A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well
Issued Date
2023-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85161208731
Pubmed ID
37286580
Journal Title
Scientific Reports
Volume
13
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.13 No.1 (2023)
Suggested Citation
de Lara A.L.M.V., Bhandari P.M., Wu Y., Levis B., Thombs B., Benedetti A., Sun Y., He C., Krishnan A., Neupane D., Negeri Z., Imran M., Rice D.B., Riehm K.E., Saadat N., Azar M., Boruff J., Cuijpers P., Gilbody S., Ioannidis J.P.A., Kloda L.A., McMillan D., Patten S.B., Shrier I., Ziegelstein R.C., Akena D.H., Arroll B., Ayalon L., Baradaran H.R., Beraldi A., Bombardier C.H., Butterworth P., Carter G., Chagas M.H., Chan J.C.N., Cholera R., Chowdhary N., Clover K., Conwell Y., de Man-van Ginkel J.M., Delgadillo J., Fann J.R., Fischer F.H., Fung D., Gelaye B., Goodyear-Smith F., Greeno C.G., Hall B.J., Härter M., Hegerl U., Hides L., Hobfoll S.E., Hudson M., Hyphantis T., Inagaki M., Ismail K., Jetté N., Khamseh M.E., Kiely K.M., Kwan Y., Lamers F., Liu S.I., Lotrakul M., Loureiro S.R., Löwe B., Marsh L., McGuire A., Mohd Sidik S., Munhoz T.N., Muramatsu K., Osório F.L., Patel V., Pence B.W., Persoons P., Picardi A., Reuter K., Rooney A.G., Santos I.S., Shaaban J., Sidebottom A., Simning A., Stafford L., Sung S.C., Tan P.L.L., Turner A., van der Feltz-Cornelis C.M., van Weert H.C., Vöhringer P.A., White J., Whooley M.A., Winkley K., Yamada M., Zhang Y. A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well. Scientific Reports Vol.13 No.1 (2023). doi:10.1038/s41598-023-36129-w Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/83055
Title
A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well
Author(s)
de Lara A.L.M.V.
Bhandari P.M.
Wu Y.
Levis B.
Thombs B.
Benedetti A.
Sun Y.
He C.
Krishnan A.
Neupane D.
Negeri Z.
Imran M.
Rice D.B.
Riehm K.E.
Saadat N.
Azar M.
Boruff J.
Cuijpers P.
Gilbody S.
Ioannidis J.P.A.
Kloda L.A.
McMillan D.
Patten S.B.
Shrier I.
Ziegelstein R.C.
Akena D.H.
Arroll B.
Ayalon L.
Baradaran H.R.
Beraldi A.
Bombardier C.H.
Butterworth P.
Carter G.
Chagas M.H.
Chan J.C.N.
Cholera R.
Chowdhary N.
Clover K.
Conwell Y.
de Man-van Ginkel J.M.
Delgadillo J.
Fann J.R.
Fischer F.H.
Fung D.
Gelaye B.
Goodyear-Smith F.
Greeno C.G.
Hall B.J.
Härter M.
Hegerl U.
Hides L.
Hobfoll S.E.
Hudson M.
Hyphantis T.
Inagaki M.
Ismail K.
Jetté N.
Khamseh M.E.
Kiely K.M.
Kwan Y.
Lamers F.
Liu S.I.
Lotrakul M.
Loureiro S.R.
Löwe B.
Marsh L.
McGuire A.
Mohd Sidik S.
Munhoz T.N.
Muramatsu K.
Osório F.L.
Patel V.
Pence B.W.
Persoons P.
Picardi A.
Reuter K.
Rooney A.G.
Santos I.S.
Shaaban J.
Sidebottom A.
Simning A.
Stafford L.
Sung S.C.
Tan P.L.L.
Turner A.
van der Feltz-Cornelis C.M.
van Weert H.C.
Vöhringer P.A.
White J.
Whooley M.A.
Winkley K.
Yamada M.
Zhang Y.
Bhandari P.M.
Wu Y.
Levis B.
Thombs B.
Benedetti A.
Sun Y.
He C.
Krishnan A.
Neupane D.
Negeri Z.
Imran M.
Rice D.B.
Riehm K.E.
Saadat N.
Azar M.
Boruff J.
Cuijpers P.
Gilbody S.
Ioannidis J.P.A.
Kloda L.A.
McMillan D.
Patten S.B.
Shrier I.
Ziegelstein R.C.
Akena D.H.
Arroll B.
Ayalon L.
Baradaran H.R.
Beraldi A.
Bombardier C.H.
Butterworth P.
Carter G.
Chagas M.H.
Chan J.C.N.
Cholera R.
Chowdhary N.
Clover K.
Conwell Y.
de Man-van Ginkel J.M.
Delgadillo J.
Fann J.R.
Fischer F.H.
Fung D.
Gelaye B.
Goodyear-Smith F.
Greeno C.G.
Hall B.J.
Härter M.
Hegerl U.
Hides L.
Hobfoll S.E.
Hudson M.
