Abstract
Background and ObjectiveMeta-analysis may produce estimates that are unrepresentative of a test's performance in practice. Tailored meta-analysis (TMA) 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. The aim is to investigate the effects a correlation between r and p has on estimating the applicable region and how this affects TMA. MethodsSix methods for estimating 99% confidence intervals (CI) for r and p were investigated: Wilson's ± Bonferroni correction, Clopper-Pearson's ± Bonferroni correction, and Hotelling's T2 statistic ± continuity correction. These were analyzed 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 were evaluated. ResultsHotelling'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 CI's 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 TMA estimates using other methods. ConclusionTMA returns estimates that are tailored to practice providing the applicable region is accurately defined. This is most likely when the CI for r and p are estimated using Hotelling's T2 statistic with a continuity correction. Potentially, the applicable region may be obtained using routine electronic health data.
Highlights
It is of interest to policy makers and clinicians to ensure that the results of diagnostic tests from studies can be applied to a particular clinical setting
The coverage of Hotelling’s CI’s 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. This increased the sensitivity by over 4% compared with Tailored meta-analysis (TMA) estimates using other methods
The aim of this study is to investigate the effects the correlation between the test positive rate and prevalence has on estimating the applicable region and how this affects tailored meta-analysis
Summary
It is of interest to policy makers and clinicians to ensure that the results of diagnostic tests from studies can be applied to a particular clinical setting. A diagnostic test’s performance may be measured using several metricsdsensitivity, specificity, positive, or negative likelihood ratios [1,2]. These metrics are influenced by many external factors such as disease prevalence, patient spectrum, test threshold, and reliability that sometimes change across different settings [3e7]. Tailored meta-analysis (TMA) 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.
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