Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data

Biometrika

Volumn 96, Issue 3, 2009, Pages 723-734

  Cao, Weihua ^a   Tsiatis, Anastasios A ^a   Davidian, Marie ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Causal inference; Enhanced propensity score model; Missing at random; No unmeasured confounders; Outcome regression

Indexed keywords

EID: 69249196484     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asp033     Document Type: Article

References (19)

1. BANG, H. & ROBINS, J.M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61, 962-72.
2. KANG, D. Y. J. & SCHAFER, J. L. (2007). Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data (with discussion and rejoinder). Statist. Sci. 22, 523-80.
3. LUNCEFORD, J. K. & DAVIDIAN, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statist. Med. 23, 2937-60.
4. ROBINS, J. M.,HERŃAN, M. & BRUMBACK, B. (2000).Marginal structural models and causal inference in epidemiology. Epidemiol. 11, 550-60.
5. ROBINS, J. M., ROTNITZKY, A. & ZHAO, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. J. Am. Statist. Assoc. 89, 846-66.
6. ROBINS, J. M., SUED, M., LEI-GOMEZ, Q. & ROTNITZKY, A. (2007). Performance of double-robust estimators when inverse probability weights are highly variable. Statist. Sci. 22, 544-59.
7. ROSENBAUM, P. R. (1987). Model-based direct adjustment. J. Am. Statist. Assoc. 82, 387-94.
8. ROSENBAUM, P. R. & RUBIN, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
9. ROSENBAUM, P. R. & RUBIN, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. J. Am. Statist. Assoc. 79, 516-24.
10. RUBIN, D. B. (1976). Inference and missing data. Biometrika 63, 581-92.
11. RUBIN, D. B. (1978). Bayesian inference for causal effects: the role of randomization. Ann. Statist. 6, 34-58.
12. RUBIN, D. B. & THOMAS N. (1996).Matching using estimated propensity scores: relating theory to practice. Biometrics 52, 249-64.
13. SAS INSTITUTE, INC. (2006). SAS Online Documentation 9.1.3. Cary, NC: SAS Institute.
14. STEFANSKI, L. A. & BOOS, D. D. (2002). The calculus of M-estimation. Am. Statist. 56, 29-38.
15. SCHARFSTEIN, D. O.,ROTNITZKY, A. & ROBINS, J.M. (1999). Adjusting for nonignorable drop-out using semiparametric nonresponse models (with discussion and rejoinder). J. Am. Statist. Assoc. 94, 1096-146.
16. TAN, Z. (2006). A distributional approach for causal inference using propensity scores. J. Am. Statist. Assoc. 101, 1619-37.
17. TAN Z. (2007). Understanding OR, PS and DR. Statist. Sci. 22, 560-8.
18. TSIATIS, A. A. (2006). Semiparametric Theory and Missing Data. New York: Springer.
19. TSIATIS, A. A. & DAVIDIAN, M. (2007). Comment on Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.' Statist. Sci. 22, 569-73.