliana C.N. Chan; Rushina Cholera; Yeates Conwell; Janneke M. De Man-Van Ginkel; Jesse R. Fann; Felix H. Fischer; Daniel Fung; Daniel Fung; Daniel Fung; Daniel Fung; Bizu Gelaye; Felicity Goodyear-Smith; Catherine G. Greeno; Brian J. Hall; Brian J. Hall; Patricia A. Harrison; Martin Härter; Ulrich Hegerl; Leanne Hides; Stevan E. Hobfoll; Marie Hudson; Marie Hudson; Thomas Hyphantis; Masatoshi Inagaki; Nathalie Jetté; Nathalie Jetté; Nathalie Jetté; Mohammad E. Khamseh; Kim M. Kiely; Kim M. Kiely; Yunxin Kwan; Femke Lamers; Shen Ing Liu; Shen Ing Liu; Shen Ing Liu; Shen Ing Liu; Manote Lotrakul; Sonia R. Loureiro; Bernd Löwe; Anthony McGuire; Sherina Mohd-Sidik; Tiago N. Munhoz; Kumiko Muramatsu; Flávia L. Osório; Flávia L. Osório; Vikram Patel; Vikram Patel; Brian W. Pence; Philippe Persoons; Philippe Persoons; Angelo Picardi; Katrin Reuter; Alasdair G. Rooney; Iná S. Santos; Juwita Shaaban; Abbey Sidebottom; Adam Simning; Lesley Stafford; Lesley Stafford; Sharon Sung; Sharon Sung; Pei Lin Lynnette Tan; Alyna Turner; Alyna Turner; Melbourne Institute; Melbourne School of Psychological Sciences; Mackay Medical College; Duke-NUS Medical School Singapore; City of Minneapolis; Hunter Medical Research Institute, Australia; Niigata Seiryo University; Makerere University; Concordia University; University Medical Center Utrecht; Royal Women's Hospital, Carlton; Harvard T.H. Chan School of Public Health; KU Leuven– University Hospital Leuven; The University of Queensland; Mackay Memorial Hospital Taiwan; University of New South Wales (UNSW) Australia; The University of Edinburgh; Yong Loo Lin School of Medicine; Shimane University; Charité – Universitätsmedizin Berlin; Universiti Putra Malaysia; The University of North Carolina at Chapel Hill; KU Leuven; Iran University of Medical Sciences; Universitäts Klinikum Freiburg und Medizinische Fakultät; Prince of Wales Hospital Hong Kong; University of Rochester Medical Center; Neuroscience Research Australia; Universidade de Macau; Lady Davis Institute for Medical Research; UNC School of Medicine; Deakin University; University of Newcastle, Faculty of Health and Medicine; University of York; Saint Joseph's College of Maine; Faculty of Medicine, Ramathibodi Hospital, Mahidol University; University of Pittsburgh; University of Washington, Seattle; Universidade Federal de Pelotas; Icahn School of Medicine at Mount Sinai; Stanford University; University of Aberdeen School of Medicine, Medical Sciences and Nutrition; Istituto Superiore Di Sanita; Singapore Institute of Mental Health; The Australian National University; Vrije Universiteit Amsterdam; Universidade de Sao Paulo - USP; Goethe-Universität Frankfurt am Main; University of Auckland; Nanyang Technological University; Johns Hopkins University; Universitätsklinikum Hamburg-Eppendorf und Medizinische Fakultät; Panepistimion Ioanninon; Chinese University of Hong Kong; Bar-Ilan University; Harvard Medical School; School of Medical Sciences - Universiti Sains Malaysia; McGill University; Tan Tock Seng Hospital; University of Calgary; STAR-Stress; National Institute of Science and Technology; Allina Health
    Copyright © Cambridge University Press 2019. Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.Methods We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.Results 16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (-0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).Conclusions PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.
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    How to use an article about genetic association - C: What are the results and will they help me in caring for my patients?