Hyphantis T.
Inagaki M.
Ismail K.
Jetté N.
Khamseh M.E.
Kiely K.M.
Kwan Y.
Lamers F.
Liu S.I.
Lotrakul M.
Loureiro S.R.
Löwe B.
Marsh L.
McGuire A.
Mohd Sidik S.
Munhoz T.N.
Muramatsu K.
Osório F.L.
Patel V.
Pence B.W.
Persoons P.
Picardi A.
Reuter K.
Rooney A.G.
Santos I.S.
Shaaban J.
Sidebottom A.
Simning A.
Stafford L.
Sung S.C.
Tan P.L.L.
Turner A.
van der Feltz-Cornelis C.M.
van Weert H.C.
Vöhringer P.A.
White J.
Whooley M.A.
Winkley K.
Yamada M.
Zhang Y.
Author's Affiliation
Faculty of Medicine and Health Sciences
Ramathibodi Hospital
Makerere University College of Health Sciences
School of Medicine
School of Medicine and Public Health
L'Institut de Recherche du Centre Universitaire de Santé McGill
NYU Shanghai
Duke-NUS Medical School
Niigata Seiryo University
University of South Florida Health
Concordia University
Stanford University School of Medicine
Harvard T.H. Chan School of Public Health
KU Leuven– University Hospital Leuven
The University of Queensland
Keele University
McGill Faculty of Medicine and Health Sciences
Faculty of Medicine
UNSW Sydney
The University of Edinburgh
Charité – Universitätsmedizin Berlin
The University of North Carolina at Chapel Hill
Iran University of Medical Sciences
Prince of Wales Hospital Hong Kong
University of Rochester Medical Center
University of California, San Francisco
Lady Davis Institute for Medical Research
Technische Universität München
Monash University
National Center of Neurology and Psychiatry Kodaira
University of Newcastle, Faculty of Health and Medicine
University of York
Leids Universitair Medisch Centrum
University of Pittsburgh
University of Washington
Universidade Federal de Pelotas
Icahn School of Medicine at Mount Sinai
Hospital Clínico Universidad De Chile
King's College London
Istituto Superiore Di Sanita
Singapore Institute of Mental Health
The Australian National University
Shimane University Faculty of Medicine
Vrije Universiteit Amsterdam
Universidade de São Paulo
Goethe-Universität Frankfurt am Main
Centre Universitaire de Santé McGill
The University of Auckland
Duke University School of Medicine
Universitätsklinikum Hamburg-Eppendorf
Bar-Ilan University
Harvard Medical School
School of Medical Sciences, Universiti Sains Malaysia
Université McGill
Tan Tock Seng Hospital
Baylor College of Medicine
The University of Sheffield
Johns Hopkins School of Medicine
Cumming School of Medicine
Allina Health
Private Practice for Psychotherapy and Psycho-oncology
STAR-Stress
Clinical Psychiatrist
The Royal Women’s Hospital
Amsterdam Medical Centers
Ramathibodi Hospital
Makerere University College of Health Sciences
School of Medicine
School of Medicine and Public Health
L'Institut de Recherche du Centre Universitaire de Santé McGill
NYU Shanghai
Duke-NUS Medical School
Niigata Seiryo University
University of South Florida Health
Concordia University
Stanford University School of Medicine
Harvard T.H. Chan School of Public Health
KU Leuven– University Hospital Leuven
The University of Queensland
Keele University
McGill Faculty of Medicine and Health Sciences
Faculty of Medicine
UNSW Sydney
The University of Edinburgh
Charité – Universitätsmedizin Berlin
The University of North Carolina at Chapel Hill
Iran University of Medical Sciences
Prince of Wales Hospital Hong Kong
University of Rochester Medical Center
University of California, San Francisco
Lady Davis Institute for Medical Research
Technische Universität München
Monash University
National Center of Neurology and Psychiatry Kodaira
University of Newcastle, Faculty of Health and Medicine
University of York
Leids Universitair Medisch Centrum
University of Pittsburgh
University of Washington
Universidade Federal de Pelotas
Icahn School of Medicine at Mount Sinai
Hospital Clínico Universidad De Chile
King's College London
Istituto Superiore Di Sanita
Singapore Institute of Mental Health
The Australian National University
Shimane University Faculty of Medicine
Vrije Universiteit Amsterdam
Universidade de São Paulo
Goethe-Universität Frankfurt am Main
Centre Universitaire de Santé McGill
The University of Auckland
Duke University School of Medicine
Universitätsklinikum Hamburg-Eppendorf
Bar-Ilan University
Harvard Medical School
School of Medical Sciences, Universiti Sains Malaysia
Université McGill
Tan Tock Seng Hospital
Baylor College of Medicine
The University of Sheffield
Johns Hopkins School of Medicine
Cumming School of Medicine
Allina Health
Private Practice for Psychotherapy and Psycho-oncology
STAR-Stress
Clinical Psychiatrist
The Royal Women’s Hospital
Amsterdam Medical Centers
Other Contributor(s)
Abstract
The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings.