Abstract
Background: Meta-analysis may produce estimates that are unrepresentative of a test’s performance in practice. Tailored meta-analysis circumvents this by deriving an applicable region for the practice and selecting the studies compatible with the region. It requires the test positive rate, r and prevalence, p being estimated for the setting but previous studies have assumed their independence.
Objective: The aim is to investigate the effects a correlation between test positive rate and prevalence has on estimating the applicable region and how this affects tailored meta-analysis.
Method: Four methods for estimating 99% confidence intervals for r and p were investigated: Wilson’s score, Clopper-Pearson’s exact interval, the Bonferroni correction and Hotelling’s T2 statistic. These were analysed in terms of the coverage probability using simulation trials over different correlations, sample sizes, and values for r and p. The methods were then applied to two published meta-analyses with associated practice data and the effects on the applicable region, studies selected and summary estimates evaluated.
Results: Hotelling’s T2 statistic with a continuity correction had the highest median coverage (0.9971). This and the Clopper-Pearson method with a Bonferroni correction both had coverage consistently above 0.99. The coverage of Hotelling’s T2 statistic intervals varied the least across different correlations. For both meta-analyses, the number of studies selected was largest when Hotelling’s T2 statistic was used to derive the applicable region. In one instance this increased the sensitivity by over 4% compared with tailored meta-analysis estimates using other methods.
Conclusion: Tailored meta-analysis returns estimates which are tailored to practice providing the applicable region is accurately defined. This is most likely when the 99% confidence interval for test positive rate and prevalence are estimated using Hotelling’s T2 statistic with a continuity correction. Potentially, the applicable region may be obtained using routine electronic health data.
Objective: The aim is to investigate the effects a correlation between test positive rate and prevalence has on estimating the applicable region and how this affects tailored meta-analysis.
Method: Four methods for estimating 99% confidence intervals for r and p were investigated: Wilson’s score, Clopper-Pearson’s exact interval, the Bonferroni correction and Hotelling’s T2 statistic. These were analysed in terms of the coverage probability using simulation trials over different correlations, sample sizes, and values for r and p. The methods were then applied to two published meta-analyses with associated practice data and the effects on the applicable region, studies selected and summary estimates evaluated.
Results: Hotelling’s T2 statistic with a continuity correction had the highest median coverage (0.9971). This and the Clopper-Pearson method with a Bonferroni correction both had coverage consistently above 0.99. The coverage of Hotelling’s T2 statistic intervals varied the least across different correlations. For both meta-analyses, the number of studies selected was largest when Hotelling’s T2 statistic was used to derive the applicable region. In one instance this increased the sensitivity by over 4% compared with tailored meta-analysis estimates using other methods.
Conclusion: Tailored meta-analysis returns estimates which are tailored to practice providing the applicable region is accurately defined. This is most likely when the 99% confidence interval for test positive rate and prevalence are estimated using Hotelling’s T2 statistic with a continuity correction. Potentially, the applicable region may be obtained using routine electronic health data.
| Original language | English |
|---|---|
| Article number | O37 |
| Journal | Diagnostic and Prognostic Research |
| Volume | 2 (Supplement 1) |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2 Jul 2018 |