Article type
Year
Abstract
Background: 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.
Objective: 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.
Method: MEDLINE, EMBASE, and CINAHL for relevant studies; routine data 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: 1) Mammography in the NHS Breast Screening Program; 2) PHQ-9 questionnaire to screen for depression in general practice; 3) Centor’s criteria used to diagnose group A beta-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. Baseline study characteristics, quality items and 2x2 tables completed.
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 to 57.9) and 19.8 (12.8 to 30.9) respectively for mammography; and 4.89 (2.02 to 11.8) and 2.35 (1.51 to 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.
Objective: 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.
Method: MEDLINE, EMBASE, and CINAHL for relevant studies; routine data 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: 1) Mammography in the NHS Breast Screening Program; 2) PHQ-9 questionnaire to screen for depression in general practice; 3) Centor’s criteria used to diagnose group A beta-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. Baseline study characteristics, quality items and 2x2 tables completed.
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 to 57.9) and 19.8 (12.8 to 30.9) respectively for mammography; and 4.89 (2.02 to 11.8) and 2.35 (1.51 to 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.