These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

180 related articles for article (PubMed ID: 34519089)

  • 1. Joint analysis of longitudinal measurements and spatially clustered competing risks HIV/AIDS data.
    Momenyan S
    Stat Med; 2021 Dec; 40(28):6459-6477. PubMed ID: 34519089
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Competing risks model for clustered data based on the subdistribution hazards with spatial random effects.
    Momenyan S; Ahmadi F; Poorolajal J
    J Appl Stat; 2022; 49(7):1802-1820. PubMed ID: 35707554
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Bayesian joint modeling of longitudinal and spatial survival AIDS data.
    Martins R; Silva GL; Andreozzi V
    Stat Med; 2016 Aug; 35(19):3368-84. PubMed ID: 26990773
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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; 10():69. PubMed ID: 20670425
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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. PubMed ID: 20549344
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Mixed-effects varying-coefficient model with skewed distribution coupled with cause-specific varying-coefficient hazard model with random-effects for longitudinal-competing risks data analysis.
    Lu T; Wang M; Liu G; Dong GH; Qian F
    J Biopharm Stat; 2016; 26(3):519-33. PubMed ID: 26097990
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A two-level copula joint model for joint analysis of longitudinal and competing risks data.
    Lu X; Chekouo T; Shen H; de Leon AR
    Stat Med; 2023 May; 42(12):1909-1930. PubMed ID: 37194500
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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. PubMed ID: 24697192
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.
    Long JD; Mills JA
    BMC Med Res Methodol; 2018 Nov; 18(1):138. PubMed ID: 30445915
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Simultaneous inference for semiparametric mixed-effects joint models with skew distribution and covariate measurement error for longitudinal competing risks data analysis.
    Lu T
    J Biopharm Stat; 2017; 27(6):1009-1027. PubMed ID: 28272995
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Joint analysis of longitudinal and survival AIDS data with a spatial fraction of long-term survivors: A Bayesian approach.
    Martins R; Silva GL; Andreozzi V
    Biom J; 2017 Nov; 59(6):1166-1183. PubMed ID: 28464317
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A tractable Bayesian joint model for longitudinal and survival data.
    Alvares D; Rubio FJ
    Stat Med; 2021 Aug; 40(19):4213-4229. PubMed ID: 34114254
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study.
    Ngwa JS; Cabral HJ; Cheng DM; Gagnon DR; LaValley MP; Cupples LA
    BMC Med Res Methodol; 2021 Feb; 21(1):29. PubMed ID: 33568059
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study.
    Baghfalaki T; Kalantari S; Ganjali M; Hadaegh F; Pahlavanzadeh B
    J Biopharm Stat; 2020 Jul; 30(4):689-703. PubMed ID: 32129702
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Modeling the underlying biological processes in Alzheimer's disease using a multivariate competing risk joint model.
    van Oudenhoven FM; Swinkels SHN; Hartmann T; Rizopoulos D
    Stat Med; 2022 Jul; 41(17):3421-3433. PubMed ID: 35582814
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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):762-771. PubMed ID: 18162112
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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. PubMed ID: 23907983
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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; 28(11):1601-19. PubMed ID: 19308919
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.