Title
Combining Information: Heterogeneity in Research Synthesis
Speaker
Eloise Kaizar, Carnegie Mellon University
Abstract
Research synthesis plays a central role in the process of scientific discovery, providing a formal methodology for the systematic accumulation and evaluation of scientific evidence. There are many situations in which research synthesis is required because obtaining the necessary information from an individual trial is not possible or practical. The study of the relationship between suicide and antidepressant use in children and adolescents is one such case. To shed light on this issue, the FDA combined data from 24 randomized controlled trials using standard frequentist fixed and random effects models. However, the diversity of the trials suggested the presence of systematic effect variation that was not incorporated into these models. We applied a Bayesian hierarchical model to the data to include more appropriate variance structure and answer scientific questions regarding subsets of the data. This type of model is sensitive to prior specification of the variance components, and so we conducted an extensive analysis to determine the robustness of our results. While this technique was successful in solving the variance and subsetting issues, the collection of clinical trials share qualities that limit the generalizability of the meta-analysis. I am investigating extensions of research synthesis to overcome some of these limitations.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
