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 *

126 related articles for article (PubMed ID: 25652674)

  • 41. A joint model for repeated events of different types and multiple longitudinal outcomes with application to a follow-up study of patients after kidney transplant.
    Musoro JZ; Geskus RB; Zwinderman AH
    Biom J; 2015 Mar; 57(2):185-200. PubMed ID: 25316383
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data.
    Rizopoulos D
    Biometrics; 2011 Sep; 67(3):819-29. PubMed ID: 21306352
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Gaussian variational approximate inference for joint models of longitudinal biomarkers and a survival outcome.
    Tu J; Sun J
    Stat Med; 2023 Feb; 42(3):316-330. PubMed ID: 36443903
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Exploration of model misspecification in latent class methods for longitudinal data: Correlation structure matters.
    Neely ML; Pieper CF; Gu B; Dmitrieva NO; Pendergast JF
    Stat Med; 2023 Jun; 42(14):2420-2438. PubMed ID: 37019876
    [TBL] [Abstract][Full Text] [Related]  

  • 45. The relationship of longitudinal change in reproductive hormones and vasomotor symptoms during the menopausal transition.
    Randolph JF; Sowers M; Bondarenko I; Gold EB; Greendale GA; Bromberger JT; Brockwell SE; Matthews KA
    J Clin Endocrinol Metab; 2005 Nov; 90(11):6106-12. PubMed ID: 16144949
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Estimating treatment efficacy over time: a logistic regression model for binary longitudinal outcomes.
    Choi L; Dominici F; Zeger SL; Ouyang P
    Stat Med; 2005 Sep; 24(18):2789-805. PubMed ID: 16134133
    [TBL] [Abstract][Full Text] [Related]  

  • 47. The impact of random-effect misspecification on percentile estimation for longitudinal growth data.
    Cheon K; Albert PS; Zhang Z
    Stat Med; 2012 Dec; 31(28):3708-18. PubMed ID: 22786788
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Median regression models for longitudinal data with dropouts.
    Yi GY; He W
    Biometrics; 2009 Jun; 65(2):618-25. PubMed ID: 18759840
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Improving the Rank Precision of Population Health Measures for Small Areas with Longitudinal and Joint Outcome Models.
    Athens JK; Remington PL; Gangnon RE
    PLoS One; 2015; 10(6):e0130027. PubMed ID: 26098858
    [TBL] [Abstract][Full Text] [Related]  

  • 50. 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]  

  • 51. Parametric latent class joint model for a longitudinal biomarker and recurrent events.
    Han J; Slate EH; Peña EA
    Stat Med; 2007 Dec; 26(29):5285-302. PubMed ID: 17542002
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Longitudinal and repeated cross-sectional cluster-randomization designs using mixed effects regression for binary outcomes: bias and coverage of frequentist and Bayesian methods.
    Localio AR; Berlin JA; Have TR
    Stat Med; 2006 Aug; 25(16):2720-36. PubMed ID: 16345043
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Analytic approaches for assessing long-term treatment effects. Examples of empirical applications and findings.
    Hser YI; Shen H; Chou CP; Messer SC; Anglin MD
    Eval Rev; 2001 Apr; 25(2):233-62. PubMed ID: 11317718
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response.
    Huang X
    Biometrics; 2009 Jun; 65(2):361-8. PubMed ID: 18759837
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Mixed effects logistic regression models for longitudinal binary response data with informative drop-out.
    Ten Have TR; Kunselman AR; Pulkstenis EP; Landis JR
    Biometrics; 1998 Mar; 54(1):367-83. PubMed ID: 9544529
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Bayesian joint modeling of longitudinal measurements and time-to-event data using robust distributions.
    Baghfalaki T; Ganjali M; Hashemi R
    J Biopharm Stat; 2014; 24(4):834-55. PubMed ID: 24697192
    [TBL] [Abstract][Full Text] [Related]  

  • 57. A cautionary note on modeling growth trends in longitudinal data.
    Kuljanin G; Braun MT; Deshon RP
    Psychol Methods; 2011 Sep; 16(3):249-64. PubMed ID: 21517180
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Joint principal trend analysis for longitudinal high-dimensional data.
    Zhang Y; Ouyang Z
    Biometrics; 2018 Jun; 74(2):430-438. PubMed ID: 28759699
    [TBL] [Abstract][Full Text] [Related]  

  • 59. A conditional estimating equation approach for recurrent event data with additional longitudinal information.
    Shen Y; Huang H; Guan Y
    Stat Med; 2016 Oct; 35(24):4306-4319. PubMed ID: 27241902
    [TBL] [Abstract][Full Text] [Related]  

  • 60. 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]  

    [Previous]   [Next]    [New Search]
    of 7.