Methods for handling missing data

Epidemiological Methods in Life Course Research

Volumn , Issue , 2009, Pages

  Clarke, Paul ^a   Hardy, Rebecca ^b  

^a LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Bayesian inference; Hot deck imputation; Likelihood methods; Missing data; Multiple imputation

Indexed keywords

EID: 84921051638     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1093/acprof:oso/9780198528487.003.0007     Document Type: Chapter

References (49)

1. Allison PD (2001). Missing data. Sage, Thousand Oaks, CA.
2. Brick J, Kalton G (1996). Handling missing data in survey research. Statistical Methods in Medical Research 5, 215-238.
3. Greenland S, Finkle WD (1995). A critical look at methods for handling missing covariates in epidemiologic regression analysis. American Journal of Epidemiology 142, 1255-1268.
4. Groves RM, Couper MP (1998). Nonresponse in household interview surveys. Wiley, New York.
5. Little RJA, Rubin DB (1989). The analysis of social science data with missing values. Sociological Methods and Research 18, 292-326.
6. Groves RM, Dilman DA, Eltinge JL, Little RJA, ed. (2002). Survey nonresponse. Section I. Wiley, Chichester.
7. Wadsworth MEJ, Butterworth SL, Hardy RJ, Kuh DJ, Richards M, Langenberg C, et al. (2003). The life course prospective design: an example of the benefits and problems associated with study longevity. Social Science and Medicine 57, 2193-2205.
8. Allison PD (2001). Missing data. Sage, Thousand Oaks, CA.
9. Little RJA, Rubin DB (2002). Statistical analysis with missing data, 2nd edn. Wiley, Hoboken.
10. Schafer JL, Graham JW (2002). Missing data: our view of the state of the art. Psychological Methods 7, 147-177.
11. Curran PJ (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research 38, 529-569.
12. Groves RM, Couper MP (1998). Nonresponse in household interview surveys. Wiley, New York.
13. Robins JM, Gill RD (1997). Non-response models for the analysis of non-monotone ignorable missing data. Statistics in Medicine 16, 39-56.
14. Rubin DB (1976). Inference and missing data. Biometrika 63, 581-592.
15. Little RJA (1992). Regression with missing X's: a review. Journal of the American Statistical Association 87, 1227-1237.
16. Reilly MJ (1993). Data analysis using hot deck multiple imputation. Statistician 42, 307-313.
17. Reilly MJ, Pepe MS (1997). The relationship between hot-deck multiple imputation and weighted likelihood. Statistics in Medicine 16, 5-19.
18. Greenland S, Finkle WD (1995). A critical look at methods for handling missing covariates in epidemiologic regression analysis. American Journal of Epidemiology 142, 1255-1268.
19. Faris PD, Ghali WA, Brant R, Norris CM, Galbraith PD, Knudtson ML (2002). Multiple imputation for dealing with missing data in observational healthcare outcome systems. Journal of Clinical Epidemiology 55, 184-191.
20. Crawford SL, Tennstedt SL, Mckinlay JB (1995). A comparison of analytic methods for non-random missingness of outcomes data. Journal of Clinical Epidemiology 48, 209-219.
21. Schafer JL (1997). Analysis of incomplete multivariate data. Chapman and Hall, London.
22. Arbukle JL, Wothke W (1999). AMOS 4.0 User's Guide. SmallWaters, Chicago.
23. Dempster AP, Laird NM, Rubin DB (1977). Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1-38.
24. Ibrahim JG, Chen MH, Lipsitz SR, Herring AH (2005). Missing-data methods for generalized linear models: a comparative review. Journal of the American Statistical Association 100, 332-346.
25. Schafer JL, Olsen MK (1998). Multiple imputation for multivariate missing-data problems: a data analyst's perspective. Multivariate Behavioral Research 33, 545-571.
26. Schafer JL (1999). Multiple imputation: a primer. Statistical Methods in Medical Research 8, 3-15.
27. Patrician PA (2002). Multiple imputation for missing data. Research in Nursing and Health 25, 76-84.
28. Rubin DB (1987). Multiple imputation for nonresponse in surveys. Wiley, New York.
29. Tanner MA (1996). Tools for statistical inference, 3rd edn. Springer, New York.
30. Horvitz DG, Thompson DJ (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association 47, 663-685.
31. Binder DA (1983). On the variances of asymptotically normal estimators from complex surveys. International Statistical Review 51, 279-292.
32. Robins JM, Rotnitzky A, Zhao LP (1995). Analysis of semi-parametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106-121.
33. Carpenter JR, Kenward MG, Vansteelandt S (2006). A comparison of multiple imputation and doubly robust estimation for analyses with missing data. Journal of the Royal Statistical Society A 169, 571-584.
34. Enders CK (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods 6, 352-370.
35. Little RJA, Schluchter MD (1985). Maximum likelihood estimation for mixed continuous and continuous categorical data with missing values. Biometrika 72, 497-512.
36. Oakes D (1999). Direct calculation of the information matrix via the EM algorithm. Journal of the Royal Statistical Society B 61, 479-482.
37. Herring AH, Ibrahim JG (2001). Likelihood-based methods for missing covariates in the Cox proportional hazards model. Journal of the American Statistical Association 96, 292-302.
38. Collins LM, Schafer JL, Kam CM (2001). A comparison of restrictive and inclusive missing data strategies. Psychological Methods 6, 330-351.
39. Wiggins RD, Sacker A (2002). Strategies for handling missing data in SEM: a user's perspective. In: Marcoulides GA, Moustaki I, ed. Latent variable and latent structure models. Lawrence Erlbaum, Mahwah, NJ, pp. 105-120.
40. Olinsky A, Chen S, Harlow L (2003). The comparative efficiency of imputation for missing data in structural equation modeling. European Journal of Operational Research 151, 53-79.
41. Meng XL (1994). Multiple-imputation inferences with uncongenial sources of input (with discussion). Statistical Science 10, 538-573.
42. Schafer JL (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistic Neerlandica 57, 19-35.
43. Longford NT, Ely M, Hardy R, Wadsworth MEJ (2000). Handling missing data in diaries of alcohol consumption. Journal of the Royal Statistical Society A 163, 381-402.
44. Di Stavola BR, Silva ID, McCormack V, Hardy R, Kuh DJ, Wadsworth MEJ (2004). Childhood growth and breast cancer. American Journal of Epidemiology 159, 671-682.
45. Molenberghs G, Williams PL, Lipsitz SR (2002). Prediction of survival and opportunistic infections in HIV-infected patients: a comparison of multiple imputation methods of incomplete CD4 counts. Statistics in Medicine 21, 1387-1408.
46. Van Buuren S, Boshuizen HC, Knook DL (1999). Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine 18, 681-694.
47. Paddock SM (2002). Bayesian nonparametric multiple imputation of partially observed data with ignorable nonresponse. Biometrika 89, 529-538.
48. Demissie S, LaValley MP, Horton NJ, Glynn RJ, Cupples LA (2003). Bias due to missing exposure data using complete-case analysis in the proportional hazards regression model. Statistics in Medicine 22, 545-557.
49. Horton NJ, Lipsitz SR (2001). Multiple imputation in practice: comparison of software packages for regression models with missing variables. American Statistician 55, 244-254.