351 related articles for article (PubMed ID: 16135036)
1. 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]
2. 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]
3. HIV viral dynamic models with dropouts and missing covariates.
Wu L
Stat Med; 2007 Jul; 26(17):3342-57. PubMed ID: 17221835
[TBL] [Abstract][Full Text] [Related]
4. Simple adjustments for randomized trials with nonrandomly missing or censored outcomes arising from informative covariates.
Baker SG; Fitzmaurice GM; Freedman LS; Kramer BS
Biostatistics; 2006 Jan; 7(1):29-40. PubMed ID: 15923407
[TBL] [Abstract][Full Text] [Related]
5. A bias correction in testing treatment efficacy under informative dropout in clinical trials.
Kong F; Chen YF; Jin K
J Biopharm Stat; 2009 Nov; 19(6):980-1000. PubMed ID: 20183460
[TBL] [Abstract][Full Text] [Related]
6. A comparison of two methods for the estimation of precision with incomplete longitudinal data, jointly modelled with a time-to-event outcome.
Touloumi G; Babiker AG; Kenward MG; Pocock SJ; Darbyshire JH
Stat Med; 2003 Oct; 22(20):3161-75. PubMed ID: 14518021
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. A residuals-based transition model for longitudinal analysis with estimation in the presence of missing data.
Koru-Sengul T; Stoffer DS; Day NL
Stat Med; 2007 Jul; 26(17):3330-41. PubMed ID: 17124699
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies.
Wu L
Stat Med; 2004 Jun; 23(11):1715-31. PubMed ID: 15160404
[TBL] [Abstract][Full Text] [Related]
12. Structural inference in transition measurement error models for longitudinal data.
Pan W; Lin X; Zeng D
Biometrics; 2006 Jun; 62(2):402-12. PubMed ID: 16918904
[TBL] [Abstract][Full Text] [Related]
13. Joint modeling of longitudinal data and informative dropout time in the presence of multiple changepoints.
Ghosh P; Ghosh K; Tiwari RC
Stat Med; 2011 Mar; 30(6):611-26. PubMed ID: 21337357
[TBL] [Abstract][Full Text] [Related]
14. A hybrid model for nonignorable dropout in longitudinal binary responses.
Wilkins KJ; Fitzmaurice GM
Biometrics; 2006 Mar; 62(1):168-76. PubMed ID: 16542243
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A copula model for repeated measurements with non-ignorable non-monotone missing outcome.
Shen C; Weissfeld L
Stat Med; 2006 Jul; 25(14):2427-40. PubMed ID: 16143999
[TBL] [Abstract][Full Text] [Related]
17. Bayesian analysis for generalized linear models with nonignorably missing covariates.
Huang L; Chen MH; Ibrahim JG
Biometrics; 2005 Sep; 61(3):767-80. PubMed ID: 16135028
[TBL] [Abstract][Full Text] [Related]
18. Joint inference for nonlinear mixed-effects models and time to event at the presence of missing data.
Wu L; Hu XJ; Wu H
Biostatistics; 2008 Apr; 9(2):308-20. PubMed ID: 17728318
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]