BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

209 related articles for article (PubMed ID: 32180497)

  • 1. Quantile regression for incomplete longitudinal data with selection by death.
    Jacqmin-Gadda H; Rouanet A; Mba RD; Philipps V; Dartigues JF
    Stat Methods Med Res; 2020 Sep; 29(9):2697-2716. PubMed ID: 32180497
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Semi-parametric methods of handling missing data in mortal cohorts under non-ignorable missingness.
    Wen L; Seaman SR
    Biometrics; 2018 Dec; 74(4):1427-1437. PubMed ID: 29772074
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout, with applications to quality of life measurements from clinical trials.
    Lv Y; Qin G; Zhu Z; Tu D
    Stat Med; 2019 Jul; 38(16):2972-2991. PubMed ID: 30997691
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Semiparametric regression models for repeated measures of mortal cohorts with non-monotone missing outcomes and time-dependent covariates.
    Shardell M; Hicks GE; Miller RR; Magaziner J
    Stat Med; 2010 Sep; 29(22):2282-96. PubMed ID: 20564729
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. A hidden Markov model for continuous longitudinal data with missing responses and dropout.
    Pandolfi S; Bartolucci F; Pennoni F
    Biom J; 2023 Jun; 65(5):e2200016. PubMed ID: 37035989
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition.
    Jones M; Mishra GD; Dobson A
    J Clin Epidemiol; 2015 Oct; 68(10):1165-75. PubMed ID: 25920943
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random.
    Preisser JS; Lohman KK; Rathouz PJ
    Stat Med; 2002 Oct; 21(20):3035-54. PubMed ID: 12369080
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Weighted quantile regression for analyzing health care cost data with missing covariates.
    Sherwood B; Wang L; Zhou XH
    Stat Med; 2013 Dec; 32(28):4967-79. PubMed ID: 23836597
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Inference methods for saturated models in longitudinal clinical trials with incomplete binary data.
    Song JX
    Pharm Stat; 2006; 5(4):295-304. PubMed ID: 17128429
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Issues with the expected information matrix of linear mixed models provided by popular statistical packages under missingness at random dropout.
    Thomadakis C; Pantazis N; Touloumi G
    Stat Med; 2023 Jul; 42(16):2873-2885. PubMed ID: 37094843
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.
    Lin H; Fu B; Qin G; Zhu Z
    Biometrics; 2017 Dec; 73(4):1132-1139. PubMed ID: 28369661
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Bayesian quantile regression for longitudinal studies with nonignorable missing data.
    Yuan Y; Yin G
    Biometrics; 2010 Mar; 66(1):105-14. PubMed ID: 19459836
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Methods for handling longitudinal outcome processes truncated by dropout and death.
    Wen L; Terrera GM; Seaman SR
    Biostatistics; 2018 Oct; 19(4):407-425. PubMed ID: 29028922
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Doubly robust generalized estimating equations for longitudinal data.
    Seaman S; Copas A
    Stat Med; 2009 Mar; 28(6):937-55. PubMed ID: 19153970
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Quantile Regression for Competing Risks Data with Missing Cause of Failure.
    Sun Y; Wang HJ; Gilbert PB
    Stat Sin; 2012 Apr; 22(2):703-728. PubMed ID: 23950622
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A doubly robust method to handle missing multilevel outcome data with application to the China Health and Nutrition Survey.
    Butera NM; Zeng D; Green Howard A; Gordon-Larsen P; Cai J
    Stat Med; 2022 Feb; 41(4):769-785. PubMed ID: 34786739
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Quantile regression models for survival data with missing censoring indicators.
    Qiu Z; Ma H; Chen J; Dinse GE
    Stat Methods Med Res; 2021 May; 30(5):1320-1331. PubMed ID: 33826461
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.