The nature of sensitivity in monotone missing not at random models

Computational Statistics and Data Analysis

Volumn 50, Issue 3, 2006, Pages 830-858

  Jansen, Ivy ^a   Hens, Niel ^a   Molenberghs, Geert ^a   Aerts, Marc ^a   Verbeke, Geert ^b   Kenward, Michael G ^c  

^a HASSELT UNIVERSITY   (Belgium)

^b UNIVERSITY OF LEUVEN   (Belgium)

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

Author keywords

Ignorability; Likelihood ratio test; Linear mixed model; Local influence; Missing at random; Missing not at random; Sensitivity analysis

Indexed keywords

MATHEMATICAL MODELS;

IGNORABILITY; LIKELIHOOD RATIO TEST; LOCAL INFLUENCE; MISSING NOT AT RANDOM;

SENSITIVITY ANALYSIS;

EID: 25444484182     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2004.10.009     Document Type: Article

References (48)

1. A. Afifi, and R. Elashoff Missing observations in multivariate statistics I review of the literature J. Amer. Statist. Assoc. 61 1966 595 604
2. R. Beran Prepivoting test statistics a bootstrap view of asymptotic refinements J. Amer. Statist. Assoc. 83 1988 687 697
3. J. Besag, P. Green, D. Higdon, and K. Mengersen Bayesian computation and stochastic systems Statist. Sci. 10 1995 3 66
4. S. Chatterjee, and A.S. Hadi Sensitivity Analysis in Linear Regression 1988 Wiley New York
5. R.D. Cook Assessment of local influence J. Roy. Statist. Soc. Ser. B 48 1986 133 169
6. J.B. Copas, and H.G. Li Inference from non-random samples (with discussion) J. Roy. Statist. Soc. Ser. B 59 1997 55 96
7. A.C. Davison, and D.V. Hinkley Bootstrap Methods and their Application 1997 Cambridge University Press Cambridge
8. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm (with discussion) J. Roy. Statist. Soc. Ser. B 39 1977 1 38
9. P.D. Diggle, and M.G. Kenward Informative dropout in longitudinal data analysis (with discussion) Appl. Statist. 43 1994 49 93
10. D. Draper Assessment and propagation of model uncertainty (with discussion) J. Roy. Statist. Soc. Ser. B 57 1995 45 97
11. B. Efron Bootstrap methods another look at the jackknife Ann. Statist. 7 1979 1 25
12. B. Efron, and R.J. Tibshirani An Introduction to the Bootstrap 1998 Chapman & Hall/CRC Boca Raton
13. G.M. Fitzmaurice, G. Molenberghs, and S.R. Lipsitz Regression models for longitudinal binary responses with informative dropouts J. Roy. Statist. Soc. Ser. B 57 1995 691 704
14. R.J. Glynn, N.M. Laird, and D.B. Rubin Selection modelling versus mixture modelling with nonignorable nonresponse H. Wainer Drawing Inferences from Self-Selected Samples 1986 Springer New York 115 142
15. H.O. Hartley, and R. Hocking The analysis of incomplete data Biometrics 27 1971 783 808
16. J.J. Heckman The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models Ann. Econom. Soc. Meas. 5 1976 475 492
17. Hens, N., Aerts, M., Molenberghs, G., Thijs, H., Verbeke, G., 2004. Kernel weighted influence measures. Comput. Statist. Data Anal. to appear.
18. I. Jansen, G. Molenberghs, M. Aerts, H. Thijs, and K. Van Steen A local influence approach applied to binary data from a psychiatric study Biometrics 59 2003 409 418
19. Jansen, I., Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C., 2004. Analyzing incomplete binary longitudinal clinical trial data. Statist. Sci., submitted for publication.
20. M.G. Kenward Selection models for repeated measurements with nonrandom dropout an illustration of sensitivity Statist. Med. 17 1998 2723 2732
21. M.G. Kenward, and G. Molenberghs Parametric models for incomplete continuous and categorical longitudinal studies data Statist. Methods Med. Res. 8 1999 51 83
22. M.G. Kenward, E.J.T. Goetghebeur, and G. Molenberghs Sensitivity analysis of incomplete categorical data Statist. Model. 1 2001 31 48
23. N.M. Laird Discussion to Diggle, P.J. and Kenward, M.G. informative dropout in longitudinal data analysis Appl. Statist. 43 1994 84
