Synthesis in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 37% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 1 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
7 authors at 20 institutions in 21 countries.
Aurelio López Malo Vázquez de LaraLady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
Parash Mani BhandariLady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
Yin WuLady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
Brooke LevisLady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
Brett ThombsLady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
Andrea BenedettiDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. andrea.benedetti@mcgill.ca.
DEPRESsion Screening Data (DEPRESSD) PHQ-9 Collaboration
Jewish General Hospital · CAUniversidade de São Paulo · BRUniversity of Newcastle Australia · AUUniversity of York · GBAmsterdam University Medical Centers · NLChinese University of Hong Kong · HKDuke-NUS Medical School · SGHarvard University · USIran University of Medical Sciences · IRKing's College London · GBTan Tock Seng Hospital · SGUniversidade Federal de Pelotas · BRUniversität Hamburg · DEUniversity of Auckland · NZUniversity of Rochester Medical Center · USUniversity of Washington · USAllina Health · USAustralian National University · AUBar-Ilan University · ILCharité - Universitätsmedizin Berlin · DE
Funding
Obesity and Energy Metabolism CoreP30DK050456 · NIDDK · UNIVERSITY OF MINNESOTA TWIN CITIES · PI BERNLOHR, DAVID A · 1995 to 2015
$17.0M
University of Rochester Mentoring Environment: Nurturing Training Opportunities in Research (UR-MENTOR)T32GM007356 · NIGMS · UNIVERSITY OF ROCHESTER · PI O'BANION, M. KERRY · 1985 to 2023
$15.1M
CDC Injury Control Research Center for Preventing Suicide (CDC ICRC S)R49CE002093 · CE · UNIVERSITY OF ROCHESTER · PI CAINE, ERIC D. · 2012 to 2018
$5.6M
Quality and location of presented Ag as determinants of tumor-specific immunityR01CA078400 · NCI · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI ENGELHARD, VICTOR H · 1998 to 2012
$4.4M
Epidemiology of Depression and Heart Failure in AgingR01HL079235 · NHLBI · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WHOOLEY, MARY A · 2005 to 2009
$4.2M
The University of Rochester's Clinical and Translational Science InstituteTL1RR024135 · NCRR · UNIVERSITY OF ROCHESTER · PI PEARSON, THOMAS ARTHUR · 2006 to 2011
$3.7M
University of Washington MHIRT Training Program GrantT37MD001449 · NIMHD · UNIVERSITY OF WASHINGTON · PI WILLIAMS, MICHELLE A. · 2005 to 2018
$3.5M
CONTROLLED TRIAL OF SERTRALINE FOR DEPRESSION AFTER TBIR01HD039415 · NICHD · UNIVERSITY OF WASHINGTON · PI BOMBARDIER, CHARLES HENRY · 2000 to 2004
$2.6M
IP-RISP: The Senior Health and Research AllianceR24MH071604 · NIMH · UNIVERSITY OF ROCHESTER · PI CONWELL, YEATES · 2006 to 2010
$2.5M
Methods of Optimal Depression Detection in Parkinson'sR01MH069666 · NIMH · JOHNS HOPKINS UNIVERSITY · PI MARSH, LAURA · 2004 to 2009
$2.3M
Terrorism and Traumatic Responding: Exposure and Resiliency FactorsR01MH073687 · NIMH · RUSH UNIVERSITY MEDICAL CENTER · PI HOBFOLL, STEVAN · 2006 to 2009
$2.2M
THE TREATMENT OF EATING DISORDERSK02MH065919 · NIMH · UNIVERSITY OF MINNESOTA TWIN CITIES · PI CROW, SCOTT J · 2003 to 2007
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.
Indexed as
Major Depressive DisorderData CollectionHumansSeizuresSensitivity and Specificity
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well. · full record | OpenQuestion