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 *

144 related articles for article (PubMed ID: 28910499)

  • 1. Joint model selection of marginal mean regression and correlation structure for longitudinal data with missing outcome and covariates.
    Shen CW; Chen YH
    Biom J; 2018 Jan; 60(1):20-33. PubMed ID: 28910499
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Model selection for generalized estimating equations accommodating dropout missingness.
    Shen CW; Chen YH
    Biometrics; 2012 Dec; 68(4):1046-54. PubMed ID: 22463099
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness.
    Chen C; Shen B; Zhang L; Xue Y; Wang M
    Biometrics; 2019 Sep; 75(3):950-965. PubMed ID: 31004449
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Model selection for marginal regression analysis of longitudinal data with missing observations and covariate measurement error.
    Shen CW; Chen YH
    Biostatistics; 2015 Oct; 16(4):740-53. PubMed ID: 26012353
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Model selection of generalized estimating equations with multiply imputed longitudinal data.
    Shen CW; Chen YH
    Biom J; 2013 Nov; 55(6):899-911. PubMed ID: 23970494
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data.
    Shults J; Sun W; Tu X; Kim H; Amsterdam J; Hilbe JM; Ten-Have T
    Stat Med; 2009 Aug; 28(18):2338-55. PubMed ID: 19472307
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Regression analysis with missing covariate data using estimating equations.
    Zhao LP; Lipsitz S; Lew D
    Biometrics; 1996 Dec; 52(4):1165-82. PubMed ID: 8962448
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates.
    Yi GY
    Biostatistics; 2008 Jul; 9(3):501-12. PubMed ID: 18199691
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates.
    Chen Z; Yi GY; Wu C
    Biom J; 2014 Jan; 56(1):69-85. PubMed ID: 24123126
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A characterization of missingness at random in a generalized shared-parameter joint modeling framework for longitudinal and time-to-event data, and sensitivity analysis.
    Njagi EN; Molenberghs G; Kenward MG; Verbeke G; Rizopoulos D
    Biom J; 2014 Nov; 56(6):1001-15. PubMed ID: 24947904
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Doubly robust estimates for binary longitudinal data analysis with missing response and missing covariates.
    Chen B; Zhou XH
    Biometrics; 2011 Sep; 67(3):830-42. PubMed ID: 21281272
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.
    Shen CW; Chen YH
    Biometrics; 2018 Sep; 74(3):934-943. PubMed ID: 29534287
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Variable Selection and Inference Procedures for Marginal Analysis of Longitudinal Data with Missing Observations and Covariate Measurement Error.
    Yi GY; Tan X; Li R
    Can J Stat; 2015 Dec; 43(4):498-518. PubMed ID: 26877582
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Joint mean-covariance random effect model for longitudinal data.
    Bai Y; Qian M; Tian M
    Biom J; 2020 Jan; 62(1):7-23. PubMed ID: 31544252
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model.
    Huque MH; Moreno-Betancur M; Quartagno M; Simpson JA; Carlin JB; Lee KJ
    Biom J; 2020 Mar; 62(2):444-466. PubMed ID: 31919921
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Expected estimating equations for missing data, measurement error, and misclassification, with application to longitudinal nonignorable missing data.
    Wang CY; Huang Y; Chao EC; Jeffcoat MK
    Biometrics; 2008 Mar; 64(1):85-95. PubMed ID: 17608787
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improved methods for the marginal analysis of longitudinal data in the presence of time-dependent covariates.
    Chen IC; Westgate PM
    Stat Med; 2017 Jul; 36(16):2533-2546. PubMed ID: 28436045
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Improving estimation efficiency for regression with MNAR covariates.
    Che M; Han P; Lawless JF
    Biometrics; 2020 Mar; 76(1):270-280. PubMed ID: 31393001
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness.
    Xu C; Li Z; Xue Y; Zhang L; Wang M
    Commun Stat Simul Comput; 2019; 48(9):2812-2829. PubMed ID: 32346220
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Merging multiple longitudinal studies with study-specific missing covariates: A joint estimating function approach.
    Wang F; Song PX; Wang L
    Biometrics; 2015 Dec; 71(4):929-40. PubMed ID: 26193911
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.