1081 Burrard Street
Ehsan Karim, PhD, M.Sc.
Scientist, Biostatistician, CHÉOS
Assistant Professor, School of Population and Public Health, UBC
Researchers and statisticians frequently use propensity score analyses (PSA) to analyze observational datasets and 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 incorrectly choose to ignore the complex survey design features; 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 these recommendations are contradictory, inconclusive, or not generalizable to all types of PSA. This workshop will help recognize some of the challenges and open questions in the ‘big data’ analysis setting. The workshop is aimed at practitioners and is particularly focused on demonstrating the implementation of PSA in a complex survey data analysis context through an illustrative data analysis exercise.
Background in causal inference or survey data analysis is not required. Attendees should have prerequisite knowledge of multiple regression analysis and working knowledge in R (e.g., basic data manipulation and regression fitting). In the workshop, R will be the primary software package used to demonstrate the implementations. The provided software codes will be annotated for those who prefer to use other software packages.
Tentative outline of this workshop:
- an introduction to PSA,
- explanation of some of the real-world challenges of applying PSA in a complex survey data analysis context,
- familiarization with some of the recommendations outlined in the recent literature,
- demonstration of the corresponding PSA implementation strategies through an illustrative data analysis example, and
- discussion of resources and future directions.