Sensitivity analysis of continuous incomplete longitudinal outcomes

Statistica Neerlandica

Volumn 57, Issue 1, 2003, Pages 112-135

  Molenberghs, Geert ^a   Thijs, Herbert ^a   Kenward, Michael G ^b   Verbeke, Geert ^c  

^a HASSELT UNIVERSITY   (Belgium)

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

^c UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Local influence; Missing data; Multiple imputation; Pattern mixture model; Selection model

Indexed keywords

EID: 28444468503     PISSN: 00390402     EISSN: 14679574     Source Type: Journal    
DOI: 10.1111/1467-9574.00224     Document Type: Review

References (40)

1. AFIFI, A. and R. ELASHOFF (1966), Missing observations in multivariate statistics I: review of the literature, Journal of the American Statistical Association 61, 595-604.
2. BECKMAN, R. J., C. J. NACHTSHEIM and R. D. COOK (1987), Diagnostics for mixed-model analysis of variance, Technometrics 27, 413-426.
3. CHATTERJEE, S. and A. S. HADI (1988), Sensitivity analysis in linear regression, New York: Wiley.
4. COOK, R. D. (1977), Detection of influential observations in linear regression, Technometrics 19, 15-18.
5. COOK, R. D. (1979), Influential observations in linear regression, Journal of the American Statistical Association 74, 169-174.
6. COOK, R. D. (1986), Assessment of local influence, Journal of the Royal Statistical Society, Series B 48, 133-169.
7. COOK, R. D. and S. WEISBERG (1982), Residuals and influence in regression, London: Chapman & Hall.
8. CULLIS, B. R. (1994), Discussion to P. J. DIGGLE and M. G. KENWARD: Informative dropout in longitudinal data analysis, Applied Statistics 43, 79-80.
9. CURRAN, D., F. PIGNATTI and G. MOLENBERGHS (1998), Milk protein trial: informative dropout versus random drop-in, Technical Report.
10. DEMPSTER, A. P., N. M. LAIRD and D. B. RUBIN (1977), Maximum likelihood from incomplete data via the EM algorithm (with discussion), Journal of the Royal Statistical Society, Series B 39, 1-38.
11. DIGGLE, P. J. (1990), Time series: a biostatistical introduction, Oxford: Oxford University Press.
12. DIGGLE, P. J. and M. G. KENWARD (1994), Informative drop-out in longitudinal data analysis (with discussion), Applied Statistics 43, 49-93.
13. DRAPER, D. (1995), Assessment and propagation of model uncertainty (with discussion), Journal of the Royal Statistical Society, Series B 57, 45-97.
14. GLYNN, R. J., N. M. LAIRD and D. B. RUBIN (1986), Selection modeling versus mixture modeling with nonignorable nonresponse, in H. WAINER (ed.), Drawing inferences from self-selected samples, 115-142, New York: Springer-Verlag.
15. HARTLEY, H. O. and R. HOCKING (1971), The analysis of incomplete data, Biometrics, 27, 7783-7808.
16. HOGAN, J. W. and N. M. LAIRD (1997), Mixture models for the joint distribution of repeated measures and event times, Statistics in Medicine 16, 239-258.
17. KENWARD, M. G. (1998), Selection models for repeated measurements with nonrandom dropout: an illustration of sensitivity, Statistics in Medicine 17, 2723-2732.
18. KENWARD, M. G., G. MOLENBERGHS and H. THIJS (2001), Pattern-mixture models with proper time dependence, Submitted for publication.
19. LAIRD, N. M. (1994), Discussion to P. J. DIGGLE and M. G. KENWARD: Informative dropout in longitudinal data analysis, Applied Statistics 43, 84.
20. LESAFFRE, E. and G. VERBEKE (1998), Local influence in linear mixed models, Biometrics 54, 570-582.
21. LITTLE, R. J. A. (1993), Pattern-mixture models for multivariate incomplete data, Journal of the American Statistical Association 88, 125-134.
22. LITTLE, R. J. A. (1994), A class of pattern-mixture models for normal incomplete data, Biometrika 81, 471-483.
23. LITTLE, R. J. A. (1995), Modeling the drop-out mechanism in repeated-measures studies, Journal of the American Statistical Association 90, 1112-1121.
24. LITTLE, R. J. A. and RUBIN D. B. (1987), Statistical analysis with missing data, New York: Wiley.
25. MOLENBERGHS, G., B. MICHIELS, M. G. KENWARD and P. J. DIGGLE (1998), Missing data mechanisms and pattern-mixture models, Statistica Neerlandica 52, 153-161.
26. MOLENBERGHS, G., E. J. T. GOETGHEBEUR, S. R. LIPSITZ and M. G. KENWARD (1999), Non-random missingness in categorical data: strengths and limitations, The American Statistician 53, 110-118.
27. MOLENBERGHS, G., M. G. KENWARD and E. LESAFFRE (1997), The analysis of longitudinal ordinal data with non-random dropout, Biometrika 84, 33-44.
28. MOLENBERGHS, G., B. MICHIELS and S. R. LIPSITZ (1999), Selection models and pattern-mixture models for incomplete categorical data with covariates, Biometrics 55, 978-983.
29. RUBIN, D. B. (1976), Inference and missing data, Biometrika 63, 581-592.
30. RUBIN, D. B. (1987), Multiple imputation for nonresponse in surveys, New York: Wiley.
31. RUBIN, D. B. (1994), Discussion to P. J. DIGGLE and M. G. KENWARD: Informative dropout in longitudinal data analysis, Applied Statistics 43, 80-82.
32. SCHAFER, J. L. (1997), Analysis of incomplete multivariate data, London: Chapman & Hall.
33. SHEINER, L. B., S. L. BEALE and A. DUNNE (1997), Analysis of nonrandomly censored ordered categorical longitudinal data from analgesic trials, Journal of the American Statistical Association 92, 1235-1244.
34. SMITH, D. M., B. ROBERTSON and P. J. DIGGLE (1996), Object-oriented software for the analysis of longitudinal data in S, Technical Report MA 96/192. Department of Mathematics and Statistics, University of Lancaster, LA1 4YF, United Kingdom.
35. THUS, H., G. MOLENBERGHS, B. MICHIELS, G. VERBEKE and D. CURRAN (2001), Strategies to fit pattern-mixture models, Biostatistics 3, 245-265.
36. VERBEKE, G. and G. MOLENBERGHS (2000), Linear mixed models for longitudinal data, New York: Springer-Verlag.
37. VERBEKE, G., G. MOLENBERGHS, H. THIJS, E. LESAFFRE and M. G. KENWARD (2001), Sensitivity analysis for non-random dropout: a local influence approach, Biometrics 57, 7-14.
38. VERBYLA, A. P. and B. R. CULLIS (1990), Modelling in repeated measures experiments, Applied Statistics 39, 341-356.
39. WU, M. C. and K. R. BAILEY (1989), Estimation and comparison of changes in the presence of informative right censoring: conditional linear model, Biometrics 45, 939-955.
40. WU, M. C. and R. J. CARROLL (1988), Estimation and comparison of changes in the presence informative right censoring by modeling the censoring process, Biometrics 44, 175-188.