3 Observational Studies: Design
Module 3 Slides
Recording: Module 3, Part 1
Textbook reading
Additional Readings
Rosenbaum, P.R., Rubin, D.B. (1983) “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” Biometrika, 70:41-55.
Rosenbaum, P.R., Rubin, D.B. (1984) “Reducing Bias in Observational Studies Using Subclassification on the Propensity Score,” Journal of the American Statistical Association, 79:516-524.
Rubin, D.B., Thomas, N. (1996) “Matching Using Estimated Propensity Scores: Relating Theory to Practice,” Biometrics, 52:249-264.
Rubin, D.B., Thomas, N. (2000) “Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates,” Journal of the American Statistical Association, 95:573-585.
Leon, A.C., Hedeker, D. (2005) “A Mixed-Effects Quintile-Stratified Propensity Adjustment for Effectiveness Analysis of Ordered Categorical Doses,” Statistics in Medicine, 24:647-658.
Zanutto, E.L. (2006) “A Comparison of Propensity Score and Linear Regression Analysis of Complex Survey Data,” Journal of Data Science, 4:67-91.
Stuart, E.A. (2010) “Matching Methods for Causal Inference: A Review and A Look Forward,” Statistical Science, 25: 1-21.
Li, F., Zaslavsky, A.M., Landrum, M.B. (2013) “Propensity Score Weighting with Multilevel Data,” Statistics in Medicine, 32: 3373-3387.
Li, F., Thomas, L.E., Li, F. (2019) “Addressing Extreme Propensity Scores via the Overlap Weights,” American Journal of Epidemiology, 188: 250-257.
Software
Propensity score and matching Software for implementing matching methods and propensity scores.