Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses

Music City Center 

Meta-analysis is a widely used tool for synthesizing results from multiple studies. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after removing the identified outlier and provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

Meta-analysis

heterogeneity

iterative method

outlier

sensitivity analysis 

Biometrics Section