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 *

132 related articles for article (PubMed ID: 32377032)

  • 1. BiMM tree: A decision tree method for modeling clustered and longitudinal binary outcomes.
    Speiser JL; Wolf BJ; Chung D; Karvellas CJ; Koch DG; Durkalski VL
    Commun Stat Simul Comput; 2020; 49(4):1004-1023. PubMed ID: 32377032
    [TBL] [Abstract][Full Text] [Related]  

  • 2. BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes.
    Speiser JL; Wolf BJ; Chung D; Karvellas CJ; Koch DG; Durkalski VL
    Chemometr Intell Lab Syst; 2019 Feb; 185():122-134. PubMed ID: 31656362
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.
    Speiser JL; Karvellas CJ; Wolf BJ; Chung D; Koch DG; Durkalski VL
    Comput Methods Programs Biomed; 2019 Jul; 175():111-120. PubMed ID: 31104700
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.
    Speiser JL
    J Biomed Inform; 2021 May; 117():103763. PubMed ID: 33781921
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data.
    Fokkema M; Edbrooke-Childs J; Wolpert M
    Psychother Res; 2021 Mar; 31(3):313-325. PubMed ID: 32602811
    [No Abstract]   [Full Text] [Related]  

  • 6. Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.
    Fokkema M; Smits N; Zeileis A; Hothorn T; Kelderman H
    Behav Res Methods; 2018 Oct; 50(5):2016-2034. PubMed ID: 29071652
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.
    Wolf BJ; Jiang Y; Wilson SH; Oates JC
    J Clin Transl Sci; 2020 Nov; 5(1):e59. PubMed ID: 33948279
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.
    Ferrari A; Comelli M
    J Neurosci Methods; 2016 Dec; 274():131-140. PubMed ID: 27751892
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.
    Zhu K; Lou Z; Zhou J; Ballester N; Kong N; Parikh P
    Methods Inf Med; 2015; 54(6):560-7. PubMed ID: 26548400
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model.
    Xiao J; Chen L; Johnson S; Yu Y; Zhang X; Chen J
    Front Microbiol; 2018; 9():1391. PubMed ID: 29997602
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Generalized quasi-linear mixed-effects model.
    Saigusa Y; Eguchi S; Komori O
    Stat Methods Med Res; 2022 Jul; 31(7):1280-1291. PubMed ID: 35286226
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint.
    Adrion C; Mansmann U
    BMC Med Res Methodol; 2012 Sep; 12():137. PubMed ID: 22962944
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Analyzing longitudinal binary data in clinical studies.
    Li Y; Feng D; Sui Y; Li H; Song Y; Zhan T; Cicconetti G; Jin M; Wang H; Chan I; Wang X
    Contemp Clin Trials; 2022 Apr; 115():106717. PubMed ID: 35240309
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Estimating relative risks in multicenter studies with a small number of centers - which methods to use? A simulation study.
    Pedroza C; Truong VTT
    Trials; 2017 Nov; 18(1):512. PubMed ID: 29096682
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comparison of predictor approaches for longitudinal binary outcomes: application to anesthesiology data.
    Aktas Samur A; Coskunfirat N; Saka O
    PeerJ; 2014; 2():e648. PubMed ID: 25374787
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm.
    Tovissodé CF; Diop A; Glèlè Kakaï R
    PLoS One; 2021; 16(4):e0249604. PubMed ID: 33822818
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.
    Asadi F; Homayounfar R; Farjam M; Mehrali Y; Masaebi F; Zayeri F
    Arch Iran Med; 2024 May; 27(5):239-247. PubMed ID: 38690790
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study.
    Bakbergenuly I; Kulinskaya E
    BMC Med Res Methodol; 2018 Jul; 18(1):70. PubMed ID: 29973146
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Neural networks for clustered and longitudinal data using mixed effects models.
    Mandel F; Ghosh RP; Barnett I
    Biometrics; 2023 Jun; 79(2):711-721. PubMed ID: 34951484
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.
    Ngufor C; Van Houten H; Caffo BS; Shah ND; McCoy RG
    J Biomed Inform; 2019 Jan; 89():56-67. PubMed ID: 30189255
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.