Conference Workshops

There will be a number of conference workshops for ICAS 2019. Complete program schedule will be published soon.

Number of seats: 50 (first come basis)

Venue: ISRT computer lab room

Topics and Resource Persons

Workshop-I: Applying Propensity Score Approaches to Complex Survey Data Analyses


Propensity score analyses (PSA) are widely used in analyzing observational datasets to reduce the impact of confounding due to observed covariates. In many of these applied studies, nationally representative population-based complex survey datasets are frequently used. Most of these studies choose to ignore the complex survey design features (strata, PSU, survey weights); partly because there is a lack of clear guidelines of how PSA should be implemented in a complex survey data analysis context. Only a few relatively recent studies have examined how to incorporate PSA in this context, and some of the recommendations are contradictory, inconclusive or not generalizable to all types of PSA. This short workshop will help recognize some of the challenges and open questions in this regard. The workshop is aimed for practitioners; particularly focused on demonstrating the implementation of PSA in a complex survey data analysis context through an illustrative data analysis exercise. The prerequisite is knowledge of multiple regression analysis and working knowledge in R. Some background in PSA and survey data features would be useful, but not required.

Resource Person:

Dr. M. Ehsan Karim

Assistant Professor, School of Population and Public Health

University British Columbia

Scientist & Biostatistician, CHEOS, Canada

Workshop-II: Step into Data Science - A not so Basic Intro


The data science and machine learning buzzwords have taken many of us by the storm. Especially, many (if not the majority) in the statistics community are in limbo as to perceiving the new landscape as it rapidly evolves. Much of what data science is and is not have been well exemplified. However, the adoption and implementation of data science is still an ongoing process across industries. At the heart of data science is what is known as machine learning. In this workshop, I will provide a high-level intro to machine learning and how data science is being understood and interpreted across industries. I will give some real use cases from industries and discuss how the new generation of statisticians should look beyond what the textbook prepares them for if they want to take a bite of the bigger pie.

Python will be used as programming language but R can do the job as well. As such, familiarity with Python would be advantageous but not required. Students with working experience of any programming language would be able to follow along.

Resource Person:

Enayet Raheem, PhD

Senior Data Scientist (Surgery)

Atrium Health, USA