Concepts (S)

Propensity Score Analysis

This section provides a comprehensive exploration into various facets of propensity score (PS) methods and their application in observational studies and surveys. Beginning with an in-depth look into key concepts and calculations related to ATE and ATT, the content navigates through the practical application and diagnostic checks of covariate balance using the SMD. It further elucidates the methodology and application of PS, particularly focusing on matching and weighting to mitigate bias and create comparable groups for causal inference. The intricacies of employing PS methods within surveys are explored, highlighting different approaches and the incorporation of design variables in PS and outcome models. Fundamental assumptions for causal inference, namely Conditional Exchangeability, Positivity, and Causal Consistency, are dissected to form a foundational understanding for conducting robust causal analyses. Additionally, the content optionally delves into the nuances of implementing IPW in surveys. Lastly, additional optional content features an insightful workshop, offering more explanations of PS method implementations in research contexts.

Reading list

Key reference: (Peter C. Austin 2011)

Optional reading:

  • Propensity score introduction (Karim 2021) External link
  • Extensions of Propensity score approaches External link: prepared for Guest Lecture in SPPH 500/007 (Analytical Methods in Epidemiological Research)
  • Propensity score for complex surveys External link: Uses the same lectures here, with some added text descriptions. This also includes a a structured framework for reporting analyses using PS methods in research manuscripts.
  • Reporting guideline (Stuart 2018; Simoneau et al. 2022)
  • Assumptions (Hernán and Robins 2020)

Theoretical references for propensity score analyses in complex surveys:

(Peter C. Austin, Jembere, and Chiu 2018; DuGoff, Schuler, and Stuart 2014; Zanutto 2006; Leite, Stapleton, and Bettini 2018; Lenis, Ackerman, and Stuart 2018; Lenis et al. 2017; Ridgeway et al. 2015)

Video Lessons

Target parameters

Average Treatment Effect (ATE) vs. Average Treatment effect on the Treated (ATT)

Balance

Balance and standardized mean difference (SMD) in observational studies

Propensity score matching
Propensity score matching in complex survey
Propensity score weighting in complex survey
Causal Assumptions
Conference Workshop (Optional)

Post Conference Workshop for 2021 Conference - Canadian Society for Epidemiology and Biostatistics (CSEB)

Video Lesson Slides

Target parameters

Balance

Propensity score matching

Propensity score matching in complex survey

Propensity score weighting in complex survey

Causal Assumptions

FAQ

References

Austin, Peter C. 2011. “A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of in-Hospital Smoking Cessation Counseling on Mortality.” Multivariate Behavioral Research 46 (1): 119–51.
Austin, Peter C, Nathaniel Jembere, and Maria Chiu. 2018. “Propensity Score Matching and Complex Surveys.” Statistical Methods in Medical Research 27 (4): 1240–57.
DuGoff, Eva H, Megan Schuler, and Elizabeth A Stuart. 2014. “Generalizing Observational Study Results: Applying Propensity Score Methods to Complex Surveys.” Health Services Research 49 (1): 284–303.
Hernán, Miguel A., and James M. Robins. 2020. “Chapter 3.” In Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
Karim, ME. 2021. “Understanding Propensity Score Matching.” 2021. https://ehsanx.github.io/psw/.
Leite, Walter L., Laura M. Stapleton, and Eduardo F. Bettini. 2018. “Propensity Score Analysis of Complex Survey Data with Structural Equation Modeling: A Tutorial with Mplus.” Structural Equation Modeling: A Multidisciplinary Journal, 1–22.
Lenis, Diego, Benjamin Ackerman, and Elizabeth A. Stuart. 2018. “Measuring Model Misspecification: Application to Propensity Score Methods with Complex Survey Data.” Computational Statistics & Data Analysis.
Lenis, Diego, Thuan Quoc Nguyen, Dong, and Elizabeth A. Stuart. 2017. “It’s All about Balance: Propensity Score Matching in the Context of Complex Survey Data.” Biostatistics.
Ridgeway, Greg, Stephanie A. Kovalchik, Beth Ann Griffin, and Mohammed U. Kabeto. 2015. “Propensity Score Analysis with Survey Weighted Data.” Journal of Causal Inference 3 (2): 237–49.
Simoneau, Gabrielle, Fabio Pellegrini, Thomas PA Debray, Julie Rouette, Johanna Muñoz, Robert W Platt, John Petkau, et al. 2022. “Recommendations for the Use of Propensity Score Methods in Multiple Sclerosis Research.” Multiple Sclerosis Journal 28 (9): 1467–80.
Stuart, Elizabeth A. 2018. “Chapter 28. Propensity Scores and Matching Methods.” In The Reviewer’s Guide to Quantitative Methods in the Social Sciences, Second Edition, edited by Gregory R. Hancock, Ralph O. Mueller, and Laura M. Stapleton. Routledge.
Zanutto, Elaine L. 2006. “A Comparison of Propensity Score and Linear Regression Analysis of Complex Survey Data.” Journal of Data Science 4 (1): 67–91.