225 related articles for article (PubMed ID: 21306352)
21. Joint models for multivariate longitudinal and multivariate survival data.
Chi YY; Ibrahim JG
Biometrics; 2006 Jun; 62(2):432-45. PubMed ID: 16918907
[TBL] [Abstract][Full Text] [Related]
22. Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters.
Zeger SL; Diggle PJ
Biometrics; 1994 Sep; 50(3):689-99. PubMed ID: 7981395
[TBL] [Abstract][Full Text] [Related]
23. Shared parameter models for the joint analysis of longitudinal data and event times.
Vonesh EF; Greene T; Schluchter MD
Stat Med; 2006 Jan; 25(1):143-63. PubMed ID: 16025541
[TBL] [Abstract][Full Text] [Related]
24. Prospective analyses of HIV-1-specific proliferative responses, recall antigen proliferative responses, and clinical outcomes in an HIV-1-seropositive cohort.
Ratto-Kim S; Garner RP; Kim JH; Jagodzinski LL; Michael NL; Paris R; Redfield RR; Birx DL
J Infect Dis; 2004 Jun; 189(11):1988-95. PubMed ID: 15143464
[TBL] [Abstract][Full Text] [Related]
25. A characterization of missingness at random in a generalized shared-parameter joint modeling framework for longitudinal and time-to-event data, and sensitivity analysis.
Njagi EN; Molenberghs G; Kenward MG; Verbeke G; Rizopoulos D
Biom J; 2014 Nov; 56(6):1001-15. PubMed ID: 24947904
[TBL] [Abstract][Full Text] [Related]
26. Retinal microangiopathy in human immunodeficiency virus infection is related to higher human immunodeficiency virus-1 load in plasma.
Furrer H; Barloggio A; Egger M; Garweg JG;
Ophthalmology; 2003 Feb; 110(2):432-6. PubMed ID: 12578793
[TBL] [Abstract][Full Text] [Related]
27. Risk of reactivation of tuberculosis in the course of human immunodeficiency virus infection.
Di Perri G; Micciolo R; Vento S; Cruciani M; Marocco S; Carlotto A; Adami T; Mazzi R; Cazzadori A; Concia E
Eur J Med; 1993 May; 2(5):264-8. PubMed ID: 7902760
[TBL] [Abstract][Full Text] [Related]
28. Estimation for the optimal combination of markers without modeling the censoring distribution.
Chiang CT; Huang SY
Biometrics; 2009 Mar; 65(1):152-8. PubMed ID: 18422791
[TBL] [Abstract][Full Text] [Related]
29. On longitudinal prediction with time-to-event outcome: Comparison of modeling options.
Maziarz M; Heagerty P; Cai T; Zheng Y
Biometrics; 2017 Mar; 73(1):83-93. PubMed ID: 27438160
[TBL] [Abstract][Full Text] [Related]
30. Infection with GB virus C and reduced mortality among HIV-infected patients.
Tillmann HL; Heiken H; Knapik-Botor A; Heringlake S; Ockenga J; Wilber JC; Goergen B; Detmer J; McMorrow M; Stoll M; Schmidt RE; Manns MP
N Engl J Med; 2001 Sep; 345(10):715-24. PubMed ID: 11547740
[TBL] [Abstract][Full Text] [Related]
31. Secular trends in the survival of HIV-infected homosexual men in Amsterdam and Vancouver estimated from a death-included CD4-staged Markov model.
Hendriks JC; Craib KJ; Veugelers PJ; van Druten HA; Coutinho RA; Schechter MT; van Griensven GJ
Int J Epidemiol; 2000 Jun; 29(3):565-72. PubMed ID: 10869332
[TBL] [Abstract][Full Text] [Related]
32. CD4+ cell count 6 years after commencement of highly active antiretroviral therapy in persons with sustained virologic suppression.
Moore RD; Keruly JC
Clin Infect Dis; 2007 Feb; 44(3):441-6. PubMed ID: 17205456
[TBL] [Abstract][Full Text] [Related]
33. Dietary intake and counseling, weight maintenance, and the course of HIV infection.
Chlebowski RT; Grosvenor M; Lillington L; Sayre J; Beall G
J Am Diet Assoc; 1995 Apr; 95(4):428-32; quiz 433-5. PubMed ID: 7699184
[TBL] [Abstract][Full Text] [Related]
34. Individualized dynamic prediction of survival with the presence of intermediate events.
Papageorgiou G; Mokhles MM; Takkenberg JJM; Rizopoulos D
Stat Med; 2019 Dec; 38(30):5623-5640. PubMed ID: 31667885
[TBL] [Abstract][Full Text] [Related]
35. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking.
Rizopoulos D; Molenberghs G; Lesaffre EMEH
Biom J; 2017 Nov; 59(6):1261-1276. PubMed ID: 28792080
[TBL] [Abstract][Full Text] [Related]
36. A framework for the joint modeling of longitudinal diagnostic outcome data and latent infection status: application to investigating the temporal relationship between infection and disease.
Jones G; Johnson WO; Vink WD; French N
Biometrics; 2012 Jun; 68(2):371-9. PubMed ID: 22004274
[TBL] [Abstract][Full Text] [Related]
37. 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]
38. H-likelihood approach for joint modeling of longitudinal outcomes and time-to-event data.
Ha ID; Noh M; Lee Y
Biom J; 2017 Nov; 59(6):1122-1143. PubMed ID: 29139605
[TBL] [Abstract][Full Text] [Related]
39. Joint modeling of HIV data in multicenter observational studies: A comparison among different approaches.
Brombin C; Di Serio C; Rancoita PM
Stat Methods Med Res; 2016 Dec; 25(6):2472-2487. PubMed ID: 24671658
[TBL] [Abstract][Full Text] [Related]
40. Dynamic predictions using flexible joint models of longitudinal and time-to-event data.
Barrett J; Su L
Stat Med; 2017 Apr; 36(9):1447-1460. PubMed ID: 28110499
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
