Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 117

1.

A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome.

Gao F, Miller JP, Xiong C, Beiser JA, Gordon M; The Ocular Hypertension Treatment Study (OHTS) Group.

Stat Methods Appt. 2011 Mar 1;20(1):83-100.

3.

Parametric and nonparametric population methods: their comparative performance in analysing a clinical dataset and two Monte Carlo simulation studies.

Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, Jelliffe R.

Clin Pharmacokinet. 2006;45(4):365-83.

PMID:
16584284
4.

Evaluating heterogeneity in indoor and outdoor air pollution using land-use regression and constrained factor analysis.

Levy JI, Clougherty JE, Baxter LK, Houseman EA, Paciorek CJ; HEI Health Review Committee.

Res Rep Health Eff Inst. 2010 Dec;(152):5-80; discussion 81-91.

PMID:
21409949
5.
6.

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.

7.
8.

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.

9.
10.

An example of complex modelling in dentistry using Markov chain Monte Carlo (MCMC) simulation.

Helfenstein U, Menghini G, Steiner M, Murati F.

Community Dent Health. 2002 Sep;19(3):152-60.

PMID:
12269461
11.

Bayesian joint modeling of longitudinal measurements and time-to-event data using robust distributions.

Baghfalaki T, Ganjali M, Hashemi R.

J Biopharm Stat. 2014;24(4):834-55. doi: 10.1080/10543406.2014.903657.

PMID:
24697192
12.

Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes.

Li S.

Lifetime Data Anal. 2016 Jan;22(1):145-60. doi: 10.1007/s10985-014-9316-6. Epub 2015 Jan 9.

PMID:
25573223
14.

Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials.

Zhang D, Chen MH, Ibrahim JG, Boye ME, Shen W.

J Comput Graph Stat. 2017;26(1):121-133. doi: 10.1080/10618600.2015.1117472. Epub 2017 Feb 16.

15.

The impact of covariate measurement error on risk prediction.

Khudyakov P, Gorfine M, Zucker D, Spiegelman D.

Stat Med. 2015 Jul 10;34(15):2353-67. doi: 10.1002/sim.6498. Epub 2015 Apr 10.

16.

Binary regression with misclassified response and covariate subject to measurement error: a bayesian approach.

McGlothlin A, Stamey JD, Seaman JW Jr.

Biom J. 2008 Feb;50(1):123-34. doi: 10.1002/bimj.200710402.

PMID:
18283683
17.

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
18.
19.

Jointly Modeling Event Time and Skewed-Longitudinal Data with Missing Response and Mismeasured Covariate for AIDS Studies.

Huang Y, Yan C, Xing D, Zhang N, Chen H.

J Biopharm Stat. 2015;25(4):670-94. doi: 10.1080/10543406.2014.920866.

PMID:
24905593
20.

How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS.

Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR.

Stat Med. 2005 Aug 15;24(15):2401-28.

PMID:
16015676

Supplemental Content

Support Center