Improving the Statistical Rigor of Healthcare Data Integration
Wednesday, January 26, 2022
2:30 PM-3:30 PM
This upcoming Department of Epidemiology and Biostatistics Seminar Series will feature Mengli Xiao, MS, PhD candidate at the University of Minnesota.
The growth of big health data provides remarkable opportunities for integrating different sources of datasets and facilitating healthcare decisions on preventions, diagnostics, and treatments. However, scientific studies, including health sciences, have faced unprecedented challenges due to failure to replicate influential studies.
This talk will discuss how to improve the scientific rigor of using meta-analysis to integrate evidence. Specifically, Xiao developed a novel statistical measure that quantifies replicability and identifies non-replicable studies in a meta-analysis. Xiao will present examples to improve the rigor of two real meta-analyses that study 1) the quality of life among breast cancer survivors and 2) susceptibility to the COVID-19 infection among children and adults. Understanding whether a discrepancy in a meta-analysis arises from non-replicability may give researchers insight to guide further investigation of sources of the discrepancy, polish future study designs, and improve health care decisions.