This message has been cross posted to the following eGroups: Statistical Consulting Section and Biopharmaceutical Section .
-------------------------------------------
Statistical Issues in Comparative Effectiveness Research
Friday, February 20, 2015
11:00 am to 1:00 pm EST (10:00 am to 12:00 noon CST)
Presenter:
Sharon-Lise Normand, Department of Health Care Policy, Harvard Medical School & Department of Biostatistics, Harvard School of Public Health
Description:
Comparative Effectiveness Research (CER) refers to a body of research that generates and synthesizes evidence on the comparative benefits and harms of alternative interventions to prevent, diagnose, treat, and monitor clinical conditions, or to improve the delivery of health care. The evidence from CER is intended to support clinical and policy decision making at both the individual and the population level. While the growth of massive health care data sources has given rise to new opportunities for CER, several statistical challenges have also emerged. This tutorial will provide an overview of the types of research questions addressed by CER, review the main statistical methodology currently utilized, and highlight areas where new methodology is required. Inferential issues in the "big data" context are identified. Examples from cardiology and mental illness will illustrate methodological issues.
Topics Covered:
Defining CER, causal inference, treatment effect heterogeneity (measured and unmeasured confounding), pragmatic trials, risk models, model averaging, distributed networks, and meta-analysis.
Background Required:
Participants should have some quantitative training, with familiarity in regression modeling.
Registration (https://portal.enar.org/Events/SelectRegType.aspx?EventCode=WEB022015)
Please email
enar@enar.org with questions about registering for this webinar.
-------------------------------------------
Lynn Eberly
Univ of Minnesota
-------------------------------------------