24. N.M. Laird, and J.H. Ware Random effects models for longitudinal data Biometrics 38 1982 963 974
25. P.W. Lavori, R. Dawson, and D. Shera A multiple imputation strategy for clinical trials with truncation of patient data Statist. Med. 14 1995 1913 1925
26. E. Lesaffre, and G. Verbeke Local influence in linear mixed models Biometrics 54 1998 570 582
27. R.J.A. Little Pattern-mixture models for multivariate incomplete data J. Amer. Statist. Assoc. 88 1993 125 134
28. R.J.A. Little Discussion to Diggle, P.J., Kenward, M.G. informative dropout in longitudinal data analysis Appl. Statist. 43 1994 78
29. R.J.A. Little, and D.B. Rubin Statistical Analysis with Missing Data 1987 Wiley New York
30. C.H. Mallinckrodt, W.S. Clark, R.J. Carroll, and G. Molenberghs Assessing response profiles from incomplete longitudinal clinical trial data under regulatory considerations J. Biopharm. Statist. 13 2003 179 190
31. C.H. Mallinckrodt, T.M. Sanger, S. Dube, D.J. Debrota, G. Molenberghs, R.J. Carroll, W.M. Zeigle Protter, and G.D. Tollefson Assessing and interpreting treatment effects in longitudinal clinical trials with missing data Biol. Psychiat. 53 2003 754 760
32. G. Molenberghs, M.G. Kenward, and E. Lesaffre The analysis of longitudinal ordinal data with nonrandom dropout Biometrika Series 84 1997 33 44
33. G. Molenberghs, E. Goetghebeur, S.R. Lipsitz, and M.G. Kenward Non-random missingness in categorical data strengths and limitations Amer. Statist. 53 1999 110 118
34. G. Molenberghs, G. Verbeke, H. Thijs, E. Lesaffre, and M.G. Kenward Mastitis in dairy cattle local influence to assess sensitivity of the dropout process Comput. Statist. Data Anal. 37 2001 93 113
35. G. Molenberghs, H. Thijs, I. Jansen, C. Beunckens, M.G. Kenward, C. Mallinckrodt, and R.J. Carroll Analyzing incomplete longitudinal clinical trial data Biostatistics 5 2004 445 464
36. J.A. Nelder, and R. Mead A simplex method for function minimisation Comput. J. 7 1965 303 313
37. E.V. Nordheim Inference from nonrandomly missing categorical data an example from a genetic study on Turner's syndrome J. Amer. Statist. Assoc. 79 1984 772 780
38. A. Rotnitzky, D.R. Cox, M. Bottai, and J. Robins Likelihood-based inference with singular information matrix Bernouilli 6 2 2000 243 284
39. D.B. Rubin Multiple imputation for Nonresponse in Surveys 1987 Wiley New York
40. D.B. Rubin Discussion to Diggle, P.J. and Kenward, M.G. informative dropout in longitudinal data analysis Appl. Statist. 43 1994 80 82
41. D.O. Scharfstein, A. Rotnizky, and J.M. Robins Adjusting for non-ignorable drop-out using semiparametric nonresponse models (with discussion) J. Amer. Statist. Assoc. 94 1999 1096 1146
42. H. Thijs, G. Molenberghs, and G. Verbeke The milk protein trial influence analysis of the dropout process Biometrical J. 42 2000 617 646
43. H. Thijs, G. Molenberghs, B. Michiels, G. Verbeke, and D. Curran Strategies to fit pattern-mixture models Biostatistics 3 2002 245 265
44. W. Vach, and M. Blettner Logistic regression with incompletely observed categorical covariates-investigating the sensitivity against violation of the missing at random assumption Statist. Med. 12 1995 1315 1330
45. K. Van Steen, G. Molenberghs, G. Verbeke, and H. Thijs A local influence approach to sensitivity analysis of incomplete longitudinal ordinal data Statist. Model. Internat. J. 1 2001 125 142
46. G. Verbeke, and G. Molenberghs Linear Mixed Models for Longitudinal Data 2000 Springer New York
47. G. Verbeke, G. Molenberghs, H. Thijs, E. Lesaffre, and M.G. Kenward Sensitivity analysis for non-random dropout a local influence approach Biometrics 57 2001 43 50
48. Verdonck, A., De Ridder, L., Verbeke, G., Bourguignon, J.P., Carels, C., Kuhn, E.R., Darras, V., de Zegher, F., 1998. Comparative effects of neonatal and prepubertal castration on craniofacial growth in rats. Archives of Oral Biology 43, 861-871.