Combining the complete-data and nonresponse models for drawing imputations under MAR

Journal of Statistical Computation and Simulation

Volumn 83, Issue 5, 2013, Pages 868-879

  Jolani, S ^a   van Buuren, S ^a,b   Frank, L E ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

^b TNO   (Netherlands)

Author keywords

dual modelling; missingness mechanism; misspecification; multiple imputation; propensity score

Indexed keywords

EID: 84877268322     PISSN: 00949655     EISSN: 15635163     Source Type: Journal    
DOI: 10.1080/00949655.2011.639773     Document Type: Article

References (24)

1. Harel, O. and Zhou, X. H. 2007. Multiple imputation: Review of theory, implementation and software. Stat. Med., 26: 3057 - 3077. (doi:10.1002/sim.2787)
2. Kenward, M. G. and Carpenter, J. 2007. Multiple imputation: Current perspectives. Stat. Methods Med. Res., 16: 199 - 218. (doi:10.1177/0962280206075304)
3. Zhang, P. 2003. Multiple imputation: Theory and method. Int. Stat. Rev., 71: 581 - 592. (doi:10.1111/j.1751-5823.2003.tb00213.x)
4. Rubin, D. B. 1987. Multiple Imputation for Nonresponse in Surveys, New York: John Wiley.
5. Collins, L. M., Schafer, J. L. and Kam, C. M. 2001. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol. Methods, 6: 330 - 351. (doi:10.1037/1082-989X.6.4.330)
6. van Buuren, S., Boshuizen, H. C. and Knook, D. L. 1999. Multiple imputation of missing blood pressure covariates in survival analysis. Stat. Med., 18: 681 - 694. (doi:10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R)
7. Reiter, J. P., Raghunathan, T. E. and Kinney, S. K. 2006. The importance of modeling the sampling design in multiple imputation for missing data. Surv. Methodol., 32: 143 - 149.
8. Spratt, M., Carpenter, J., Sterne, J. A.C., Carlin, J. B., Heron, J., Henderson, J. and Tilling, K. 2010. Strategies for multiple imputation in longitudinal studies. Am. J. Epidemiol., 172: 478 - 487. (doi:10.1093/aje/kwq137)
9. Lavori, P. W., Dawson, R. and Shera, D. 1995. Multiple imputation strategy for clinical trials with truncation of patient data. Stat. Med., 14: 1913 - 1925. (doi:10.1002/sim.4780141707)
10. Kang, J. D.Y. and Schafer, J. L. 2007. Demystifying double robustness: A comparison of alternative strategies for estimating population means from incomplete data. Statist. Sci., 26: 523 - 539. (doi:10.1214/07-STS227)
11. Bang, H. and Robins, J. M. 2005. Doubly robust estimation in missing data and causal inference models. Biometrics, 61: 962 - 972. (doi:10.1111/j.1541-0420.2005.00377.x)
12. Rubin, D. B. 1976. Inference and missing data. Biometrika, 63: 581 - 592. (doi:10.1093/biomet/63.3.581)
13. Rosenbaum, P. R. and Rubin, D. B. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika, 70: 41 - 55. (doi:10.1093/biomet/70.1.41)
14. Scharfstein, D. O., Rotnitzky, A. and Robins, J. M. 1999. Adjusting for nonignorable drop-out using semiparametric nonresponse models. J. Am. Statist. Assoc., 94: 1096 - 1120. (with Rejoinder, pp. 1135-1146) (doi:10.1080/01621459.1999.10473862)
15. Robins, J. M. Robust estimation in sequentially ignorable missing data and causal inference models. Proceedings of the American Statistical Association Section on Bayesian Statistical Science. Alexandria, VA. pp. 6 - 10.
16. van der Laan, M. J. and Robins, J. M. 2003. Unified Methods for Censored Longitudinal Data and Causality, New York: Springer Verlag.
17. Neugebauer, R. and van der Laan, M. J. 2005. Why prefer double robust estimates?. J. Stat. Plann. Inference, 129: 405 - 426. (doi:10.1016/j.jspi.2004.06.060)
18. Carpenter, J., Kenward, M. and Vansteelandt, S. 2006. A comparison of multiple imputation and doubly robust estimation for analyses with missing data. J. R. Statist. Soc. A, 169: 571 - 584. (doi:10.1111/j.1467-985X.2006.00407.x)
19. Little, R. and An, H. 2004. Robust likekihood-based analysis of multivariate data with missing values. Statist. Sinica, 14: 949 - 969.
20. Hasslet, J. and Raftery, A. E. 1989. Space-time modeling with long-memory dependence: Assessing Ireland's wind power resource. Appl. Stat., 38: 1 - 50. (doi:10.2307/2347679)
21. Hosmer, D. W. and Lemeshow, S. 2003. Applied Logistic Regression, New York: John Wiley.
22. van Buuren, S., Brand, J. P.L., Groothuis-Oudshoorn, C. G.M. and Rubin, D. B. 2006. Fully conditional specification in multivariate imputation. J. Stat. Comput. Simul., 76: 1048 - 1064. (doi:10.1080/10629360600810434)
23. van Buuren, S. and Oudshoorn, K. G. MICE: Multivariate Imputation by Chained Equations in R. J. Stat. Softw, forthcoming
24. Gelfand, A. E. 2000. Gibbs sampling. J. Am. Statist. Assoc., 95: 1300 - 1304. (doi:10.1080/01621459.2000.10474335)