On weighting approaches for missing data

Statistical Methods in Medical Research

Volumn 22, Issue 1, 2013, Pages 14-30

  Li, Lingling ^a   Shen, Changyu ^b   Li, Xiaochun ^b   Robins, James M ^c  

^a HARVARD MEDICAL SCHOOL   (United States)

^b INDIANA UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

inverse probability weighting; missing at random; missing data; missing not at random; monotone missing; non monotone missing

Indexed keywords

ARTICLE; CALCULATION; COMPUTER MODEL; CONCEPTUAL FRAMEWORK; DATA ANALYSIS; EPIDEMIOLOGY; HUMAN; INFORMATION; INTUITION; INVERSE PROBABILITY WEIGHTING; MATHEMATICAL MODEL; MISSING DATA; PROBABILITY; RANDOMIZATION; STATISTICAL ANALYSIS;

DATA INTERPRETATION, STATISTICAL; MODELS, STATISTICAL; PROBABILITY;

EID: 84874501270     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280211403597     Document Type: Article

References (44)

1. Little R, Rubin D Statistical analysis with missing data. New York: John Wiley & Sons ; 1987 :
2. Raghunathan TE. What do we do with missing data? Some options for analysis of incomplete data. Ann Rev Public Health. 2004 ; 25: 99-117 (Pubitemid 38543612)
3. Rubin D. Inference and missing data (with discussion). Biometrika. 1976 ; 63: 581-592
4. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Am Stat Assoc. 1952 ; 47: 663-685
5. Robins J, Rotnitzky A, Zhao L. Estimation of regression coefficients when some regressors are not always observed. J Am Stat Assoc. 1994 ; 89: 846-866
6. Robins J, Rotnitzky A, Zhao L. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J Am Stat Assoc. 1995 ; 90: 106-121
7. Robins J, Hernan M, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000 ; 11: 550-560 (Pubitemid 30660034)
8. Hernan M, Brumback B, Robins J. Marginal structural models to estimate the causal effect of Zidovudine on the survival of HIV-positive men. Epidemiology. 2000 ; 11: 561-570 (Pubitemid 30660035)
9. Horton NJ, Laird NM. Maximum likelihood analysis of generalized linear models with missing covariates. Stat Methods Med Res. 1999 ; 8: 37-50 (Pubitemid 29222786)
10. Ibrahim JG, Chen MH, Lipsitz SR, Herring AH. Missing-data methods for generalized linear models: a comparative review. J Am Stat Assoc. 2005 ; 100: 332-346 (Pubitemid 40366842)
11. Ibrahim JG, Molenberghs G. Missing data methods in longitudinal studies: a review. Test. 2009 ; 18: 1-43
12. Ibrahim JG, Chen MH. Power prior distributions for regression models. Stat Sci. 2000 ; 15: 46-60
13. Chen MH, Ibrahim JG, Lipsitz SR. Bayesian methods for missing covariates in cure rate models. Lifetime Data Anal. 2002 ; 8: 117-146
14. Ibrahim JG, Chen MH, Lipsitz SR. Bayesian methods for generalized linear models with covariates missing at random. Can J Stat. 2002 ; 30: 55-78
15. Harel O, Zhou XH. Multiple imputation: review of theory, implementation and software. Stat Med. 2007 ; 26: 3057-3077 (Pubitemid 47019007)
16. Rubin DB Multiple imputation for nonresponse in surveys. New York: Wiley ; 1987 :
17. Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999 ; 8: 3-15 (Pubitemid 29222784)
18. Tsiatis A Semiparametric theory and missing data. New York: Springer ; 2006 :
19. van der Laan M, Robins J Unified methods for censored longitudinal data and causality. New York: Springer ; 2003 :
20. Robins JM, Rotnitzky A. Semiparametric efficiency in multivariate regression-models with missing data. J Am Stat Assoc. 1995 ; 90: 122-129
21. Rotnitzky A, Robins JM, Scharfstein DO. Semiparametric regression for repeated outcomes with nonignorable nonresponse. J Am Stat Assoc. 1998 ; 93: 1321-1339 (Pubitemid 128385545)
