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 *

191 related articles for article (PubMed ID: 30824126)

  • 1. Mediation analysis with zero-inflated substance use outcomes: Challenges and recommendations.
    O'Rourke HP; Vazquez E
    Addict Behav; 2019 Jul; 94():16-25. PubMed ID: 30824126
    [TBL] [Abstract][Full Text] [Related]  

  • 2. On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data.
    Rose CE; Martin SW; Wannemuehler KA; Plikaytis BD
    J Biopharm Stat; 2006; 16(4):463-81. PubMed ID: 16892908
    [TBL] [Abstract][Full Text] [Related]  

  • 3. On performance of parametric and distribution-free models for zero-inflated and over-dispersed count responses.
    Tang W; Lu N; Chen T; Wang W; Gunzler DD; Han Y; Tu XM
    Stat Med; 2015 Oct; 34(24):3235-45. PubMed ID: 26078035
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Zero-inflated count models for longitudinal measurements with heterogeneous random effects.
    Zhu H; Luo S; DeSantis SM
    Stat Methods Med Res; 2017 Aug; 26(4):1774-1786. PubMed ID: 26113383
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A comparison of statistical methods for modeling count data with an application to hospital length of stay.
    Fernandez GA; Vatcheva KP
    BMC Med Res Methodol; 2022 Aug; 22(1):211. PubMed ID: 35927612
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Zero inflated statistical count models for analysing the costs imposed by GERD and dyspepsia.
    Akbarzadeh Baghban A; Pourhoseingholi A; Zayeri F; Ashtari S; Zali MR
    Arab J Gastroenterol; 2013 Dec; 14(4):165-8. PubMed ID: 24433646
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Variable selection for distribution-free models for longitudinal zero-inflated count responses.
    Chen T; Wu P; Tang W; Zhang H; Feng C; Kowalski J; Tu XM
    Stat Med; 2016 Jul; 35(16):2770-85. PubMed ID: 26844819
    [TBL] [Abstract][Full Text] [Related]  

  • 8. The utility of the zero-inflated Poisson and zero-inflated negative binomial models: a case study of cross-sectional and longitudinal DMF data examining the effect of socio-economic status.
    Lewsey JD; Thomson WM
    Community Dent Oral Epidemiol; 2004 Jun; 32(3):183-9. PubMed ID: 15151688
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A GEE-type approach to untangle structural and random zeros in predictors.
    Ye P; Tang W; He J; He H
    Stat Methods Med Res; 2019 Dec; 28(12):3683-3696. PubMed ID: 30472921
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Zero-inflated and hurdle models of count data with extra zeros: examples from an HIV-risk reduction intervention trial.
    Hu MC; Pavlicova M; Nunes EV
    Am J Drug Alcohol Abuse; 2011 Sep; 37(5):367-75. PubMed ID: 21854279
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Nonlinear mixed-effects modeling of longitudinal count data: Bayesian inference about median counts based on the marginal zero-inflated discrete Weibull distribution.
    Burger DA; Lesaffre E
    Stat Med; 2021 Oct; 40(23):5078-5095. PubMed ID: 34155664
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Untangle the Structural and Random Zeros in Statistical Modelings.
    Tang W; He H; Wang WJ; Chen DG
    J Appl Stat; 2018; 45(9):1714-1733. PubMed ID: 30906098
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A score test for overdispersion in zero-inflated poisson mixed regression model.
    Xiang L; Lee AH; Yau KK; McLachlan GJ
    Stat Med; 2007 Mar; 26(7):1608-22. PubMed ID: 16794991
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Distribution-free Inference of Zero-inated Binomial Data for Longitudinal Studies.
    He H; Wang WJ; Hu J; Gallop R; Crits-Christoph P; Xia YL
    J Appl Stat; 2015 Oct; 42(10):2203-2219. PubMed ID: 26435563
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Modelling count data with excessive zeros: the need for class prediction in zero-inflated models and the issue of data generation in choosing between zero-inflated and generic mixture models for dental caries data.
    Gilthorpe MS; Frydenberg M; Cheng Y; Baelum V
    Stat Med; 2009 Dec; 28(28):3539-53. PubMed ID: 19902494
    [TBL] [Abstract][Full Text] [Related]  

  • 16. New variable selection methods for zero-inflated count data with applications to the substance abuse field.
    Buu A; Johnson NJ; Li R; Tan X
    Stat Med; 2011 Aug; 30(18):2326-40. PubMed ID: 21563207
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Modeling change trajectories with count and zero-inflated outcomes: Challenges and recommendations.
    Grimm KJ; Stegmann G
    Addict Behav; 2019 Jul; 94():4-15. PubMed ID: 30322730
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mediation analysis for count and zero-inflated count data without sequential ignorability and its application in dental studies.
    Guo Z; Small DS; Gansky SA; Cheng J
    J R Stat Soc Ser C Appl Stat; 2018 Feb; 67(2):371-394. PubMed ID: 30983638
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Marginalized zero-inflated negative binomial regression with application to dental caries.
    Preisser JS; Das K; Long DL; Divaris K
    Stat Med; 2016 May; 35(10):1722-35. PubMed ID: 26568034
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Count data distributions and their zero-modified equivalents as a framework for modelling microbial data with a relatively high occurrence of zero counts.
    Gonzales-Barron U; Kerr M; Sheridan JJ; Butler F
    Int J Food Microbiol; 2010 Jan; 136(3):268-77. PubMed ID: 19913934
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.