    (2009-01-21) John Attia; John P.A. Ioannidis; Ammarin Thakkinstian; Mark McEvoy; Rodney J. Scott; Cosetta Minelli; John Thompson; Claire Infante-Rivard; Gordon Guyatt; Royal Newcastle Hospital; Hunter Medical Research Institute, Australia; John Hunter Hospital; University of Ioannina, School of Medicine; Tufts University School of Medicine; Mahidol University; University of Newcastle, Australia; Hunter Area Pathology Service; University of Newcastle Faculty of Medicine and Health Sciences; National Heart and Lung Institute; University of Leicester; McGill University; McMaster University, Faculty of Health Sciences
    In the first 2 articles of this series, we reviewed the basic genetics concepts necessary to understand genetic association studies, and we enumerated the major issues in judging the validity of these studies. In this third article, we review the issues relating to the applicability of the results in the clinical situation. How large and precise are the associations? Many genetic effects are expected to be smaller in magnitude than traditional risk factors. Does the genetic association improve predictive power beyond easily measured clinical variables? In some cases, the additional genetic information adds only a small increment in the predictive ability of a diagnostic or prognostic test. What are the absolute vs relative effects? Even if the genetic risk is high in relative terms, the baseline risk may be very low in absolute terms. Is the risk-associated allele likely to be present in my patient? A risk allele may have a strong effect but be rare in a particular ethnic group. Is the patient likely better off knowing the genetic information? Given that genes cannot be modified, one must weigh whether the genetic information is likely to be helpful in planning other health interventions or initiating behavior change. ©2009 American Medical Association. All rights reserved.
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    How to use an article about genetic association A: Background concepts
    (2009-01-07) John Attia; John P.A. Ioannidis; Ammarin Thakkinstian; Mark McEvoy; Rodney J. Scott; Cosetta Minelli; John Thompson; Claire Infante-Rivard; Gordon Guyatt; Hunter Medical Research Institute, Australia; John Hunter Hospital; University of Ioannina, School of Medicine; Tufts University School of Medicine; Mahidol University; Hunter Area Pathology Service; National Heart and Lung Institute; University of Leicester; McGill University; McMaster University, Faculty of Health Sciences; University of Newcastle, Australia
    This is the first in a series of 3 articles serving as an introduction to clinicians wishing to read and critically appraise genetic association studies. We summarize the key concepts in genetics that clinicians must understand to review these studies, including the structure of DNA, transcription and translation, patterns of inheritance, Hardy-Weinberg equilibrium, and linkage disequilibrium. We review the types of DNA variation, including single-nucleotide polymorphisms (SNPs), insertions, and deletions, and how these can affect protein function. We introduce the idea of genetic association for both single-candidate gene and genome-wide association studies, in which thousands of genetic variants are tested for association with disease. We use the APOE polymorphism and its association with dementia as a case study to demonstrate the concepts and introduce the terminology used in this field. The second and third articles will focus on issues of validity and applicability. ©2009 American Medical Association. All rights reserved.
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    How to use an article about genetic association B: Are the results of the study valid?
    (2009-01-14) John Attia; John P.A. Ioannidis; Ammarin Thakkinstian; Mark McEvoy; Rodney J. Scott; Cosetta Minelli; John Thompson; Claire Infante-Rivard; Gordon Guyatt; University of Newcastle, Australia; University of Ioannina, School of Medicine; Tufts University School of Medicine; Mahidol University; Hunter Area Pathology Service; University of Newcastle Faculty of Medicine and Health Sciences; National Heart and Lung Institute; University of Leicester; McGill University; McMaster University, Faculty of Health Sciences; Royal Newcastle Hospital
    In the first article of this series, we reviewed the basic genetics concepts necessary to understand genetic association studies. In this second article, we enumerate the major issues in judging the validity of these studies, framed as critical appraisal questions. Was the disease phenotype properly defined and accurately recorded by someone blind to the genetic information? Have any potential differences between disease and nondisease groups, particularly ethnicity, been properly addressed? In genetic studies, one potential cause of spurious associations is differences between cases and controls in ethnicity, a situation termed population stratification. Was measurement of the genetic variants unbiased and accurate? Methods for determining DNA sequence variation are not perfect and may have some measurement error. Do the genotype proportions observe Hardy-Weinberg equilibrium? This simple mathematic rule about the distribution of genetic groups may be one way to check for errors in reading DNA information. Have the investigators adjusted their inferences for multiple comparisons? Given the thousands of genetic markers tested in genome-wide association studies, the potential for false-positive and false-negative results is much higher than in traditional medical studies, and it is particularly important to look for replication of results. ©2009 American Medical Association. All rights reserved.
