BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

281 related articles for article (PubMed ID: 22517270)

  • 1. Joint latent class models for longitudinal and time-to-event data: a review.
    Proust-Lima C; Séne M; Taylor JM; Jacqmin-Gadda H
    Stat Methods Med Res; 2014 Feb; 23(1):74-90. PubMed ID: 22517270
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach.
    Proust-Lima C; Taylor JM
    Biostatistics; 2009 Jul; 10(3):535-49. PubMed ID: 19369642
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Joint models for dynamic prediction in localised prostate cancer: a literature review.
    Parr H; Hall E; Porta N
    BMC Med Res Methodol; 2022 Sep; 22(1):245. PubMed ID: 36123621
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach.
    Proust-Lima C; Dartigues JF; Jacqmin-Gadda H
    Stat Med; 2016 Feb; 35(3):382-98. PubMed ID: 26376900
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Score test for conditional independence between longitudinal outcome and time to event given the classes in the joint latent class model.
    Jacqmin-Gadda H; Proust-Lima C; Taylor JM; Commenges D
    Biometrics; 2010 Mar; 66(1):11-9. PubMed ID: 19432771
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer.
    Ferrer L; Rondeau V; Dignam J; Pickles T; Jacqmin-Gadda H; Proust-Lima C
    Stat Med; 2016 Sep; 35(22):3933-48. PubMed ID: 27090611
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcomes.
    Pauler DK; Finkelstein DM
    Stat Med; 2002 Dec; 21(24):3897-911. PubMed ID: 12483774
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Joint latent class trees: A tree-based approach to modeling time-to-event and longitudinal data.
    Zhang N; Simonoff JS
    Stat Methods Med Res; 2022 Apr; 31(4):719-752. PubMed ID: 35179059
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Real-time individual predictions of prostate cancer recurrence using joint models.
    Taylor JM; Park Y; Ankerst DP; Proust-Lima C; Williams S; Kestin L; Bae K; Pickles T; Sandler H
    Biometrics; 2013 Mar; 69(1):206-13. PubMed ID: 23379600
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A copula-based approach for dynamic prediction of survival with a binary time-dependent covariate.
    Suresh K; Taylor JMG; Tsodikov A
    Stat Med; 2021 Oct; 40(23):4931-4946. PubMed ID: 34124771
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Relationship between prostate-specific antigen, alkaline phosphatase levels, and time-to-tumor shrinkage: understanding the progression of prostate cancer in a longitudinal study.
    Liaqat M; Khan RA; Fischer F; Kamal S
    BMC Urol; 2024 Jul; 24(1):137. PubMed ID: 38956570
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Choice of prognostic estimators in joint models by estimating differences of expected conditional Kullback-Leibler risks.
    Commenges D; Liquet B; Proust-Lima C
    Biometrics; 2012 Jun; 68(2):380-7. PubMed ID: 22578147
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Joint longitudinal and time-to-event cure models for the assessment of being cured.
    Barbieri A; Legrand C
    Stat Methods Med Res; 2020 Apr; 29(4):1256-1270. PubMed ID: 31213153
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A latent class mixed model for analysing biomarker trajectories with irregularly scheduled observations.
    Lin H; McCulloch CE; Turnbull BW; Slate EH; Clark LC
    Stat Med; 2000 May; 19(10):1303-18. PubMed ID: 10814979
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome.
    Sun J; Herazo-Maya JD; Molyneaux PL; Maher TM; Kaminski N; Zhao H
    Biometrics; 2019 Mar; 75(1):69-77. PubMed ID: 30178494
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: Development and validation.
    Sène M; Taylor JM; Dignam JJ; Jacqmin-Gadda H; Proust-Lima C
    Stat Methods Med Res; 2016 Dec; 25(6):2972-2991. PubMed ID: 24847900
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A dynamic trajectory class model for intensive longitudinal categorical outcome.
    Lin H; Han L; Peduzzi PN; Murphy TE; Gill TM; Allore HG
    Stat Med; 2014 Jul; 33(15):2645-64. PubMed ID: 24519416
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks.
    Blanche P; Proust-Lima C; Loubère L; Berr C; Dartigues JF; Jacqmin-Gadda H
    Biometrics; 2015 Mar; 71(1):102-113. PubMed ID: 25311240
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Joint modelling of longitudinal measurements and survival times via a multivariate copula approach.
    Zhang Z; Charalambous C; Foster P
    J Appl Stat; 2023; 50(13):2739-2759. PubMed ID: 37720246
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome.
    Proust-Lima C; Letenneur L; Jacqmin-Gadda H
    Stat Med; 2007 May; 26(10):2229-45. PubMed ID: 16900568
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.