Meta Analysis

Meta-analysis is a statistical technique used to synthesize results from multiple independent studies, providing a more robust and precise understanding of a research question than any single study alone. Current research focuses on improving meta-analysis methods, particularly by incorporating "untrusted" data (e.g., observational studies) and leveraging machine learning techniques, including convolutional neural networks (CNNs), support vector machines (SVMs), and random forests, to automate the process and handle data heterogeneity. This approach is significantly impacting various fields, from healthcare (e.g., predicting adverse drug reactions, diagnosing diseases) to software engineering (e.g., software defect prediction) and beyond, by enhancing the reliability and efficiency of evidence synthesis.