402 related articles for article (PubMed ID: 9544529)
1. 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]
2. 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]
3. 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]
4. 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]
5. Random effects probit and logistic regression models for three-level data.
Gibbons RD; Hedeker D
Biometrics; 1997 Dec; 53(4):1527-37. PubMed ID: 9423267
[TBL] [Abstract][Full Text] [Related]
6. Estimating treatment efficacy over time: a logistic regression model for binary longitudinal outcomes.
Choi L; Dominici F; Zeger SL; Ouyang P
Stat Med; 2005 Sep; 24(18):2789-805. PubMed ID: 16134133
[TBL] [Abstract][Full Text] [Related]
7. A mixed-effects model for cognitive decline with non-monotone non-response from a two-phase longitudinal study of dementia.
Shen C; Gao S
Stat Med; 2007 Jan; 26(2):409-25. PubMed ID: 16345034
[TBL] [Abstract][Full Text] [Related]
8. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random.
Preisser JS; Lohman KK; Rathouz PJ
Stat Med; 2002 Oct; 21(20):3035-54. PubMed ID: 12369080
[TBL] [Abstract][Full Text] [Related]
9. Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches.
Lane P
Pharm Stat; 2008; 7(2):93-106. PubMed ID: 17351897
[TBL] [Abstract][Full Text] [Related]
10. Quantile regression for longitudinal data using the asymmetric Laplace distribution.
Geraci M; Bottai M
Biostatistics; 2007 Jan; 8(1):140-54. PubMed ID: 16636139
[TBL] [Abstract][Full Text] [Related]
11. An approximate generalized linear model with random effects for informative missing data.
Follmann D; Wu M
Biometrics; 1995 Mar; 51(1):151-68. PubMed ID: 7766771
[TBL] [Abstract][Full Text] [Related]
12. Longitudinal and repeated cross-sectional cluster-randomization designs using mixed effects regression for binary outcomes: bias and coverage of frequentist and Bayesian methods.
Localio AR; Berlin JA; Have TR
Stat Med; 2006 Aug; 25(16):2720-36. PubMed ID: 16345043
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Random effects and latent processes approaches for analyzing binary longitudinal data with missingness: a comparison of approaches using opiate clinical trial data.
Albert PS; Follmann DA
Stat Methods Med Res; 2007 Oct; 16(5):417-39. PubMed ID: 17656452
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Intent-to-treat analysis for longitudinal studies with drop-outs.
Little R; Yau L
Biometrics; 1996 Dec; 52(4):1324-33. PubMed ID: 8962456
[TBL] [Abstract][Full Text] [Related]
17. [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]
18. A selection model for longitudinal binary responses subject to non-ignorable attrition.
Alfò M; Maruotti A
Stat Med; 2009 Aug; 28(19):2435-50. PubMed ID: 19424960
[TBL] [Abstract][Full Text] [Related]
19. Inference using conditional logistic regression with missing covariates.
Lipsitz SR; Parzen M; Ewell M
Biometrics; 1998 Mar; 54(1):295-303. PubMed ID: 9544523
[TBL] [Abstract][Full Text] [Related]
20. Marginal analysis of incomplete longitudinal binary data: a cautionary note on LOCF imputation.
Cook RJ; Zeng L; Yi GY
Biometrics; 2004 Sep; 60(3):820-8. PubMed ID: 15339307
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
