#33: Bayesian Evidence Synthesis and Network Meta-analysis
Day & Time:
June 15, 1:00PM - 4:30PM (Pacific Standard Time)Room Number:
Bradley P. Carlin, PhD, MS
Professor and Head of Biostatistics
University of Minnesota, United States
Karen Lynn Price, PhD, MA
Eli Lilly and Company, United StatesDescription:Maximize your learning while attending the DIA 2014 50th Annual Meeting! Receive $100 off of your DIA 2014 meeting registration by registering for two half-day tutorials or one full-day tutorial. Purchases must be made at the same time in order to receive the discount.
As the era of "big data" arrives in full force for health care and pharmaceutical development, researchers in these areas must turn to increasingly sophisticated statistical tools for their proper analysis. Bayesian statistical methods, while dating in principle to the publication of Bayes' Rule in 1763, have only recently begun to see widespread practical application due to advances in computation and software. Broad application of these methods has been driven by an increased need for quantitative health technology assessment (HTA), especially comparative effectiveness research (CER). In particular, Bayesian methods facilitate borrowing of strength across treatments, trials, and outcomes (say, both safety and efficacy), as well as provide a natural framework for filling in missing data values that respect the underlying correlation structure in the data.
The instructors of this tutorial will provide an overview of Bayesian statistical methods and computation, and then explore their use in evidence synthesis and network meta-analysis (NMA), especially with regard to drug safety. In addition, a demonstration of methods via case examples and discussion regarding the impact of utilizing these approaches throughout pharmaceutical development will occur. A focus on principles and understanding of critical assumptions will be discussed while still indicating where interested users can obtain corresponding technical details.
|#33: Bayesian Evidence Synthesis and Network Meta-||CME|
At the conclusion of this tutorial, participants should be able to:
- Identify the key concepts of Bayesian statistical methods, computation, and software;
- Discuss the use of Bayesian evidence synthesis techniques when applied to network (multiple treatment) meta-analysis for drug safety and efficacy;
- Identify how compounds fare statistically in relation to others, and adjusting for key confounders;
- Define "contrast-based" and "arm-based" NMA methods, as well as approaches that use aggregate versus individual-level patient data, and handle mixed outcome types (say, both binary and continuous).
This tutorial is designed for statisticians familiar with the principles of meta-analysis, who wish to learn about Bayesian methods for evidence, and for anyone undertaking or managing HTAs, including in the context of cost-effectiveness analysis.