BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

229 related articles for article (PubMed ID: 28956504)

  • 1. Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection.
    Sattar A; Sinha SK
    Stat Methods Med Res; 2019 Feb; 28(2):486-502. PubMed ID: 28956504
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Parametric Survival Model When a Covariate is Subject to Left-Censoring.
    Sattar A; Sinha SK; Morris NJ
    J Biom Biostat; 2012; Suppl 3(2):. PubMed ID: 24319625
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Frailty models for pneumonia to death with a left-censored covariate.
    Sattar A; Sinha SK; Wang XF; Li Y
    Stat Med; 2015 Jun; 34(14):2266-80. PubMed ID: 25728821
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes.
    Alam K; Maity A; Sinha SK; Rizopoulos D; Sattar A
    Lifetime Data Anal; 2021 Jan; 27(1):64-90. PubMed ID: 33236257
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Analysis of non-ignorable missing and left-censored longitudinal data using a weighted random effects tobit model.
    Sattar A; Weissfeld LA; Molenberghs G
    Stat Med; 2011 Nov; 30(27):3167-80. PubMed ID: 21898524
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Joint analysis of left-censored longitudinal biomarker and binary outcome via latent class modeling.
    Li M; Kong L
    Stat Med; 2018 Jun; 37(13):2162-2173. PubMed ID: 29611202
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A mechanistic nonlinear model for censored and mismeasured covariates in longitudinal models, with application in AIDS studies.
    Zhang H; Wong H; Wu L
    Stat Med; 2018 Jan; 37(1):167-178. PubMed ID: 29034494
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A latent class approach for joint modeling of a time-to-event outcome and multiple longitudinal biomarkers subject to limits of detection.
    Li M; Lee CW; Kong L
    Stat Methods Med Res; 2020 Jun; 29(6):1624-1638. PubMed ID: 31469042
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Generalized linear mixed model for binary outcomes when covariates are subject to measurement errors and detection limits.
    Xie X; Xue X; Strickler HD
    Stat Med; 2018 Jan; 37(1):119-136. PubMed ID: 28980332
    [TBL] [Abstract][Full Text] [Related]  

  • 11. [Analysis of longitudinal Gaussian data with missing data on the response variable].
    Jacqmin-Gadda H; Commenges D; Dartigues J
    Rev Epidemiol Sante Publique; 1999 Dec; 47(6):525-34. PubMed ID: 10673586
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.
    Touloumi G; Babiker AG; Pocock SJ; Darbyshire JH
    Stat Med; 2001 Dec; 20(24):3715-28. PubMed ID: 11782028
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with multiple features.
    Huang Y; Chen J
    Stat Med; 2016 Dec; 35(30):5666-5685. PubMed ID: 27592848
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes.
    Li S
    Lifetime Data Anal; 2016 Jan; 22(1):145-60. PubMed ID: 25573223
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A semiparametric imputation approach for regression with censored covariate with application to an AMD progression study.
    Ding Y; Kong S; Kang S; Chen W
    Stat Med; 2018 Oct; 37(23):3293-3308. PubMed ID: 29845616
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A joint model for longitudinal and survival data based on an AR(1) latent process.
    Bacci S; Bartolucci F; Pandolfi S
    Stat Methods Med Res; 2018 May; 27(5):1285-1311. PubMed ID: 27587589
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Bayesian joint modeling for partially linear mixed-effects quantile regression of longitudinal and time-to-event data with limit of detection, covariate measurement errors and skewness.
    Zhang H; Huang Y
    J Biopharm Stat; 2021 May; 31(3):295-316. PubMed ID: 33284096
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Joint modeling of survival time and longitudinal data with subject-specific changepoints in the covariates.
    Tapsoba Jde D; Lee SM; Wang CY
    Stat Med; 2011 Feb; 30(3):232-49. PubMed ID: 21213341
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies.
    Ganjali M; Baghfalaki T
    J Biopharm Stat; 2015; 25(5):1077-99. PubMed ID: 25372017
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach.
    Faucett CL; Thomas DC
    Stat Med; 1996 Aug; 15(15):1663-85. PubMed ID: 8858789
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.