What is the test's accuracy in my practice population? Tailored meta-analysis provides a plausible estimate

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • University of Exeter

Abstract

Objectives
Diagnostic test accuracy studies and meta-analyses may, in some cases, provide estimates that are highly improbable in practice; tailored meta-analysis provides a potential solution. To investigate the utility of tailored meta-analysis in synthesizing estimates of a test's accuracy compared with conventional meta-analysis for three case examples.

Study Design and Setting
MEDLINE, Embase, and CINAHL were searched for relevant studies, and routine data were collected on the test positive rate and disease prevalence from the case settings to define an applicable region for each setting. Three cases were evaluated: mammography in the NHS Breast Screening Programme, Patient Health Questionnaire-9 to screen for depression in general practice, and Centor's criteria used to diagnose group A β-hemolytic streptococcus in general practice. For conventional meta-analysis, studies were selected using standard systematic review methods; for tailored meta-analysis, this selection was refined to those with results compatible with the applicable region for the setting.

Results
In each example, studies were excluded as a result of incorporating an applicable region for the setting. Comparing tailored with conventional meta-analysis, the positive likelihood ratios (with 95% confidence intervals in brackets) were 36.5 (23.0, 57.9) and 19.8 (12.8, 30.9), respectively, for mammography and 4.89 (2.02, 11.8) and 2.35 (1.51, 3.67), respectively, for Centor's criteria. This had the effect of increasing the positive predictive value from 17% to 27% for mammography and 23% to 38% for Centor's criteria.

Conclusion
Tailored meta-analysis has the potential to provide a plausible estimate for a test's accuracy, which is specific to the practice setting. When compared with conventional meta-analysis, the difference may, in some cases, be sufficient to lead to different decisions on patient management.

Details

Original languageEnglish
Pages (from-to)847–854
JournalJournal of Clinical Epidemiology
Volume68
Issue number8
Early online date18 Oct 2014
Publication statusPublished - Aug 2015

Keywords

  • Data Interpretation, Statistical, Decision making, Diagnosis tests, routine, Mass screening, Meta-analysis, Models, statistical