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Bayesian Approaches to Randomized Trials
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Cited by:
- Isakov, Leah & Lo, Andrew W. & Montazerhodjat, Vahid, 2019.
"Is the FDA too conservative or too aggressive?: A Bayesian decision analysis of clinical trial design,"
Journal of Econometrics, Elsevier, vol. 211(1), pages 117-136.
- Vahid Montazerhodjat & Andrew W. Lo, 2015. "Is the FDA Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design," NBER Working Papers 21499, National Bureau of Economic Research, Inc.
- Leonhard Held, 2020. "A new standard for the analysis and design of replication studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 431-448, February.
- Jingjing Ye & Gregory Reaman, 2022. "Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 337-351, July.
- Francisco-José Polo & Miguel Negrín & Xavier Badía & Montse Roset, 2005. "Bayesian regression models for cost-effectiveness analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 6(1), pages 45-52, March.
- Darren Bernard & Nicole L. Cade & Frank Hodge, 2018. "Investor Behavior and the Benefits of Direct Stock Ownership," Journal of Accounting Research, Wiley Blackwell, vol. 56(2), pages 431-466, May.
- Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
- Charles F. Manski & Aleksey Tetenov, 2015.
"Clinical trial design enabling e-optimal treatment rules,"
CeMMAP working papers
CWP60/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Charles F. Manski & Aleksey Tetenov, 2015. "Clinical trial design enabling ε-optimal treatment rules," CeMMAP working papers 60/15, Institute for Fiscal Studies.
- Charles F. Manski, 2017. "Improving Clinical Guidelines and Decisions under Uncertainty," NBER Working Papers 23915, National Bureau of Economic Research, Inc.
- Constantin Volkmann & Alexander Volkmann & Christian A Müller, 2020. "On the treatment effect heterogeneity of antidepressants in major depression: A Bayesian meta-analysis and simulation study," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
- Paul Gustafson & Nhu D. Le & Refik Saskin, 2001. "Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities," Biometrics, The International Biometric Society, vol. 57(2), pages 598-609, June.
- Kruschke, John K. & Liddell, Torrin, 2016. "The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective," OSF Preprints ksfyr_v1, Center for Open Science.
- Charles F. Manski, 2019. "Meta-Analysis for Medical Decisions," NBER Working Papers 25504, National Bureau of Economic Research, Inc.
- Kruschke, John K. & Liddell, Torrin, 2016. "The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective," OSF Preprints ksfyr, Center for Open Science.
- Bradley P. Carlin & James S. Hodges, 1999. "Hierarchical Proportional Hazards Regression Models for Highly Stratified Data," Biometrics, The International Biometric Society, vol. 55(4), pages 1162-1170, December.
- David M. Rindskopf & William R. Shadish & M. H. Clark, 2018. "Using Bayesian Correspondence Criteria to Compare Results From a Randomized Experiment and a Quasi-Experiment Allowing Self-Selection," Evaluation Review, , vol. 42(2), pages 248-280, April.
- Martin E. Backhouse, 1998. "An investment appraisal approach to clinical trial design," Health Economics, John Wiley & Sons, Ltd., vol. 7(7), pages 605-619, November.
- Poitevineau, Jacques & Lecoutre, Bruno, 2010. "Implementing Bayesian predictive procedures: The K-prime and K-square distributions," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 724-731, March.
- James C. Felli & Gordon B. Hazen, 1998. "Sensitivity Analysis and the Expected Value of Perfect Information," Medical Decision Making, , vol. 18(1), pages 95-109, January.
- Peter F. Thall & Richard M. Simon & Yu Shen, 2000. "Approximate Bayesian Evaluation of Multiple Treatment Effects," Biometrics, The International Biometric Society, vol. 56(1), pages 213-219, March.
- Nandini Dendukuri & Lawrence Joseph, 2001. "Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests," Biometrics, The International Biometric Society, vol. 57(1), pages 158-167, March.
- Karl Claxton & John Posnett, 1996. "An economic approach to clinical trial design and research priority‐setting," Health Economics, John Wiley & Sons, Ltd., vol. 5(6), pages 513-524, November.
- Danila Azzolina & Giulia Lorenzoni & Silvia Bressan & Liviana Da Dalt & Ileana Baldi & Dario Gregori, 2021. "Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
- Miguel A. Negrín & Francisco J. Vázquez-Polo & María Martel & Elías Moreno & Francisco J. Girón, 2010. "Bayesian Variable Selection in Cost-Effectiveness Analysis," IJERPH, MDPI, vol. 7(4), pages 1-20, April.
- Charles F. Manski, 2021.
"Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald,"
Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
- Charles F. Manski, 2019. "Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald," NBER Working Papers 26596, National Bureau of Economic Research, Inc.
- Charles F. Manski, 2019. "Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald," Papers 1912.08726, arXiv.org, revised Feb 2021.
- Norman Simón Rodríguez Cano, 2018. "Tendencias actuales en la evaluación de políticas públicas," Ensayos de Economía 17296, Universidad Nacional de Colombia Sede Medellín.
- Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.