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Results: 1 to 20 of 123

Similar articles for PubMed (Select 19197956)

1.

Robust joint modeling of longitudinal measurements and competing risks failure time data.

Li N, Elashoff RM, Li G.

Biom J. 2009 Feb;51(1):19-30. doi: 10.1002/bimj.200810491.

2.

A joint model for longitudinal measurements and survival data in the presence of multiple failure types.

Elashoff RM, Li G, Li N.

Biometrics. 2008 Sep;64(3):762-71. Epub 2007 Dec 20.

3.

Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data.

Deslandes E, Chevret S.

BMC Med Res Methodol. 2010 Jul 29;10:69. doi: 10.1186/1471-2288-10-69.

5.

Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the NINDS rt-PA stroke trial.

Li N, Elashoff RM, Li G, Saver J.

Stat Med. 2010 Feb 28;29(5):546-57. doi: 10.1002/sim.3798.

6.
7.

Joint analysis of bivariate longitudinal ordinal outcomes and competing risks survival times with nonparametric distributions for random effects.

Li N, Elashoff RM, Li G, Tseng CH.

Stat Med. 2012 Jul 20;31(16):1707-21. doi: 10.1002/sim.4507. Epub 2012 Feb 17.

PMID:
22344869
8.

Joint modeling and analysis of longitudinal data with informative observation times.

Liang Y, Lu W, Ying Z.

Biometrics. 2009 Jun;65(2):377-84. doi: 10.1111/j.1541-0420.2008.01104.x.

PMID:
18759841
9.

A semiparametric joint model for longitudinal and survival data with application to hemodialysis study.

Li L, Hu B, Greene T.

Biometrics. 2009 Sep;65(3):737-45. doi: 10.1111/j.1541-0420.2008.01168.x. Epub 2009 Jan 23.

PMID:
19173700
10.

Joint modeling of survival and longitudinal data: likelihood approach revisited.

Hsieh F, Tseng YK, Wang JL.

Biometrics. 2006 Dec;62(4):1037-43.

PMID:
17156277
11.

A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects.

Huang X, Li G, Elashoff RM, Pan J.

Lifetime Data Anal. 2011 Jan;17(1):80-100. doi: 10.1007/s10985-010-9169-6. Epub 2010 Jun 12.

12.

Efficient estimation for patient-specific rates of disease progression using nonnormal linear mixed models.

Zhang P, Song PX, Qu A, Greene T.

Biometrics. 2008 Mar;64(1):29-38. Epub 2007 May 14.

PMID:
17501938
13.

A Bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data.

Hu W, Li G, Li N.

Stat Med. 2009 May 15;28(11):1601-19. doi: 10.1002/sim.3562.

14.
15.

Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions: a Bayesian approach.

Baghfalaki T, Ganjali M, Berridge D.

Biom J. 2013 Nov;55(6):844-65. doi: 10.1002/bimj.201200272. Epub 2013 Aug 1.

PMID:
23907983
16.

Joint models for a primary endpoint and multiple longitudinal covariate processes.

Li E, Wang N, Wang NY.

Biometrics. 2007 Dec;63(4):1068-78. Epub 2007 May 14.

18.

Bayesian quantile regression for longitudinal studies with nonignorable missing data.

Yuan Y, Yin G.

Biometrics. 2010 Mar;66(1):105-14. doi: 10.1111/j.1541-0420.2009.01269.x. Epub 2009 May 12.

PMID:
19459836
19.

Multilevel latent class models with dirichlet mixing distribution.

Di CZ, Bandeen-Roche K.

Biometrics. 2011 Mar;67(1):86-96. doi: 10.1111/j.1541-0420.2010.01448.x.

20.

Estimating population size for a continuous time frailty model with covariates in a capture-recapture study.

Xu Y, Liu L, You N, Pan H, Yip P.

Biometrics. 2007 Sep;63(3):917-21. Epub 2007 Apr 2.

PMID:
17403102
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