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    Implementation of proteomic biomarkers: Making it work
    (2012-09-01) Harald Mischak; John P.A. Ioannidis; Angel Argiles; Teresa K. Attwood; Erik Bongcam-Rudloff; Mark Broenstrup; Aristidis Charonis; George P. Chrousos; Christian Delles; Anna Dominiczak; Tomasz Dylag; Jochen Ehrich; Jesus Egido; Peter Findeisen; Joachim Jankowski; Robert W. Johnson; Bruce A. Julien; Tim Lankisch; Hing Y. Leung; David Maahs; Fulvio Magni; Michael P. Manns; Efthymios Manolis; Gert Mayer; Gerjan Navis; Jan Novak; Alberto Ortiz; Frederik Persson; Karlheinz Peter; Hans H. Riese; Peter Rossing; Naveed Sattar; Goce Spasovski; Visith Thongboonkerd; Raymond Vanholder; Joost P. Schanstra; Antonia Vlahou; University of Glasgow; Mosaiques Diagnostics; University of Ioannina, School of Medicine; Stanford University School of Medicine; Stanford University; RD Néphrologie; University of Manchester; Uppsala Universitet; Sveriges lantbruksuniversitet; Sanofi-Aventis Deutschland GmbH; Academy of Athens; University of Athens Medical School; European Commission; Medizinische Hochschule Hannover (MHH); Universidad Autonoma de Madrid; Universitatsklinikum Mannheim; Charité – Universitätsmedizin Berlin; Abbott Laboratories; University of Alabama at Birmingham; Beatson Institute for Cancer Research; University of Colorado Health Sciences Center; Universita degli Studi di Milano - Bicocca; European Medicines Agency; Medizinische Universitat Innsbruck; University of Groningen, University Medical Center Groningen; Steno Diabetes Center; Baker Heart and Diabetes Institute; Instituto de Salud Carlos III; SS Cyril and Methodius University Faculty of Medicine; Mahidol University; University Hospital of Ghent; Universite Paul Sabatier Toulouse III
    While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare. © 2012 The Authors. European Journal of Clinical Investigation © 2012 Stichting European Society for Clinical Investigation Journal Foundation.
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    Probability of major depression diagnostic classification using semi-structured versus fully structured diagnostic interviews
    (2018-06-01) Brooke Levis; Andrea Benedetti; Kira E. Riehm; Nazanin Saadat; Alexander W. Levis; Marleine Azar; Danielle B. Rice; Matthew J. Chiovitti; Tatiana A. Sanchez; Pim Cuijpers; Simon Gilbody; John P.A. Ioannidis; Lorie A. Kloda; Dean McMillan; Scott B. Patten; Ian Shrier; Russell J. Steele; Roy C. Ziegelstein; Dickens H. Akena; Bruce Arroll; Liat Ayalon; Hamid R. Baradaran; Murray Baron; Anna Beraldi; Charles H. Bombardier; Peter Butterworth; Gregory Carter; Marcos H. Chagas; Juliana C.N. Chan; Rushina Cholera; Neerja Chowdhary; Kerrie Clover; Yeates Conwell; Janneke M. De Man-Van Ginkel; Jaime Delgadillo; Jesse R. Fann; Felix H. Fischer; Benjamin Fischler; Daniel Fung; Bizu Gelaye; Felicity Goodyear-Smith; Catherine G. Greeno; Brian J. Hall; John Hambridge; Patricia A. Harrison; Ulrich Hegerl; Leanne Hides; Stevan E. Hobfoll; Marie Hudson; Thomas Hyphantis; Masatoshi Inagaki; Khalida Ismail; Nathalie Jetté; Mohammad E. Khamseh; Kim M. Kiely; Femke Lamers; Shen Ing Liu; Manote Lotrakul; Sonia R. Loureiro; Bernd Löwe; Laura Marsh; Anthony McGuire; Sherina Mohd Sidik; Tiago N. Munhoz; Kumiko Muramatsu; Flávia L. Osório; Vikram Patel; Brian W. Pence; Philippe Persoons; Angelo Picardi; Alasdair G. Rooney; Iná S. Santos; Juwita Shaaban; Abbey Sidebottom; Adam Simning; Lesley Stafford; Sharon Sung; Pei Lin Lynnette Tan; Alyna Turner; Christina M. Van Der Feltz-Cornelis; Henk C. Van Weert; Paul A. Vöhringer; Jennifer White; Mary A. Whooley; Kirsty Winkley; Mitsuhiko Yamada; Yuying Zhang; Brett D. Thombs; Melbourne School of Psychological Sciences; Melbourne School of Population and Global Health; Amsterdam Public Health; San Francisco VA Health Care System; Mackay Medical College; Calvary Mater Newcastle; Duke-NUS Medical School Singapore; City of Minneapolis; Niigata Seiryo University; Bar-Ilan University School of Social Work; Makerere University; Concordia University; University Medical Center Utrecht; Royal Women's Hospital, Carlton; Harvard School