182 related articles for article (PubMed ID: 11276032)
1. An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs.
Fitzmaurice GM; Laird NM; Shneyer L
Stat Med; 2001 Apr; 20(7):1009-21. PubMed ID: 11276032
[TBL] [Abstract][Full Text] [Related]
2. Examining the influence of drop-outs in a follow-up of maintained opiate users.
Encrenaz G; Rondeau V; Messiah A; Auriacombe M
Drug Alcohol Depend; 2005 Sep; 79(3):303-10. PubMed ID: 16102374
[TBL] [Abstract][Full Text] [Related]
3. Conditional mixed models adjusting for non-ignorable drop-out with administrative censoring in longitudinal studies.
Li J; Schluchter MD
Stat Med; 2004 Nov; 23(22):3489-503. PubMed ID: 15505888
[TBL] [Abstract][Full Text] [Related]
4. On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out.
Demirtas H; Schafer JL
Stat Med; 2003 Aug; 22(16):2553-75. PubMed ID: 12898544
[TBL] [Abstract][Full Text] [Related]
5. Sensitivity analysis of longitudinal normal data with drop-outs.
Minini P; Chavance M
Stat Med; 2004 Apr; 23(7):1039-54. PubMed ID: 15057877
[TBL] [Abstract][Full Text] [Related]
6. Multiple imputation under Bayesianly smoothed pattern-mixture models for non-ignorable drop-out.
Demirtas H
Stat Med; 2005 Aug; 24(15):2345-63. PubMed ID: 15977286
[TBL] [Abstract][Full Text] [Related]
7. An index of local sensitivity to nonignorable drop-out in longitudinal modelling.
Ma G; Troxel AB; Heitjan DF
Stat Med; 2005 Jul; 24(14):2129-50. PubMed ID: 15909292
[TBL] [Abstract][Full Text] [Related]
8. Marginalized transition models for longitudinal binary data with ignorable and non-ignorable drop-out.
Kurland BF; Heagerty PJ
Stat Med; 2004 Sep; 23(17):2673-95. PubMed ID: 15316952
[TBL] [Abstract][Full Text] [Related]
9. [Meta-analysis of the Italian studies on short-term effects of air pollution].
Biggeri A; Bellini P; Terracini B;
Epidemiol Prev; 2001; 25(2 Suppl):1-71. PubMed ID: 11515188
[TBL] [Abstract][Full Text] [Related]
10. Missing covariates in longitudinal data with informative dropouts: bias analysis and inference.
Roy J; Lin X
Biometrics; 2005 Sep; 61(3):837-46. PubMed ID: 16135036
[TBL] [Abstract][Full Text] [Related]
11. A local sensitivity analysis approach to longitudinal non-Gaussian data with non-ignorable dropout.
Xie H
Stat Med; 2008 Jul; 27(16):3155-77. PubMed ID: 17948917
[TBL] [Abstract][Full Text] [Related]
12. Analysis of change in the presence of informative censoring: application to a longitudinal clinical trial of progressive renal disease.
Schluchter MD; Greene T; Beck GJ
Stat Med; 2001 Apr; 20(7):989-1007. PubMed ID: 11276031
[TBL] [Abstract][Full Text] [Related]
13. Mixed effects logistic regression models for longitudinal binary response data with informative drop-out.
Ten Have TR; Kunselman AR; Pulkstenis EP; Landis JR
Biometrics; 1998 Mar; 54(1):367-83. PubMed ID: 9544529
[TBL] [Abstract][Full Text] [Related]
14. An autoregressive linear mixed effects model for the analysis of longitudinal data which include dropouts and show profiles approaching asymptotes.
Funatogawa T; Funatogawa I; Takeuchi M
Stat Med; 2008 Dec; 27(30):6351-66. PubMed ID: 18767204
[TBL] [Abstract][Full Text] [Related]
15. An index of local sensitivity to non-ignorability for multivariate longitudinal mixed data with potential non-random dropout.
Mahabadi SE; Ganjali M
Stat Med; 2010 Jul; 29(17):1779-92. PubMed ID: 20658547
[TBL] [Abstract][Full Text] [Related]
16. Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates.
Parzen M; Lipsitz SR; Fitzmaurice GM; Ibrahim JG; Troxel A
Stat Med; 2006 Aug; 25(16):2784-96. PubMed ID: 16345018
[TBL] [Abstract][Full Text] [Related]
17. Handling drop-out in longitudinal studies.
Hogan JW; Roy J; Korkontzelou C
Stat Med; 2004 May; 23(9):1455-97. PubMed ID: 15116353
[TBL] [Abstract][Full Text] [Related]
18. Semiparametric regression analysis of longitudinal data with informative drop-outs.
Lin DY; Ying Z
Biostatistics; 2003 Jul; 4(3):385-98. PubMed ID: 12925506
[TBL] [Abstract][Full Text] [Related]
19. Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency.
Schildcrout JS; Heagerty PJ
Biostatistics; 2005 Oct; 6(4):633-52. PubMed ID: 15917376
[TBL] [Abstract][Full Text] [Related]
20. A mixed effects model for the analysis of ordinal longitudinal pain data subject to informative drop-out.
Pulkstenis E; Ten Have TR; Landis JR
Stat Med; 2001 Feb; 20(4):601-22. PubMed ID: 11223903
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]