Event Description
The Department of Epidemiology and Biostatistics welcomes Hwanhee Hong, PhD, postdoctoral fellow at Johns Hopkins Bloomberg School of Public Health, who will present “Integrating Data for Comparative Effectiveness Research”.
Comparative effectiveness research helps answer “what works best” and provide evidence on the effectiveness, benefits, and harms of different treatments. Network meta-analysis (NMA) is an extension of a traditional pairwise meta-analysis to compare multiple treatments simultaneously and take advantage of multiple sources of data. In some situations there are some studies with only aggregated data (AD) and others with individual patient-level data (IPD) available; standard network meta-analysis methods have been extended to synthesize these types of data simultaneously.
In this talk, Dr. Hong will propose Bayesian hierarchical NMA models that borrow information adaptively across AD and IPD studies using power and commensurate priors. The methods are validated and compared via extensive simulation studies, and then applied to an example in diabetes treatment comparing 28 oral anti-diabetic drugs. |
