Observations longitudinales incomplètes : de la modélisation des observations disponibles à l'analyse de sensibilité

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1. BAKER S.G., ROSENBERGER W.F. and DERSIMONIAN R. (1992). Closed-form estimates for missing counts in two-way contengency tables. Statistics in medicine 11, 643-657. 
2. BIRMINGHAM J., ROTNITZKY A. and FITZMAURICE G.M. (2003). Pattern-mixture and selection models for analysing longitudinal data with monotone missing patterns. Journal of the royal statistical society, Serie B 65, 275-297. Zbl1063.62039MR1959827
3. BISHOP Y.M.M., FIENBERG S.E. and HOLLAND P.W. (1975). Discrete multivariate analysis : theoy and practice, MIT Press : Cambridge, Massachussetts. Zbl0332.62039MR381130
4. CHAVANCE M. et MANFREDI R. (2000). Modélisation d'observations incomplètes. Revue d'épidemiologie et de santé publique 48, 389-400. 
5. DEMPSTER A.P., LAIRD N.M. and RUBIN D.B. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). Journal of the royal statistical society, Serie B 39, 1-38. Zbl0364.62022MR501537
6. DIGGLE P.J. and KENWARD M.G. (1994). Informative drop-out in longitudinal data analysis (with discussion). Applied statistics 43, 49-93. Zbl0825.62010
7. DIGGLE P.J., HEAGERTY P., LIANG K.Y. and ZEGER S.L. (2002). Analysis of longitudinal data, 2nd edition, Oxford University Press : Oxford. Zbl1031.62002MR2049007
8. JAMSHIDIAN M. and JENNRICH R.I. (2000). Standard errors for EM estimation. Journal of the royal statistical society, Serie B 62, 257-270. MR1749538
9. KENWARD M.G. (1998). Selection and models for repeated measurements with nonrandom dropout : An illustration of sensitivity. Statistics in medicine 17, 2723-2732. 
10. LITTLE R.J.A. (1993). Pattern-mixture models for multivariate incomplete date. Journal of the american statistical association 88, 125-134. Zbl0775.62134
11. LITTLE R.J.A. ( 1994a). A class of pattern-mixture models for normal data. Biometrika 81, 471-483. Zbl0816.62023MR1311091
12. LITTLE R.J.A. ( 1994b). Discussion to Diggle and Kenward : Informative drop-out in longitudinal data analysis. Applied statistics 43, 78. 
13. LITTLE R.J.A. (1995). Modelling the drop-out mechanism in repeated measures studies. Journal of the american statistical association 90, 1112-1121. Zbl0841.62099MR1354029
14. LITTLE R.J.A. and RUBIN D.B. (1987). Statistical analysis with missing data Wiley : New-York. Zbl0665.62004MR890519
15. MININI P. and CHAVANCE M. ( 2004a). Sensitivity analysis of longitudinal normal data with drop-outs. Statistics in medicine 23, 1039-1054. Zbl1069.62101
16. MININI P. and CHAVANCE M. ( 2004b). Sensitivity analysis of longitudinal binary data with non-monotone missing values. Biostatistics (sous presse). Zbl1069.62101
17. MOLENBERGHS G., GOETGHEBEUR E., LIPSITZ S.R. and KENWARD M.G. (1999). Nonrandom missingness in categorical data : strengths and limitations. The American Statistician 53, 110-118. 
18. MOLENBERGHS G., KENWARD M.G. and GOETGHEBEUR E. (2001). Sensitivity analysis for incomplete contingency tables : the Slovenian plebiscite case. Applied statistics 50, 15-29. Zbl1021.62045
19. MOLENBERGHS G., THIJS H., MICHIELS B., VERBEKE G. and KENWARD M.G. (2004). Pattern-mixture models. Journal de la Société Française de Statistique 145, 2, 49-77. 
20. ROBINS J.M., ROTNITZKY A. and ZHAO L.P. (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. Zbl0818.62042MR1325118
21. ROTNITZKY A., ROBINS J.M. and SCHARFSTEIN D. (1998). Semiparametric regression for repeated outcomes with nonignorable nonresponse. Journal of the american statistical association 93, 1321-1339. Zbl1064.62520MR1666631
22. ROTNITZKY A., SCHARFSTEIN D., SU T.L. and ROBINS J.M. (2001). Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring. Biometrics 57, 103-113. Zbl1209.62251MR1833295
23. RUBIN D.B. (1987). Multiple imputations for nonresponse in surveys. Wiley : New-York. MR899519
24. RUBIN D.B. (1994). Discussion to Diggle and Kenward : Informative drop-out in longitudinal data analysis. Applied statistics 43, 80-82. 
25. RUBIN D.B., STERN H.S., and VEHOVAR V. (1995). Handling "don't know" in survey responses : the case of the Slovanian plebiscite. Journal of the american statistical association 90, 822-828. 
26. SCHARFSTEIN D., ROTNITZKY A. and ROBINS J.M. (1999). Adjusting for nonignorable dropout using semiparametric nonresponse models (with discussion). Journal of the american statistical association 94, 1096-1146. Zbl1072.62644MR1731478
27. UNNEBRINK K. and WINDELER J. (1999). Sensitivity analysis by worst and best case assessment : is it really sensitive?. Drug information journal 33, 835-839. 

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