BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

127 related articles for article (PubMed ID: 37094843)

  • 1. 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]  

  • 2. Misspecifying the covariance structure in a linear mixed model under MAR drop-out.
    Thomadakis C; Meligkotsidou L; Pantazis N; Touloumi G
    Stat Med; 2020 Oct; 39(23):3027-3041. PubMed ID: 32452081
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Longitudinal and time-to-drop-out joint models can lead to seriously biased estimates when the drop-out mechanism is at random.
    Thomadakis C; Meligkotsidou L; Pantazis N; Touloumi G
    Biometrics; 2019 Mar; 75(1):58-68. PubMed ID: 30357814
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A hybrid return to baseline imputation method to incorporate MAR and MNAR dropout missingness.
    Jin M
    Contemp Clin Trials; 2022 Sep; 120():106859. PubMed ID: 35872135
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Biased estimation with shared parameter models in the presence of competing dropout mechanisms.
    Vonesh EF; Greene T
    Biometrics; 2022 Mar; 78(1):399-406. PubMed ID: 33592109
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Bayesian pattern-mixture models for dropout and intermittently missing data in longitudinal data analysis.
    Blozis SA
    Behav Res Methods; 2024 Mar; 56(3):1953-1967. PubMed ID: 37221346
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Identifiability assumptions for missing covariate data in failure time regression models.
    Rathouz PJ
    Biostatistics; 2007 Apr; 8(2):345-56. PubMed ID: 16840561
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparison of Different LGM-Based Methods with MAR and MNAR Dropout Data.
    Li M; Chen N; Cui Y; Liu H
    Front Psychol; 2017; 8():722. PubMed ID: 28553242
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Comparison of data analysis strategies for intent-to-treat analysis in pre-test-post-test designs with substantial dropout rates.
    Salim A; Mackinnon A; Christensen H; Griffiths K
    Psychiatry Res; 2008 Sep; 160(3):335-45. PubMed ID: 18718673
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A new Bayesian joint model for longitudinal count data with many zeros, intermittent missingness, and dropout with applications to HIV prevention trials.
    Wu J; Chen MH; Schifano ED; Ibrahim JG; Fisher JD
    Stat Med; 2019 Dec; 38(30):5565-5586. PubMed ID: 31691322
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Treatment effects in randomized longitudinal trials with different types of nonignorable dropout.
    Yang M; Maxwell SE
    Psychol Methods; 2014 Jun; 19(2):188-210. PubMed ID: 24079928
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Two-Step Approach for Analysis of Nonignorable Missing Outcomes in Longitudinal Regression: an Application to Upstate KIDS Study.
    Liu D; Yeung EH; McLain AC; Xie Y; Buck Louis GM; Sundaram R
    Paediatr Perinat Epidemiol; 2017 Sep; 31(5):468-478. PubMed ID: 28767145
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Joint Longitudinal Models for Dealing With Missing at Random Data in Trial-Based Economic Evaluations.
    Gabrio A; Hunter R; Mason AJ; Baio G
    Value Health; 2021 May; 24(5):699-706. PubMed ID: 33933239
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An index of local sensitivity to non-ignorability for multivariate longitudinal mixed data with potential non-random dropout.
    Mahabadi SE; Ganjali M
    Stat Med; 2010 Jul; 29(17):1779-92. PubMed ID: 20658547
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improved doubly robust estimation when data are monotonely coarsened, with application to longitudinal studies with dropout.
    Tsiatis AA; Davidian M; Cao W
    Biometrics; 2011 Jun; 67(2):536-45. PubMed ID: 20731640
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multivariate-
    Lin TI; Wang WL
    Stat Methods Med Res; 2020 May; 29(5):1288-1304. PubMed ID: 31242813
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Modeling missingness for time-to-event data: a case study in osteoporosis.
    Neuenschwander B; Branson M
    J Biopharm Stat; 2004 Nov; 14(4):1005-19. PubMed ID: 15587977
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An application of the mixed-effects model and pattern mixture model to treatment groups with differential missingness suspected not-missing-at-random.
    Gosho M; Maruo K
    Pharm Stat; 2021 Jan; 20(1):93-108. PubMed ID: 33249763
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.