On-line Registration open to March 21 On-site registration available during workshop. Registration fees: $150, students and PDF: $50 Application for Participant support: Deadline to apply March 15, 2015	List of Workshop Participants	Reimbursement information for funded participants
	Accommodation in Toronto	Information for speakers
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Overview of Program:

The program will feature presentations and short workshops from statisticians, health policy researchers, and government representatives to examine topics related to the use of large, observational datasets in health policy research. Areas of focus include (a) enabling/disabling factors in the use of large databases in health policy contexts, (b) causal inference techniques, (d) combining databases and the use of data synthesis/meta-analytic techniques, (e) presentation and visualization of information. Examples will be drawn from pharmacoepidemiology, health services, public health, and health economics.

Format: The workshop will use panel sessions, short workshops, presentations, and small-group discussions to address the workshop themes.

Schedule

Monday March 23
9:00-9:30	On site registration and morning coffee
9:30-10:00	Welcome and overview of the week by Program Committee
10:00-12:00	Panel Presentation + Q & A Big Data, Causal Inference, and Health Policy: An Overview Michael Schull, Institute of Clinical Evaluative Sciences Mark Smith, Manitoba Centre for Health Policy Recent challenges (and opportunities) of working with "Big Data" in health research Arlene Ash, University of Massachusetts Medical School Seeking needles of health wisdom in haystacks of big data
12:00-1:30	Lunch break
1:30-2:30	Jonas Peters, Seminar für Statistik, Departement Mathematik, Zurich, Switzerland (Remote Lecture) Graphical models/visualization
2:30-3:00	Tea break
3:00-4:00	Mahmoud Azimaee, Institute for Clinical Evaluative Sciences Mark Smith, Manitoba Centre for Health Policy Emerging data quality assessments of administrative data for use in research
4:00+	Reception with cash bar
Tuesday March 24
9:15-9:30	Welcome and IntroductionsProgram Committee
9:30-10:30	Patrick Heagerty, University of Washington
10:30-11:00	Break
11:00-12:00	Cory Zigler, Harvard University Uncertainty and treatment effect heterogeneity in comparative effectiveness research
12:00-1:30	Lunch
1:30-2:30	Xiaochun Li, Indiana University School of Medicine EMR² : Evidence Mining Research in Electronic Medical Records Towards Better Patient Care
2:30-3:00	Break
3:00-4:00	Michael Wolfson, University of Ottawa Pretty Big Data and Analysis to Understand the Relative Importance of Health Determinants - HealthPaths
Wednesday March 25
9:30-9:45	Welcome and Introductions Program Committee
9:45-12:00	Workshop: Peter Austin, University of Toronto Propensity score methods for estimating treatment effects using observational data
12:00-1:15	Lunch
1:15-2:00	Dan Chateau, University of Manitoba Implementing a research program using data from multiple jurisdictions: The Canadian Network for Observational Drug Effect Studies (CNODES)
2:00-3:00	Constantine Gatsonis, Brown University (Remote Lecture) Diagnostic test assessment using large databases
3:00 -3:30	Break & End of day NOTE: Andrew Lo (MIT) will be presenting a special invited session at 3:30 on the topic of Big Data in Finance
Thursday March 26
9:15-9:30	Welcome and Introductions by Program Committee
9:30-12:30	Workshop: Erica Moodie, McGill University Marginal structural models
12:30-1:30	Lunch
1:30-2:30	Elizabeth Stuart, Johns Hopkins Bloomberg School of Public Health Using big data to estimate population treatment effects
2:30-3:00	Break
3:00-4:00	Hau-tieng Wu, University of Toronto Online and adaptive analysis of dynamic periodicity and trend with heteroscedastic and dependent errors -- with clinical applications
Friday March 27
9:15-9:30	Welcome and IntroductionsProgram Committee
9:30-10:30	Danica Marinac-Dabic, Center for Devices and Radiological Health, Food and Drug Administration (Remote Lecture)
10:30-10:45	Break
10:45-11:45	David Henry, Dalla Lana School of Public Health, University of Toronto, and Institute for Clinical Evaluative Sciences Being realistic about how Big Data can support health and health policy research
12:00-1:00	Closing session by Lisa Lix