Evaluation of a new version of I² with emphasis on diagnostic problems

Abstract

This paper describes results stemming from an in-depth analysis of Higgins' measure of heterogeneity for a meta-analysis applied in the context of meta-analysis for diagnostic problems. Higgins measure of heterogeneity I 2 has been criticized for being confounded by the study-specific sample size, in the sense that different I 2-values can be achieved for the same value of across-study variance if only the study-specific variance is varying enough. In particular, I 2 approaches one for any value of the heterogeneity variance (variance across studies) if the within-study variance becomes large. The paper proposes a measure which is unconfounded by sample size. It is essentially a philosophical question which heterogeneity measure is chosen. Nevertheless, a detailed simulation study has been launched and the results indicate that the newly suggest measure of heterogeneity has beneficial statistical properties. Both measures are also exemplified at hand of some meta-analytic case studies.

Clinical validity and utility of circulating tumor DNA (ctDNA) testing in advanced non-small cell lung cancer (aNSCLC): a systematic literature review and meta-analysis

“…36,37 The bivariate random-effects model proposed by Reitsma et al (2005) utilizes the standard frequentist approach and was used in our study to obtain pooled estimates for sensitivity and specificity. [38][39][40] Meta-analysis results were also presented with summary receiver operating characteristics (SROC) curves and 95% confidence regions for the pooled sensitivity and specificity estimates. In addition, as a sensitivity analysis, a Bayesian bivariate randomeffects meta-analysis was performed to avoid normal approximations of the likelihood and to obtain predictive distributions of the sensitivity and specificity to predict results in a new study.…”

Clinical tests for confirming tracheal intubation or excluding oesophageal intubation: a diagnostic test accuracy systematic review and meta‐analysis

“…We drew forest plots pairing sensitivity and specificity with 95%CI for each study in Review Manager. We estimated I 2 statistics to quantify heterogeneity using the mada package's Holling sample size adjusted approach [14], assuming 30–60% to be moderate, 50–90% substantial and 75–100% considerable heterogeneity [15]. We did not perform comparisons between tests.…”

“…Manager. We estimated I 2 statistics to quantify heterogeneity using the mada package's Holling sample size adjusted approach [14], assuming 30-60% to be moderate, 50-90% substantial and 75-100% considerable heterogeneity [15].…”

Systematic review and meta-analysis of the diagnostic value of four biomarkers in detecting neonatal sepsis in low- and middle-income countries

“…We also calculated Holling’s sample size adjusted measure for heterogeneity (I 2 ) which was developed for use in bivariate meta-analyses of diagnostic accuracy. 19 We calculated the sensitivity, specificity, and the area under the curve (AUC) along with their respective 95% CIs, for each disease and biomarker combination. 95% CIs for AUCs were calculated through bootstrapping with 2000 resamplings via the AUC boot function in the dmetatools R package created by Noma H. 20 We calculated and reported the highest Youden’s index for each biomarker and disease combination.…”