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 *

315 related articles for article (PubMed ID: 26012353)

  • 1. 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]  

  • 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. Simultaneous inference and bias analysis for longitudinal data with covariate measurement error and missing responses.
    Yi GY; Liu W; Wu L
    Biometrics; 2011 Mar; 67(1):67-75. PubMed ID: 20528858
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations.
    Li H; Yi GY
    Stat Med; 2013 Feb; 32(5):833-48. PubMed ID: 22833460
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Structural inference in transition measurement error models for longitudinal data.
    Pan W; Lin X; Zeng D
    Biometrics; 2006 Jun; 62(2):402-12. PubMed ID: 16918904
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. On using summary statistics from an external calibration sample to correct for covariate measurement error.
    Guo Y; Little RJ; McConnell DS
    Epidemiology; 2012 Jan; 23(1):165-74. PubMed ID: 22157312
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Weighted estimating equations for longitudinal studies with death and non-monotone missing time-dependent covariates and outcomes.
    Shardell M; Miller RR
    Stat Med; 2008 Mar; 27(7):1008-25. PubMed ID: 17579923
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Jointly Modeling Event Time and Skewed-Longitudinal Data with Missing Response and Mismeasured Covariate for AIDS Studies.
    Huang Y; Yan C; Xing D; Zhang N; Chen H
    J Biopharm Stat; 2015; 25(4):670-94. PubMed ID: 24905593
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error.
    Yi GY; Ma Y; Carroll RJ
    Biometrika; 2012; 99(1):151-165. PubMed ID: 28781377
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.
    Qin G; Zhang J; Zhu Z; Fung W
    Stat Med; 2016 Dec; 35(29):5401-5416. PubMed ID: 27460857
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.
    Xie Y; Zhang B
    Int J Biostat; 2017 Apr; 13(1):. PubMed ID: 28441139
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Explicating the Conditions Under Which Multilevel Multiple Imputation Mitigates Bias Resulting from Random Coefficient-Dependent Missing Longitudinal Data.
    Gottfredson NC; Sterba SK; Jackson KM
    Prev Sci; 2017 Jan; 18(1):12-19. PubMed ID: 27866307
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.