145 related articles for article (PubMed ID: 18351709)
1. Bayesian semiparametric regression for longitudinal binary processes with missing data.
Su L; Hogan JW
Stat Med; 2008 Jul; 27(17):3247-68. PubMed ID: 18351709
[TBL] [Abstract][Full Text] [Related]
2. Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function.
Su L; Daniels MJ
Stat Med; 2015 May; 34(12):2004-18. PubMed ID: 25762065
[TBL] [Abstract][Full Text] [Related]
3. Bayesian nonparametric mixed-effects joint model for longitudinal-competing risks data analysis in presence of multiple data features.
Lu T
Stat Methods Med Res; 2017 Oct; 26(5):2407-2423. PubMed ID: 26265770
[TBL] [Abstract][Full Text] [Related]
4. Marginalized models for moderate to long series of longitudinal binary response data.
Schildcrout JS; Heagerty PJ
Biometrics; 2007 Jun; 63(2):322-31. PubMed ID: 17688485
[TBL] [Abstract][Full Text] [Related]
5. Bayesian inference on mixed-effects location scale models with skew-t distribution and mismeasured covariates for longitudinal data.
Lu T
Stat Med; 2017 Jul; 36(16):2614-2629. PubMed ID: 28421622
[TBL] [Abstract][Full Text] [Related]
6. Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness.
Chen C; Shen B; Zhang L; Xue Y; Wang M
Biometrics; 2019 Sep; 75(3):950-965. PubMed ID: 31004449
[TBL] [Abstract][Full Text] [Related]
7. Estimation in regression models for longitudinal binary data with outcome-dependent follow-up.
Fitzmaurice GM; Lipsitz SR; Ibrahim JG; Gelber R; Lipshultz S
Biostatistics; 2006 Jul; 7(3):469-85. PubMed ID: 16428260
[TBL] [Abstract][Full Text] [Related]
8. Fully Bayesian inference under ignorable missingness in the presence of auxiliary covariates.
Daniels MJ; Wang C; Marcus BH
Biometrics; 2014 Mar; 70(1):62-72. PubMed ID: 24571539
[TBL] [Abstract][Full Text] [Related]
9. Mixed-effects joint models with skew-normal distribution for HIV dynamic response with missing and mismeasured time-varying covariate.
Huang Y; Chen J; Yan C
Int J Biostat; 2012 Nov; 8(1):. PubMed ID: 23192055
[TBL] [Abstract][Full Text] [Related]
10. Inference methods for saturated models in longitudinal clinical trials with incomplete binary data.
Song JX
Pharm Stat; 2006; 5(4):295-304. PubMed ID: 17128429
[TBL] [Abstract][Full Text] [Related]
11. A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time.
Su L
Biostatistics; 2012 Apr; 13(2):355-68. PubMed ID: 22133756
[TBL] [Abstract][Full Text] [Related]
12. Marginalized transition models and likelihood inference for longitudinal categorical data.
Heagerty PJ
Biometrics; 2002 Jun; 58(2):342-51. PubMed ID: 12071407
[TBL] [Abstract][Full Text] [Related]
13. A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials.
Wu J; Chen MH; Schifano ED; Ibrahim JG; Fisher JD
Stat Med; 2019 Dec; 38(30):5565-5586. PubMed ID: 31691322
[TBL] [Abstract][Full Text] [Related]
14. Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with multiple features.
Huang Y; Chen J
Stat Med; 2016 Dec; 35(30):5666-5685. PubMed ID: 27592848
[TBL] [Abstract][Full Text] [Related]
15. Joint two-part Tobit models for longitudinal and time-to-event data.
Dagne GA
Stat Med; 2017 Nov; 36(26):4214-4229. PubMed ID: 28795414
[TBL] [Abstract][Full Text] [Related]
16. A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome.
Roy J; Lum KJ; Daniels MJ
Biostatistics; 2017 Jan; 18(1):32-47. PubMed ID: 27345532
[TBL] [Abstract][Full Text] [Related]
17. Partially linear mixed-effects joint models for skewed and missing longitudinal competing risks outcomes.
Lu T; Lu M; Wang M; Zhang J; Dong GH; Xu Y
J Biopharm Stat; 2019; 29(6):971-989. PubMed ID: 29252088
[TBL] [Abstract][Full Text] [Related]
18. Bayesian semiparametric nonlinear mixed-effects joint models for data with skewness, missing responses, and measurement errors in covariates.
Huang Y; Dagne G
Biometrics; 2012 Sep; 68(3):943-53. PubMed ID: 22150787
[TBL] [Abstract][Full Text] [Related]
19. Varying-coefficient models for longitudinal processes with continuous-time informative dropout.
Su L; Hogan JW
Biostatistics; 2010 Jan; 11(1):93-110. PubMed ID: 19837655
[TBL] [Abstract][Full Text] [Related]
20. Analyzing longitudinal binary data in clinical studies.
Li Y; Feng D; Sui Y; Li H; Song Y; Zhan T; Cicconetti G; Jin M; Wang H; Chan I; Wang X
Contemp Clin Trials; 2022 Apr; 115():106717. PubMed ID: 35240309
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
