Brian H Willis1, Christopher J Hyde2. 1. School of Health and Population Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. Electronic address: b.h.willis@bham.ac.uk. 2. Institute of Health Research, University of Exeter Medical School, The Veysey Building, Salmon Pool lane, Exeter, EX2 4SG, UK.
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.
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.
Authors: Karoline Freeman; Brian H Willis; Ronan Ryan; Sian Taylor-Phillips; Aileen Clarke Journal: BMC Med Res Methodol Date: 2022-07-12 Impact factor: 4.612
Authors: Richard D Riley; Ikhlaaq Ahmed; Thomas P A Debray; Brian H Willis; J Pieter Noordzij; Julian P T Higgins; Jonathan J Deeks Journal: Stat Med Date: 2015-03-20 Impact factor: 2.373