References
- Austin, P. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine 28:3083–3107.
- Austin, P. (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 46:119–151.
- D’Agostino, R. (1998). Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine 17:2265–2281.
- Gliklich, R., Dreyer, N., and Leavy, M. eds. (2014). Registries for evaluating patient outcomes: a user’s guide. 3rd Edition. AHRQ Publication No. 13(14)-EHC111. Rockville, MD: Agency for Healthcare Research and Quality.
- Hedges, L. V., Olkin, I. (1985). Statistical Methods for Meta-Analysis. San Diego, CA: Academic Press.
- Hill, J. L., Reiter, J. P., Zanutto, E. L. (2004). A comparison of experimental and observational data analyses. In Applied Bayesian Modeling and Causal Inference From an Incomplete-Data Perspective (Edited by Andrew Gelman and Xiao-Li Meng), 44–56. Wiley.
- Imai, K., King, G., Stuart, E. A. (2008). Misunderstandings between experimentalists and observationalists about causal inference. Journal of the Royal Statistical Society, Series A (Statistics in Society) 171:481–502.
- Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics 86:4–29.
- Rosenbaum, P. R., Rubin D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55.
- Rosenbaum, P. R., Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician 39:33–38.
- Rubin, D. B. (1973). Matching to remove bias in observational studies. Biometrics 29:159–184.
- Rubin, D. B., Thomas, N. (1996) Matching using estimated propensity scores: relating theory to practice. Biometrics 52:249–264.
- Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology 2:169–188.
- Rubin, D. B. (2008). For objective causal inference, design trumps analysis. The Annals of Applied Statistics 2(3):808–840.
- Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science 25:1–21.
- Yue, L. Q. (2012). Regulatory considerations in the design of comparative observational studies using propensity scores. Journal of Biopharmaceutical Statistics 22:1272–1279.
- Yue, L. Q., Lu, N., Xu, Y. (2014) Designing premarket observational comparative studies using existing data as controls: challenges and opportunities. Journal of Biopharmaceutical Statistics 24(5):994–1010.