SORA-TABA  Workshop & DLSPH Biostatistics Research Day

Date: June 15, 2018
Time: 9:00 am to 5:00 pm
Location: Dalla Lana School of Public Health, 6th Floor Auditorium HS610, 155 College Street, Toronto
Theme: Network Meta-Analysis

The registration fee includes lunch and coffee breaks.

• CALL FOR ABSTRACTS 2018
  • Poster Instructions
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The Division of Biostatistics at the Dalla Lana School of Public Health (DLSPH) is pleased to host the “SORA-TABA Workshop and the DLSPH Biostatistics Research Day”. The event is intended to bring together the regional and local statistical communities who are interested in biostatistics, financial statistics and other applied areas of statistics. Please join us in making this SORA-TABA-DLSPH joint event a great success!

The workshop will include poster presentations by participants. Students and post-docs are particularly encouraged to present their research or practicum work, and three poster awards will be given at the closing ceremony. There will be a career panel discussion aimed to provide graduate students with career-building advice. The organization of this workshop represents a joint effort between DLSPH and the following organizations: the Southern Ontario Regional Association of SSC, the Southern Ontario Chapter of ASA, the Applied Biostatistics Association, York University, McDougall Scientific, and the Hospital for Sick Children.

Network Meta-Analysis

Instructor: Christopher Schmid, Brown University
Professor of Biostatistics and Co-Director of the Center for Evidence Synthesis in Health

Comparative effectiveness usually involves evaluation of multiple interventions and may involve multiple outcomes measured at multiple times as well. Meta-analysis, whether of continuous or discrete outcomes, has in the past focused on summarizing the evidence comparing two treatments or classes of treatments. Recently, methods have been developed to integrate comparisons of multiple treatments into coherent models that allow simultaneous comparison of all treatments, combining the direct evidence from head-to-head studies with indirect evidence from trials that involve common comparators. The network models provide estimates of the relative effectiveness or harms of all included treatments, and a ranking with associated probability estimates. These methods depend on a crucial assumption that the direct and indirect evidence are compatible (consistency) and that treatments are mutually exchangeable across studies (transitivity). This course will introduce meta-analysis in the context of evidence-based science and will then outline the basic principles of network meta-analysis and assessment of the validity of its assumptions including the key role that potential effect modifiers play. Examples of its application to different types of outcomes, both efficacy and safety with discussion of incomplete data problems will be discussed.

The course is aimed at statisticians and other data analysts who will be designing, performing and interpreting network meta-analysis. The presentation will combine principles and intuition about the proper application of the methods as well as technical information about the models employed. Although most of the examples will be taken from healthcare, the methods are applicable in any discipline where meta-analysis is undertaken including education, psychology, economics, etc. Examples in each of these areas will be given and discussion is welcomed. The course will cover the following topics:

1. Review of Meta-analysis and meta-regression for two treatments;
2. Definition of Direct and indirect comparisons and network consistency;
3. Network meta as a meta-regression problem;
4. Role of Heterogeneity and Exchangeability in choosing studies;
5. Evaluating Network Assumptions: Exchangeability, Consistency;
6. Network Diagnostics;
7. Models for Categorical Outcomes;
8. Ranking of Treatments;
9. Treatment of Missing Data
10. Software

Christopher H. Schmid is Professor of Biostatistics and Co-Director of the Center for Evidence Synthesis in Health at Brown University School of Public Health. He directs the Clinical Study Design, Epidemiology and Biostatistics Core of the Rhode Island Center to Advance Translational Science and also directs the Evidence Synthesis Academy, a federally funded educational program for mid-career professionals and users of healthcare evidence. He is a Fellow of the American Statistical Association, founding Editor of the journal Research Synthesis Methods, long-time statistical editor of the American Journal of Kidney Diseases and member of the Data Safety and Risk Management Committee for FDA. His research focuses on methods and applications for meta-analysis particularly Bayesian methods and software and on predictive models derived from combining data from different sources. He was lead statistician for the CKD-EPI consortium that developed the most commonly used formulas to estimate GFR based on the biomarkers serum creatinine and serum cystatin. Recently, he has been focusing efforts on meta-analysis of N-of-1 studies and is lead statistician on three current funded series of trials. He is an author of more than 250 publications and has served as a consulting statistician in diverse areas of medicine and health for academia, government and industry. He has coauthored consensus CONSORT reporting guidelines for N-of-1 trials and single-case designs, and PRISMA guidelines extensions for meta-analysis of individual participant studies and for network meta-analyses as well as the Institute of Medicine report that established national standards for systematic reviews Dr. Schmid graduated from Haverford College with a BA in Mathematics and received his PhD in Statistics from Harvard University. He worked for 20 years at Tufts Medical Center, including 6 years directing its Biostatistics Research Center before moving to Brown in 2012 where he directed its Masters program for 4 years.