BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

151 related articles for article (PubMed ID: 26748812)

  • 1. A two-stage approach for dynamic prediction of time-to-event distributions.
    Huang X; Yan F; Ning J; Feng Z; Choi S; Cortes J
    Stat Med; 2016 Jun; 35(13):2167-82. PubMed ID: 26748812
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker.
    Suresh K; Taylor JMG; Tsodikov A
    Biostatistics; 2021 Jul; 22(3):504-521. PubMed ID: 31820798
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Estimation of the distribution of longitudinal biomarker trajectories prior to disease progression.
    Huang X; Liu L; Ning J; Li L; Shen Y
    Stat Med; 2019 May; 38(11):2030-2046. PubMed ID: 30614014
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Mean residual life regression with functional principal component analysis on longitudinal data for dynamic prediction.
    Lin X; Lu T; Yan F; Li R; Huang X
    Biometrics; 2018 Dec; 74(4):1482-1491. PubMed ID: 29601636
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers.
    Wu C; Li L; Li R
    Stat Methods Med Res; 2020 Nov; 29(11):3179-3191. PubMed ID: 32419611
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.
    Pickett KL; Suresh K; Campbell KR; Davis S; Juarez-Colunga E
    BMC Med Res Methodol; 2021 Oct; 21(1):216. PubMed ID: 34657597
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Dynamic pseudo-observations: a robust approach to dynamic prediction in competing risks.
    Nicolaie MA; van Houwelingen JC; de Witte TM; Putter H
    Biometrics; 2013 Dec; 69(4):1043-52. PubMed ID: 23865523
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A comparison of two approaches to dynamic prediction: Joint modeling and landmark modeling.
    Li W; Li L; Astor BC
    Stat Med; 2023 Jun; 42(13):2101-2115. PubMed ID: 36938960
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach.
    Devaux A; Genuer R; Peres K; Proust-Lima C
    BMC Med Res Methodol; 2022 Jul; 22(1):188. PubMed ID: 35818025
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Landmark Linear Transformation Model for Dynamic Prediction with Application to a Longitudinal Cohort Study of Chronic Disease.
    Zhu Y; Li L; Huang X
    J R Stat Soc Ser C Appl Stat; 2019 Apr; 68(3):771-791. PubMed ID: 31467454
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Dynamic predictions using flexible joint models of longitudinal and time-to-event data.
    Barrett J; Su L
    Stat Med; 2017 Apr; 36(9):1447-1460. PubMed ID: 28110499
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Quantile residual lifetime regression with functional principal component analysis of longitudinal data for dynamic prediction.
    Lin X; Li R; Yan F; Lu T; Huang X
    Stat Methods Med Res; 2019 Apr; 28(4):1216-1229. PubMed ID: 29402190
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.
    Zhu Y; Huang X; Li L
    Biom J; 2020 Oct; 62(6):1371-1393. PubMed ID: 32196728
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease.
    Li L; Luo S; Hu B; Greene T
    Stat Biosci; 2017 Dec; 9(2):357-378. PubMed ID: 29250207
    [TBL] [Abstract][Full Text] [Related]  

  • 16. On longitudinal prediction with time-to-event outcome: Comparison of modeling options.
    Maziarz M; Heagerty P; Cai T; Zheng Y
    Biometrics; 2017 Mar; 73(1):83-93. PubMed ID: 27438160
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Comparison of Joint and Landmark Modeling for Predicting Cancer Progression in Men With Castration-Resistant Prostate Cancer: A Secondary Post Hoc Analysis of the PREVAIL Randomized Clinical Trial.
    Finelli A; Beer TM; Chowdhury S; Evans CP; Fizazi K; Higano CS; Kim J; Martin L; Saad F; Saarela O
    JAMA Netw Open; 2021 Jun; 4(6):e2112426. PubMed ID: 34129025
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Landmarking 2.0: Bridging the gap between joint models and landmarking.
    Putter H; van Houwelingen HC
    Stat Med; 2022 May; 41(11):1901-1917. PubMed ID: 35098578
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparing statistical methods in assessing the prognostic effect of biomarker variability on time-to-event clinical outcomes.
    Gao F; Luo J; Liu J; Wan F; Wang G; Gordon M; Xiong C
    BMC Med Res Methodol; 2022 Jul; 22(1):201. PubMed ID: 35869438
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.