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 *

162 related articles for article (PubMed ID: 28321912)

  • 1. Boosting joint models for longitudinal and time-to-event data.
    Waldmann E; Taylor-Robinson D; Klein N; Kneib T; Pressler T; Schmid M; Mayr A
    Biom J; 2017 Nov; 59(6):1104-1121. PubMed ID: 28321912
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques.
    Griesbach C; Groll A; Bergherr E
    Comput Math Methods Med; 2021; 2021():4384035. PubMed ID: 34819988
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data.
    Tang AM; Zhao X; Tang NS
    Biom J; 2017 Jan; 59(1):57-78. PubMed ID: 27667731
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Jointly modeling the relationship between longitudinal and survival data subject to left truncation with applications to cystic fibrosis.
    Piccorelli AV; Schluchter MD
    Stat Med; 2012 Dec; 31(29):3931-45. PubMed ID: 22786556
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset.
    Andrinopoulou ER; Clancy JP; Szczesniak RD
    BMC Pulm Med; 2020 May; 20(1):142. PubMed ID: 32429862
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Variable selection for joint models of multivariate skew-normal longitudinal and survival data.
    Tang J; Tang AM; Tang N
    Stat Methods Med Res; 2023 Sep; 32(9):1694-1710. PubMed ID: 37408456
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. More than one way: exploring the capabilities of different estimation approaches to joint models for longitudinal and time-to-event outcomes.
    Rappl A; Mayr A; Waldmann E
    Int J Biostat; 2021 Apr; 18(1):127-149. PubMed ID: 33818032
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Boosting distributional copula regression.
    Hans N; Klein N; Faschingbauer F; Schneider M; Mayr A
    Biometrics; 2023 Sep; 79(3):2298-2310. PubMed ID: 36165288
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Shared parameter models for joint analysis of longitudinal and survival data with left truncation due to delayed entry - Applications to cystic fibrosis.
    Schluchter MD; Piccorelli AV
    Stat Methods Med Res; 2019 May; 28(5):1489-1507. PubMed ID: 29618290
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A joint model for survival and longitudinal data measured with error.
    Wulfsohn MS; Tsiatis AA
    Biometrics; 1997 Mar; 53(1):330-9. PubMed ID: 9147598
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Joint partially linear model for longitudinal data with informative drop-outs.
    Kim S; Zeng D; Taylor JM
    Biometrics; 2017 Mar; 73(1):72-82. PubMed ID: 27479944
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Flexible Bayesian additive joint models with an application to type 1 diabetes research.
    Köhler M; Umlauf N; Beyerlein A; Winkler C; Ziegler AG; Greven S
    Biom J; 2017 Nov; 59(6):1144-1165. PubMed ID: 28796339
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A Bayesian model for sparse functional data.
    Thompson WK; Rosen O
    Biometrics; 2008 Mar; 64(1):54-63. PubMed ID: 17573864
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The evolution of boosting algorithms. From machine learning to statistical modelling.
    Mayr A; Binder H; Gefeller O; Schmid M
    Methods Inf Med; 2014; 53(6):419-27. PubMed ID: 25112367
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Extending statistical boosting. An overview of recent methodological developments.
    Mayr A; Binder H; Gefeller O; Schmid M
    Methods Inf Med; 2014; 53(6):428-35. PubMed ID: 25112429
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A Bayesian joint model of recurrent events and a terminal event.
    Li Z; Chinchilli VM; Wang M
    Biom J; 2019 Jan; 61(1):187-202. PubMed ID: 30479030
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data.
    Ding J; Wang JL
    Biometrics; 2008 Jun; 64(2):546-56. PubMed ID: 17888040
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Bayesian model for joint analysis of multivariate repeated measures and time to event data in crossover trials.
    Liu F; Li Q
    Stat Methods Med Res; 2016 Oct; 25(5):2180-2192. PubMed ID: 24448442
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.