of Public Health; London School of Hygiene & Tropical Medicine; KU Leuven– University Hospital Leuven; University of Queensland; Mackay Memorial Hospital Taiwan; University of Edinburgh; Yong Loo Lin School of Medicine; University of Melbourne; Charité – Universitätsmedizin Berlin; Universiti Putra Malaysia; The University of North Carolina at Chapel Hill; KU Leuven; Iran University of Medical Sciences; Prince of Wales Hospital Hong Kong; University of Rochester Medical Center; University of California, San Francisco; Tufts University; Universidade de Macau; Lady Davis Institute for Medical Research; Okayama University Medical School; Technical University of Munich; Monash University; Deakin University; Rush University Medical Center; National Center of Neurology and Psychiatry Kodaira; University of Newcastle, Faculty of Health and Medicine; University of York; Saint Joseph's College of Maine; Faculty of Medicine, Ramathibodi Hospital, Mahidol University; University of Pittsburgh; University of Washington, Seattle; Universidade Federal de Pelotas; John Hunter Hospital; Stanford University; Universidad de Chile; King's College London; University of Newcastle, Australia; Istituto Superiore Di Sanita; Singapore Institute of Mental Health; Australian National University; Vrije Universiteit Amsterdam; Universidade de Sao Paulo - USP; Centre universitaire de santé McGill; Johns Hopkins Bloomberg School of Public Health; University of Auckland; Nanyang Technological University; Universitätsklinikum Hamburg-Eppendorf und Medizinische Fakultät; Panepistimion Ioanninon; Universitätsklinikum Leipzig und Medizinische Fakultät; Chinese University of Hong Kong; Harvard Medical School; School of Medical Sciences - Universiti Sains Malaysia; McGill University; Tan Tock Seng Hospital; Baylor College of Medicine; University of Calgary; TRANZO Scientific Center for Care and Wellbeing; University of Sheffield; Amsterdam UMC - University of Amsterdam; The Johns Hopkins School of Medicine; Clinical Psychiatrist; National Institute of Science and Technology; Clinical Centre of Excellence for Body; Allina Health; Public Health Foundation of India; Ministry of Economy; Schön Klinik Hamburg Eilbek
    Copyright © 2018 The Royal College of Psychiatrists. Background: Different diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification. Aims: To evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics. Method: Data collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit. Results: A total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15-3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98-10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7-15) (OR = 0.96; 95% CI = 0.56-1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26-0.97). Conclusions: The MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.
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    Selective cutoff reporting in studies of the accuracy of the Patient Health Questionnaire-9 and Edinburgh Postnatal Depression Scale: Comparison of results based on published cutoffs versus all cutoffs using individual participant data meta-analysis
    (2021-09-01) Dipika Neupane; Brooke Levis; Parash M. Bhandari; Brett D. Thombs; Andrea Benedetti; Ying Sun; Chen He; Yin Wu; Ankur Krishnan; Zelalem Negeri; Mahrukh Imran; Danielle B. Rice; Kira E. Riehm; Nazanin Saadat; Marleine Azar; Tatiana A. Sanchez; Matthew J. Chiovitti; Alexander W. Levis; Jill T. Boruff; Pim Cuijpers; Simon Gilbody; John P.A. Ioannidis; Lorie A. Kloda; Scott B. Patten; Ian Shrier; Roy C. Ziegelstein; Liane Comeau; Nicholas D. Mitchell; Marcello Tonelli; Simone N. Vigod; Dickens H. Akena; Rubén Alvarado; Bruce Arroll; Muideen O. Bakare; Hamid R. Baradaran; Cheryl Tatano Beck; Charles H. Bombardier; Adomas Bunevicius; Gregory Carter; Marcos H. Chagas; Linda H. Chaudron; Rushina Cholera; Kerrie Clover; Yeates Conwell; Tiago Castro e Couto; Janneke M. de Man-van Ginkel; Jaime Delgadillo; Jesse R. Fann; Nicolas Favez; Daniel Fung; Lluïsa Garcia-Esteve; Bizu Gelaye; Felicity Goodyear-Smith; Thomas Hyphantis; Masatoshi Inagaki; Khalida Ismail; Nathalie Jetté; Dina Sami