**Both Short Courses are held during 1-4:30pm in different rooms. (Jump to Short Course 2)
Location: Davis Centre Room: 1302
Title: Unveiling the Power of Real-World Data: A Causal Inference Framework for Designing and Analyzing Randomized Clinical Trials
Dr. Shu Yang, Associate Professor of Statistics, North Carolina State University.
The short course will cover the objectives and methods that allow integrative analyses of data from RCTs and observational studies. These methods exploit the complementing features of RCTs and observational studies to estimate the average treatment effect (ATE) and heterogeneity of treatment effect (HTE) over a target population.
In Part I, we will review existing statistical methods for generalizing RCT findings to a target population leveraging the representativeness of the observational studies. Due to population heterogeneity, the ATE estimated from the RCTs lack external validity/generalizability to a target population. We will review the statistical methods for conducting generalizable RCT analysis for the targeted ATE, including inverse probability sampling weighting, calibration weighting, outcome regression, and doubly robust estimators. R software and applications will also be covered.
In Part II, we will review existing statistical methods for integrating RCTs and observational studies for robust and efficient estimation of the HTE. RCTs have been regarded as the gold standard for treatment effect evaluation due to the randomization of treatment, which may be Underpowered to detect HTEs due to practical limitations. On the other hand, large observational studies contain rich information on how patients respond to treatment, which, however, may be confounded. We will review statistical methods for robust and efficient estimation of the HTE leveraging the treatment randomization in RCTs and rich information in observational studies, including calibration, test-based integrative analysis, and confounding function modeling. R software and applications will also be covered.
About the Instructor
Dr. Shu Yang is an Associate Professor of Statistics, Goodnight Early Career Innovator, and University Faculty Scholar at North Carolina State University. She received her Ph.D. in Applied Mathematics and Statistics from Iowa State University and postdoctoral training at Harvard T.H. Chan School of Public Health. Her primary research interest is causal inference and data integration, particularly with applications to comparative effectiveness research in health studies. She also works extensively on methods for missing data and spatial statistics. She has been co-Investigator for a Patient-Centered Outcomes Research Institute grant and Principal Investigator for U.S. National Science Foundation and National Institute of Health research projects.
Location: Davis Centre Room 1304
Title: A Crash Course in Spatial Statistics with R
Dr. Patrick Brown, Associate Professor, University of Toronto.
Profile Link: https://www.statistics.utoronto.ca/people/directories/all-faculty/patric...
Part 1: The world is not flat
This first part will cover some basic spatial data visualization and manipulation in R. It's not possible to make a two dimensional map that will correctly represent the sizes and distances for points and objects on the sphere. We will discuss various standard and customized Coordinate Reference Systems (CRS) for projecting the round world onto a flat map, show how to use them in R, and deal with complications that arise for with non-standard CRS's.
Part 2: Spatial random effects models
The second part will show how to fit the 'standard' spatial model for health outcome data, the Besag, York, and Mollié model for area-level data. The model assumes disease risk in each region is correlated with the risk in neighbouring regions, perhaps because of some unmeasured risk factors. The model will be presented, Bayesian inference for the model described, and how to carry out an analysis and visualize results in R demonstrated.
The mapmiscTerra and diseasemappingTerra packages available at https://r-forge.r-project.org/R/?group_id=312 will be used.
About the Instructor
Patrick Brown's research focuses on models and inference methodologies for spatio-temporal data, motivated by problems in spatial epidemiology and the environmental sciences. Current statistical methods research involves Bayesian inference for non-Gaussian spatial data, and non-parameteric methods for spatially aggregated and censored locations.