Literature DB >> 27060912

Sample sizes and precision of estimates of sensitivity and specificity from primary studies on the diagnostic accuracy of depression screening tools: a survey of recently published studies.

Brett D Thombs1,2,3,4,5,6,7, Danielle B Rice1,4.   

Abstract

Depression screening tools are useful to the extent that they accurately discriminate between depressed and non-depressed patients. Studies without enough patients to generate precise estimates make it difficult to evaluate accuracy. We conducted a survey of recently published studies on depression screening tool accuracy to evaluate the percentage with sample size calculations; the percentage that provided confidence intervals; and precision, based on the width and lower bounds of 95% confidence intervals for sensitivity and specificity. We calculated 95% confidence intervals, if possible, when not provided. Only three of 89 studies (3%) described a viable sample size calculation. Only 30 studies (34%) provided reasonably accurate confidence intervals. Of 86 studies where 95% confidence intervals were provided or could be calculated, only seven (8%) had interval widths for sensitivity of ≤ 10%, whereas 53 (62%) had widths of ≥ 21%. Lower bounds of confidence intervals were < 80% for 84% of studies for sensitivity and 66% of studies for specificity. Overall, few studies on the diagnostic accuracy of depression screening tools reported sample size calculations, and the number of patients in most studies was too small to generate reasonably precise accuracy estimates. The failure to provide confidence intervals in published reports may obscure these shortcomings.
Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Entities:  

Keywords:  depression; diagnostic test accuracy; sample size

Mesh:

Year:  2016        PMID: 27060912      PMCID: PMC6877233          DOI: 10.1002/mpr.1504

Source DB:  PubMed          Journal:  Int J Methods Psychiatr Res        ISSN: 1049-8931            Impact factor:   4.035


  33 in total

1.  Sample size calculation should be performed for design accuracy in diagnostic test studies.

Authors:  Antoine Flahault; Michel Cadilhac; Guy Thomas
Journal:  J Clin Epidemiol       Date:  2005-08       Impact factor: 6.437

Review 2.  Should we screen for depression?

Authors:  Simon Gilbody; Trevor Sheldon; Simon Wessely
Journal:  BMJ       Date:  2006-04-29

3.  Bias in sensitivity and specificity caused by data-driven selection of optimal cutoff values: mechanisms, magnitude, and solutions.

Authors:  Mariska M G Leeflang; Karel G M Moons; Johannes B Reitsma; Aielko H Zwinderman
Journal:  Clin Chem       Date:  2008-02-07       Impact factor: 8.327

Review 4.  Reporting quality of diagnostic accuracy studies: a systematic review and meta-analysis of investigations on adherence to STARD.

Authors:  Daniël A Korevaar; W Annefloor van Enst; René Spijker; Patrick M M Bossuyt; Lotty Hooft
Journal:  Evid Based Med       Date:  2013-12-24

Review 5.  Meta-epidemiologic analysis indicates that MEDLINE searches are sufficient for diagnostic test accuracy systematic reviews.

Authors:  Wynanda A van Enst; Rob J P M Scholten; Penny Whiting; Aeilko H Zwinderman; Lotty Hooft
Journal:  J Clin Epidemiol       Date:  2014-07-02       Impact factor: 6.437

6.  The PHQ-9: validity of a brief depression severity measure.

Authors:  K Kroenke; R L Spitzer; J B Williams
Journal:  J Gen Intern Med       Date:  2001-09       Impact factor: 5.128

7.  Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis.

Authors:  Laura Manea; Simon Gilbody; Dean McMillan
Journal:  CMAJ       Date:  2011-12-19       Impact factor: 8.262

8.  Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire.

Authors:  R L Spitzer; K Kroenke; J B Williams
Journal:  JAMA       Date:  1999-11-10       Impact factor: 56.272

Review 9.  Risk of bias from inclusion of patients who already have diagnosis of or are undergoing treatment for depression in diagnostic accuracy studies of screening tools for depression: systematic review.

Authors:  Brett D Thombs; Erin Arthurs; Ghassan El-Baalbaki; Anna Meijer; Roy C Ziegelstein; Russell J Steele
Journal:  BMJ       Date:  2011-08-18

Review 10.  Depression screening and patient outcomes in cancer: a systematic review.

Authors:  Anna Meijer; Michelle Roseman; Katherine Milette; James C Coyne; Michael E Stefanek; Roy C Ziegelstein; Erin Arthurs; Allison Leavens; Steven C Palmer; Donna E Stewart; Peter de Jonge; Brett D Thombs
Journal:  PLoS One       Date:  2011-11-14       Impact factor: 3.240

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  5 in total

1.  Reducing Waste and Increasing the Usability of Psychiatry Research: The Family of EQUATOR Reporting Guidelines and One of Its Newest Members: The PRISMA-DTA Statement.

Authors:  Brett D Thombs; Brooke Levis; Danielle B Rice; Yin Wu; Andrea Benedetti
Journal:  Can J Psychiatry       Date:  2018-04-25       Impact factor: 4.356

Review 2.  Depression in people with type 2 diabetes: current perspectives.

Authors:  Lina Darwish; Erika Beroncal; Ma Veronica Sison; Walter Swardfager
Journal:  Diabetes Metab Syndr Obes       Date:  2018-07-10       Impact factor: 3.168

3.  Targeted test evaluation: a framework for designing diagnostic accuracy studies with clear study hypotheses.

Authors:  Daniël A Korevaar; Gowri Gopalakrishna; Jérémie F Cohen; Patrick M Bossuyt
Journal:  Diagn Progn Res       Date:  2019-12-19

4.  Sample size and precision of estimates in studies of depression screening tool accuracy: A meta-research review of studies published in 2018-2021.

Authors:  Elsa-Lynn Nassar; Brooke Levis; Marieke A Neyer; Danielle B Rice; Linda Booij; Andrea Benedetti; Brett D Thombs
Journal:  Int J Methods Psychiatr Res       Date:  2022-04-01       Impact factor: 4.182

5.  Diagnostic accuracy of the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) for detecting major depression: protocol for a systematic review and individual patient data meta-analyses.

Authors:  Brett D Thombs; Andrea Benedetti; Lorie A Kloda; Brooke Levis; Marleine Azar; Kira E Riehm; Nazanin Saadat; Pim Cuijpers; Simon Gilbody; John P A Ioannidis; Dean McMillan; Scott B Patten; Ian Shrier; Russell J Steele; Roy C Ziegelstein; Carmen G Loiselle; Melissa Henry; Zahinoor Ismail; Nicholas Mitchell; Marcello Tonelli
Journal:  BMJ Open       Date:  2016-04-13       Impact factor: 2.692

  5 in total

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