A Bivariate Finite Mixture Random Effects Model for Identifying and Accommodating Outliers in Diagnostic Test Accuracy Meta-Analyses.

Outlying studies are prevalent in meta-analyses of diagnostic test accuracy studies and may lead to misleading inferences and decision-making unless their negative effect is appropriately dealt with. Statistical methods for detecting and down-weighting the impact of such studies have recently gained the attention of many researchers. However, these methods dichotomize each study in the meta-analysis as outlying or non-outlying and focus on examining the effect of outlying studies on the summary sensitivity and specificity only. We developed and evaluated a robust and flexible random-effects bivariate finite mixture model for meta-analyzing diagnostic test accuracy studies. The proposed model accounts for both the within- and across-study heterogeneity in diagnostic test results, generates the probability that each study in a meta-analysis is outlying instead of dichotomizing the status of the studies, and allows assessing the impact of outlying studies on the pooled sensitivity, pooled specificity, and between-study heterogeneity. Our simulation study and real-life data examples demonstrated that the proposed model was robust to the existence of outlying studies, produced precise point and interval estimates of the pooled sensitivity and specificity, and yielded similar results to the standard models when there were no outliers. Extensive simulations demonstrated relatively better bias and confidence interval width, but comparable root mean squared error and lesser coverage probability of the proposed model. Practitioners can use our proposed model as a stand-alone model to conduct a meta-analysis of diagnostic test accuracy studies or as an alternative sensitivity analysis model when outlying studies are present in a meta-analysis.