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 *

391 related articles for article (PubMed ID: 26530822)

  • 1. Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping.
    Carroll R; Lawson AB; Faes C; Kirby RS; Aregay M; Watjou K
    Spat Spatiotemporal Epidemiol; 2015; 14-15():45-54. PubMed ID: 26530822
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Spatial and spatio-temporal models with R-INLA.
    Blangiardo M; Cameletti M; Baio G; Rue H
    Spat Spatiotemporal Epidemiol; 2013 Dec; 7():39-55. PubMed ID: 24377114
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Spatial and spatio-temporal models with R-INLA.
    Blangiardo M; Cameletti M; Baio G; Rue H
    Spat Spatiotemporal Epidemiol; 2013 Mar; 4():33-49. PubMed ID: 23481252
    [TBL] [Abstract][Full Text] [Related]  

  • 4. On fitting spatio-temporal disease mapping models using approximate Bayesian inference.
    Ugarte MD; Adin A; Goicoa T; Militino AF
    Stat Methods Med Res; 2014 Dec; 23(6):507-30. PubMed ID: 24713158
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Network meta-analysis with integrated nested Laplace approximations.
    Sauter R; Held L
    Biom J; 2015 Nov; 57(6):1038-50. PubMed ID: 26360927
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A design-by-treatment interaction model for network meta-analysis and meta-regression with integrated nested Laplace approximations.
    Günhan BK; Friede T; Held L
    Res Synth Methods; 2018 Jun; 9(2):179-194. PubMed ID: 29193801
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique.
    Moraga P; Dean C; Inoue J; Morawiecki P; Noureen SR; Wang F
    Spat Spatiotemporal Epidemiol; 2021 Nov; 39():100440. PubMed ID: 34774255
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Animal models and integrated nested Laplace approximations.
    Holand AM; Steinsland I; Martino S; Jensen H
    G3 (Bethesda); 2013 Aug; 3(8):1241-51. PubMed ID: 23708299
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations.
    Paul M; Riebler A; Bachmann LM; Rue H; Held L
    Stat Med; 2010 May; 29(12):1325-39. PubMed ID: 20101670
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A tutorial on spatio-temporal disease risk modelling in R using Markov chain Monte Carlo simulation and the CARBayesST package.
    Lee D
    Spat Spatiotemporal Epidemiol; 2020 Aug; 34():100353. PubMed ID: 32807395
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation.
    Mathew B; Holand AM; Koistinen P; Léon J; Sillanpää MJ
    Theor Appl Genet; 2016 Feb; 129(2):215-25. PubMed ID: 26582509
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Space-time interactions in Bayesian disease mapping with recent tools: Making things easier for practitioners.
    Urdangarin A; Goicoa T; Dolores Ugarte M
    Stat Methods Med Res; 2022 Jun; 31(6):1085-1103. PubMed ID: 35179396
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran.
    Asmarian N; Ayatollahi SMT; Sharafi Z; Zare N
    Int J Environ Res Public Health; 2019 Nov; 16(22):. PubMed ID: 31766251
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Estimating multilevel regional variation in excess mortality of cancer patients using integrated nested Laplace approximation.
    Seppä K; Rue H; Hakulinen T; Läärä E; Sillanpää MJ; Pitkäniemi J
    Stat Med; 2019 Feb; 38(5):778-791. PubMed ID: 30334278
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.
    Schmidt PJ; Pintar KD; Fazil AM; Topp E
    Risk Anal; 2013 Sep; 33(9):1677-93. PubMed ID: 23311599
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested Laplace approximations.
    Riebler A; Held L
    Biom J; 2017 May; 59(3):531-549. PubMed ID: 28139001
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Hierarchical Bayesian estimates of distributed MEG sources: theoretical aspects and comparison of variational and MCMC methods.
    Nummenmaa A; Auranen T; Hämäläinen MS; Jääskeläinen IP; Lampinen J; Sams M; Vehtari A
    Neuroimage; 2007 Apr; 35(2):669-85. PubMed ID: 17300961
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An accessible method for implementing hierarchical models with spatio-temporal abundance data.
    Ross BE; Hooten MB; Koons DN
    PLoS One; 2012; 7(11):e49395. PubMed ID: 23166658
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data.
    Baghfalaki T; Ganjali M
    Stat Methods Med Res; 2021 Jun; 30(6):1484-1501. PubMed ID: 33872092
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Age-space-time CAR models in Bayesian disease mapping.
    Goicoa T; Ugarte MD; Etxeberria J; Militino AF
    Stat Med; 2016 Jun; 35(14):2391-405. PubMed ID: 26814019
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.