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 *

159 related articles for article (PubMed ID: 26193911)

  • 1. 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]  

  • 2. Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach.
    Qu Y; Lipkovich I
    Stat Med; 2009 Apr; 28(9):1402-14. PubMed ID: 19222021
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Generalized estimating equation model for binary outcomes with missing covariates.
    Xie F; Paik MC
    Biometrics; 1997 Dec; 53(4):1458-66. PubMed ID: 9423260
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Shrinkage empirical likelihood estimator in longitudinal analysis with time-dependent covariates--application to modeling the health of Filipino children.
    Leung DH; Small DS; Qin J; Zhu M
    Biometrics; 2013 Sep; 69(3):624-32. PubMed ID: 23845158
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Multiple imputation methods for the missing covariates in generalized estimating equation.
    Xie F; Paik MC
    Biometrics; 1997 Dec; 53(4):1538-46. PubMed ID: 9423268
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Posterior predictive checking of multiple imputation models.
    Nguyen CD; Lee KJ; Carlin JB
    Biom J; 2015 Jul; 57(4):676-94. PubMed ID: 25939490
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Estimation in closed capture-recapture models when covariates are missing at random.
    Lee SM; Hwang WH; de Dieu Tapsoba J
    Biometrics; 2016 Dec; 72(4):1294-1304. PubMed ID: 26909877
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data.
    Tang AM; Zhao X; Tang NS
    Biom J; 2017 Jan; 59(1):57-78. PubMed ID: 27667731
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Rank-based estimating equations with general weight for accelerated failure time models: an induced smoothing approach.
    Chiou S; Kang S; Yan J
    Stat Med; 2015 Apr; 34(9):1495-510. PubMed ID: 25640630
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A simple imputation method for longitudinal studies with non-ignorable non-responses.
    Wang M; Fitzmaurice GM
    Biom J; 2006 Apr; 48(2):302-18. PubMed ID: 16708780
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Quantile regression methods for reference growth charts.
    Wei Y; Pere A; Koenker R; He X
    Stat Med; 2006 Apr; 25(8):1369-82. PubMed ID: 16143984
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates.
    Parzen M; Lipsitz SR; Fitzmaurice GM; Ibrahim JG; Troxel A
    Stat Med; 2006 Aug; 25(16):2784-96. PubMed ID: 16345018
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A penalized spline approach to functional mixed effects model analysis.
    Chen H; Wang Y
    Biometrics; 2011 Sep; 67(3):861-70. PubMed ID: 21155747
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A comparison of two methods of estimating propensity scores after multiple imputation.
    Mitra R; Reiter JP
    Stat Methods Med Res; 2016 Feb; 25(1):188-204. PubMed ID: 22687877
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 8.