References
- D'Agostino, Jr. RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–2281. doi: 10.1002/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-B
- Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55. doi: 10.1093/biomet/70.1.41
- Imbens GW. The role of the propensity score in estimating dose-response functions. Biometrika. 2000;87:706–710. doi: 10.1093/biomet/87.3.706
- Feng P, Zhou X-H, Zou Q-M, Fan M-Y, Li X-S. Generalized propensity score for estimating the average treatment effect of multiple treatments. Stat Med. 2012;31:681–697.
- Tu C, Koh WY, Jiao S. Using generalized doubly robust estimator to estimate average treatment effects of multiple treatments in observational studies. J Stat Comput Simul. 2012; in press. Available from: http://www.tandfonline.com/doi/abs/10.1080/00949655.2012.663375
- Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med. 2004;23:2937–2960. doi: 10.1002/sim.1903
- Agresti A. Categorical data analysis. Hoboken, NJ: Wiley; 2002.
- Persson E, Waernbaum I., Estimating a marginal causal odds ratio in a case-control design: analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus. Stat. Med. 2013; in press. Available from: http://onlinelibrary.wiley.com/doi/10.1002/sim.5826/abstract
- Austin PC. The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Stat Med. 2010;29:2137–2148. doi: 10.1002/sim.3854
- Brent RP. Algorithms for minimization without derivatives. New York: Dover Publications; 2002.