BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

119 related articles for article (PubMed ID: 36610077)

  • 1. Characterizing quantile-varying covariate effects under the accelerated failure time model.
    Reeder HT; Lee KH; Haneuse S
    Biostatistics; 2024 Apr; 25(2):449-467. PubMed ID: 36610077
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.
    Hari A; Jinto EG; Dennis D; Krishna KMNJ; George PS; Roshni S; Mathew A
    Stat Appl Genet Mol Biol; 2024 Jan; 23(1):. PubMed ID: 38736398
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Shrinkage Estimation of Varying Covariate Effects Based On Quantile Regression.
    Peng L; Xu J; Kutner N
    Stat Comput; 2014 Sep; 24(5):853-869. PubMed ID: 25332515
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Bayesian nonparametric quantile process regression and estimation of marginal quantile effects.
    Xu SG; Reich BJ
    Biometrics; 2023 Mar; 79(1):151-164. PubMed ID: 34611897
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selection.
    Zheng Q; Peng L
    Commun Stat Theory Methods; 2017; 46(3):1031-1049. PubMed ID: 28008212
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Quantile regression for survival data with covariates subject to detection limits.
    Yu T; Xiang L; Wang HJ
    Biometrics; 2021 Jun; 77(2):610-621. PubMed ID: 32453884
    [TBL] [Abstract][Full Text] [Related]  

  • 7. On semiparametric accelerated failure time models with time-varying covariates: A maximum penalised likelihood estimation.
    Ma D; Ma J; Graham PL
    Stat Med; 2023 Dec; 42(30):5577-5595. PubMed ID: 37845791
    [TBL] [Abstract][Full Text] [Related]  

  • 8. ANALYSIS OF DEPENDENTLY CENSORED DATA BASED ON QUANTILE REGRESSION.
    Ji S; Peng L; Li R; Lynn MJ
    Stat Sin; 2014; 24(3):1411-1432. PubMed ID: 25382953
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Model-based standardization using multiple imputation.
    Remiro-Azócar A; Heath A; Baio G
    BMC Med Res Methodol; 2024 Feb; 24(1):32. PubMed ID: 38341552
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Accelerated failure time models for semi-competing risks data in the presence of complex censoring.
    Lee KH; Rondeau V; Haneuse S
    Biometrics; 2017 Dec; 73(4):1401-1412. PubMed ID: 28395116
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Generalized scale-change models for recurrent event processes under informative censoring.
    Xu G; Chiou SH; Yan J; Marr K; Huang CY
    Stat Sin; 2020; 30():1773-1795. PubMed ID: 34385810
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Spline-based accelerated failure time model.
    Pang M; Platt RW; Schuster T; Abrahamowicz M
    Stat Med; 2021 Jan; 40(2):481-497. PubMed ID: 33105513
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Generalizing Quantile Regression for Counting Processes with Applications to Recurrent Events.
    Sun X; Peng L; Huang Y; Lai HJ
    J Am Stat Assoc; 2016; 111(513):145-156. PubMed ID: 27212738
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.
    Sparapani RA; Logan BR; Maiers MJ; Laud PW; McCulloch RE
    Biometrics; 2023 Dec; 79(4):3023-3037. PubMed ID: 36932826
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors.
    Crowther MJ; Royston P; Clements M
    Biostatistics; 2023 Jul; 24(3):811-831. PubMed ID: 35639824
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Time-to-event data with time-varying biomarkers measured only at study entry, with applications to Alzheimer's disease.
    Lee C; Betensky RA;
    Stat Med; 2018 Mar; 37(6):914-932. PubMed ID: 29266591
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A censored quantile regression approach for the analysis of time to event data.
    Xue X; Xie X; Strickler HD
    Stat Methods Med Res; 2018 Mar; 27(3):955-965. PubMed ID: 27166408
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Censored quantile regression with recursive partitioning-based weights.
    Wey A; Wang L; Rudser K
    Biostatistics; 2014 Jan; 15(1):170-81. PubMed ID: 23975800
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Quantile regression-based Bayesian joint modeling analysis of longitudinal-survival data, with application to an AIDS cohort study.
    Zhang H; Huang Y
    Lifetime Data Anal; 2020 Apr; 26(2):339-368. PubMed ID: 31140028
    [TBL] [Abstract][Full Text] [Related]  

  • 20. General regression model for the subdistribution of a competing risk under left-truncation and right-censoring.
    Bellach A; Kosorok MR; Gilbert PB; Fine JP
    Biometrika; 2020 Dec; 107(4):949-964. PubMed ID: 33462536
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.