22. Robins JM, Rotnitzky A, Zhao LP. Analysis of semiparametric regression-models for repeated outcomes in the presence of missing data. J Am Stat Assoc. 1995 ; 90: 106-121
23. Rotnitzky A, Robins JM. Semiparametric regression estimation in the presence of dependent censoring. Biometrika. 1995 ; 82: 805-820
24. Rotnitzky A, Robins JM. Semiparametric estimation of models for means and covariances in the presence of missing data. Scand J Stat. 1995 ; 22: 323-333
25. Rotnitzky A, Holcroft CA, Robins JM. Efficiency comparisons in multivariate multiple regression with missing outcomes. J Multivariate Anal. 1997 ; 61: 102-128 (Pubitemid 127340345)
26. Bickel PJ, Klaassen CA, Ritov Y, Wellner JA Efficient and adaptive estimation for semiparametric models. New York: Springer Verlag ; 1998 :
27. Scharfstein DO, Rotnitzky A, Robins JM. Adjusting for nonignorable drop-out using semiparametric nonresponse models. J Am Stat Assoc. 1999 ; 94: 1096-1120
28. Scharfstein DO, Rotnitzky A, Robins JM. Adjusting for nonignorable drop-out using semiparametric nonresponse models - Rejoinder. J Am Stat Assoc. 1999 ; 94: 1135-1146
29. Robins JM, Rotnitzky A. Inference for semiparametric models: some questions and an answer - Comments. Stat Sin. 2001 ; 11: 920-936
30. Vansteelandt S, Rotnitzky A, Robins J. Estimation of regression models for the mean of repeated outcomes under nonignorable nonmonotone nonresponse. Biometrika. 2007 ; 94: 841-860
31. Breiman L, Friedman JH, Olshen RA, Stone CJ Classification and regression trees. Belmont, CA: Wadsworth International Group ; 1984 :
32. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat. 2000 ; 28: 337-374
33. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting - Rejoinder. Ann Stat. 2000 ; 28: 400-407
34. Breiman L. Random forests. Mach Learn. 2001 ; 45: 5-32 (Pubitemid 32933532)
35. Hastie T, Tibshirani R, Friedman J The elements of statistical learning: data mining, inference, and prediction. 2 nd ed. New York: Springer, 2009. New York: Springer ; 2009 :
36. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009 ; 20: 512-522
37. Therneau TM and Atkinsoon EJ. An introduction to recursive partitioning using the RPART routines. Technical Report 61, Rochester, MN, Mayo Clinic, Section of Statistics, 1997.
38. Bang H, Robins J. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005 ; 61: 962-972 (Pubitemid 43906903)
39. Potthoff RF, Tudor GE, Pieper KS, Hasselblad V. Can one assess whether missing data are missing at random in medical studies? Stat. Methods Med Res. 2006 ; 15: 213-234 (Pubitemid 43833929)
40. Rotnitzky A, Robins J. Analysis of semi-parametric regression models with non-ignorable non-response. Stat Med. 1997 ; 16: 81-102 (Pubitemid 27029290)
41. Robins JM, Gill RD. Non-response models for the analysis of non-monotone ignorable missing data. Stat Med. 1997 ; 16: 39-56 (Pubitemid 27029287)
42. Robins JM. Non-response models for the analysis of non-monotone non-ignorable missing data. Stat Med. 1997 ; 16: 21-37 (Pubitemid 27029286)
43. Robins JM, Rotnitzky A, Scharfstein D Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In: Statistical models in epidemiology: the environment and clinical trials. Halloran M Berry D, ed. New York: Springer-Verlag ; 1999: 1-92.
44. Gill RD, van der Laan M, Robins JM Coarsening at random: characterizations, conjectures and counterexamples. In: Proceedings of the first Seattle symposium on biostatistics: survival analysis. Lin DY, ed. New York: Springer Verlag ; 1997: 255-294.