BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

107 related articles for article (PubMed ID: 23275450)

  • 1. An efficient method for computing single-parameter partial expected value of perfect information.
    Strong M; Oakley JE
    Med Decis Making; 2013 Aug; 33(6):755-66. PubMed ID: 23275450
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Calculating partial expected value of perfect information via Monte Carlo sampling algorithms.
    Brennan A; Kharroubi S; O'hagan A; Chilcott J
    Med Decis Making; 2007; 27(4):448-70. PubMed ID: 17761960
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Estimating multiparameter partial expected value of perfect information from a probabilistic sensitivity analysis sample: a nonparametric regression approach.
    Strong M; Oakley JE; Brennan A
    Med Decis Making; 2014 Apr; 34(3):311-26. PubMed ID: 24246566
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Simulation sample sizes for Monte Carlo partial EVPI calculations.
    Oakley JE; Brennan A; Tappenden P; Chilcott J
    J Health Econ; 2010 May; 29(3):468-77. PubMed ID: 20378190
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample: A Fast, Nonparametric Regression-Based Method.
    Strong M; Oakley JE; Brennan A; Breeze P
    Med Decis Making; 2015 Jul; 35(5):570-83. PubMed ID: 25810269
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.
    Fang W; Wang Z; Giles MB; Jackson CH; Welton NJ; Andrieu C; Thom H
    Med Decis Making; 2022 Feb; 42(2):168-181. PubMed ID: 34231446
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Need for speed: an efficient algorithm for calculation of single-parameter expected value of partial perfect information.
    Sadatsafavi M; Bansback N; Zafari Z; Najafzadeh M; Marra C
    Value Health; 2013; 16(2):438-48. PubMed ID: 23538197
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An optimization approach to calculating sample sizes with binary responses.
    Maroufy V; Marriott P; Pezeshk H
    J Biopharm Stat; 2014; 24(4):715-31. PubMed ID: 24697665
    [TBL] [Abstract][Full Text] [Related]  

  • 9. An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial.
    Vervaart M; Strong M; Claxton KP; Welton NJ; Wisløff T; Aas E
    Med Decis Making; 2022 Jul; 42(5):612-625. PubMed ID: 34967237
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Parametric and nonparametric population methods: their comparative performance in analysing a clinical dataset and two Monte Carlo simulation studies.
    Bustad A; Terziivanov D; Leary R; Port R; Schumitzky A; Jelliffe R
    Clin Pharmacokinet; 2006; 45(4):365-83. PubMed ID: 16584284
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An Efficient Estimator for the Expected Value of Sample Information.
    Menzies NA
    Med Decis Making; 2016 Apr; 36(3):308-20. PubMed ID: 25911600
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching.
    Heath A; Manolopoulou I; Baio G
    Med Decis Making; 2018 Feb; 38(2):163-173. PubMed ID: 29126364
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Efficient Value of Information Calculation Using a Nonparametric Regression Approach: An Applied Perspective.
    Tuffaha HW; Strong M; Gordon LG; Scuffham PA
    Value Health; 2016 Jun; 19(4):505-9. PubMed ID: 27325343
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A gradient Markov chain Monte Carlo algorithm for computing multivariate maximum likelihood estimates and posterior distributions: mixture dose-response assessment.
    Li R; Englehardt JD; Li X
    Risk Anal; 2012 Feb; 32(2):345-59. PubMed ID: 21906114
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Efficient computation of partial expected value of sample information using Bayesian approximation.
    Brennan A; Kharroubi SA
    J Health Econ; 2007 Jan; 26(1):122-48. PubMed ID: 16945438
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Uncertainty and the Value of Information in Risk Prediction Modeling.
    Sadatsafavi M; Yoon Lee T; Gustafson P
    Med Decis Making; 2022 Jul; 42(5):661-671. PubMed ID: 35209762
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Strategies for efficient computation of the expected value of partial perfect information.
    Madan J; Ades AE; Price M; Maitland K; Jemutai J; Revill P; Welton NJ
    Med Decis Making; 2014 Apr; 34(3):327-42. PubMed ID: 24449434
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation.
    Heath A
    Med Decis Making; 2022 Jul; 42(5):626-636. PubMed ID: 35034542
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Joint propagation of variability and imprecision in assessing the risk of groundwater contamination.
    Baudrit C; Guyonnet D; Dubois D
    J Contam Hydrol; 2007 Aug; 93(1-4):72-84. PubMed ID: 17321003
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Estimating the expected value of partial perfect information: a review of methods.
    Coyle D; Oakley J
    Eur J Health Econ; 2008 Aug; 9(3):251-9. PubMed ID: 17638032
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.