Khalifa; Mohammad E. Khamseh; Jane Kohlhoff; Zoltán Kozinszky; Laima Kusminskas; Shen Ing Liu; Manote Lotrakul; Sonia R. Loureiro; Bernd Löwe; Sherina Mohd Sidik; Sandra Nakić Radoš; Flávia L. Osório; Susan J. Pawlby; Brian W. Pence; Tamsen J. Rochat; Alasdair G. Rooney; Deborah J. Sharp; Lesley Stafford; Kuan Pin Su; Sharon C. Sung; Meri Tadinac; S. Darius Tandon; Pavaani Thiagayson; Annamária Töreki; Anna Torres-Giménez; Alyna Turner; Christina M. van der Feltz-Cornelis; Johann M. Vega-Dienstmaier; Paul A. Vöhringer; Jennifer White; Mary A. Whooley; Kirsty Winkley; Mitsuhiko Yamada; Ramathibodi Hospital; Makerere University College of Health Sciences; School of Medicine; School of Medicine and Public Health; Bristol Medical School; Lietuvos sveikatos mokslų universitetas; Duke-NUS Medical School; Universidad Peruana Cayetano Heredia, Facultad de Medicina Alberto Hurtado; University of New South Wales Faculty of Medicine, School of Psychiatry; University of the Witwatersrand Faculty of Health Sciences; Concordia University; Stanford University School of Medicine; University Medical Center Utrecht; Royal Women's Hospital, Carlton; Harvard T.H. Chan School of Public Health; China Medical University Hospital; University of Alberta; Hospital Clinic Barcelona; McGill Faculty of Medicine and Health Sciences; Faculty of Medicine; Szegedi Tudományegyetem (SZTE); The University of Edinburgh; Danderyds Sjukhus; Universiti Putra Malaysia; The University of North Carolina at Chapel Hill; Iran University of Medical Sciences; University of Rochester Medical Center; University of California, San Francisco; Lady Davis Institute for Medical Research; Monash University; Keele University, School of Medicine; National Center of Neurology and Psychiatry Kodaira; University of Newcastle, Faculty of Health and Medicine; University of Toronto; University of York; University of Washington; Universidade Federal de Uberlândia; Northwestern University Feinberg School of Medicine; University of Rochester School of Medicine and Dentistry; Hospital Clínico Universidad De Chile; King's College London; University of Montreal; Singapore Institute of Mental Health; Shimane University Faculty of Medicine; University of Zagreb; School of Nursing; Universidade de São Paulo; Centre Universitaire de Santé McGill; The University of Auckland; Duke University School of Medicine; Universitätsklinikum Hamburg-Eppendorf; Université de Genève; Medisinske Fakultet; Facultad de Medicina de la Universidad de Chile; Université McGill; Universiteit van Amsterdam; University of Calgary; The University of Sheffield; Johns Hopkins School of Medicine; Cumming School of Medicine; Private Practice; Federal Neuropsychiatric Hospital; Catholic University of Croatia
    Objectives: Selectively reported results from only well-performing cutoffs in diagnostic accuracy studies may bias estimates in meta-analyses. We investigated cutoff reporting patterns for the Patient Health Questionnaire-9 (PHQ-9; standard cutoff 10) and Edinburgh Postnatal Depression Scale (EPDS; no standard cutoff, commonly used 10–13) and compared accuracy estimates based on published cutoffs versus all cutoffs. Methods: We conducted bivariate random effects meta-analyses using individual participant data to compare accuracy from published versus all cutoffs. Results: For the PHQ-9 (30 studies, N = 11,773), published results underestimated sensitivity for cutoffs below 10 (median difference: −0.06) and overestimated for cutoffs above 10 (median difference: 0.07). EPDS (19 studies, N = 3637) sensitivity estimates from published results were similar for cutoffs below 10 (median difference: 0.00) but higher for cutoffs above 13 (median difference: 0.14). Specificity estimates from published and all cutoffs were similar for both tools. The mean cutoff of all reported cutoffs in PHQ-9 studies with optimal cutoff below 10 was 8.8 compared to 11.8 for those with optimal cutoffs above 10. Mean for EPDS studies with optimal cutoffs below 10 was 9.9 compared to 11.8 for those with optimal cutoffs greater than 10. Conclusion: Selective cutoff reporting was more pronounced for the PHQ-9 than EPDS.