A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Satoshi Morita & Peter F. Thall & Peter Müller, 2008. "Determining the Effective Sample Size of a Parametric Prior," Biometrics, The International Biometric Society, vol. 64(2), pages 595-602, June.
- Jeremy E. Oakley & Anthony O'Hagan, 2007. "Uncertainty in prior elicitations: a nonparametric approach," Biometrika, Biometrika Trust, vol. 94(2), pages 427-441.
- Bornkamp, Björn & Ickstadt, Katja, 2009. "A Note on B-Splines for Semiparametric Elicitation," The American Statistician, American Statistical Association, vol. 63(4), pages 373-377.
- Beat Neuenschwander & Sebastian Weber & Heinz Schmidli & Anthony O'Hagan, 2020. "Predictively consistent prior effective sample sizes," Biometrics, The International Biometric Society, vol. 76(2), pages 578-587, June.
- John Paul Gosling, 2018. "SHELF: The Sheffield Elicitation Framework," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 61-93, Springer.
- Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
- Fulvio De Santis, 2007. "Using historical data for Bayesian sample size determination," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 95-113, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Danila Azzolina & Paola Berchialla & Dario Gregori & Ileana Baldi, 2021. "Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
- Claire Copeland & Britta Turner & Gareth Powells & Kevin Wilson, 2022. "In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures," Energies, MDPI, vol. 15(15), pages 1-21, July.
- Michael D. Teter & Johannes O. Royset & Alexandra M. Newman, 2019. "Modeling uncertainty of expert elicitation for use in risk-based optimization," Annals of Operations Research, Springer, vol. 280(1), pages 189-210, September.
- Schmidli, Heinz & Neuenschwander, Beat & Friede, Tim, 2017. "Meta-analytic-predictive use of historical variance data for the design and analysis of clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 100-110.
- Lichtendahl Jr., Kenneth C., 2009. "Random quantiles of the Dirichlet process," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 501-507, February.
- Liyun Jiang & Lei Nie & Ying Yuan, 2023. "Elastic priors to dynamically borrow information from historical data in clinical trials," Biometrics, The International Biometric Society, vol. 79(1), pages 49-60, March.
- Meghna Bose & Jean‐François Angers & Atanu Biswas, 2023. "Prior effective sample size in phase II clinical trials with mixed binary and continuous responses," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 233-248, May.
- Haiyan Zheng & Thomas Jaki & James M.S. Wason, 2023. "Bayesian sample size determination using commensurate priors to leverage preexperimental data," Biometrics, The International Biometric Society, vol. 79(2), pages 669-683, June.
- Nicolas Bousquet, 2010. "Eliciting vague but proper maximal entropy priors in Bayesian experiments," Statistical Papers, Springer, vol. 51(3), pages 613-628, September.
- Fadlalla G. Elfadaly & Paul H. Garthwaite, 2020. "On quantifying expert opinion about multinomial models that contain covariates," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 959-981, June.
- Maarten Ijzerman & Lotte Steuten, 2011. "Early assessment of medical technologies to inform product development and market access," Applied Health Economics and Health Policy, Springer, vol. 9(5), pages 331-347, September.
- Peng Yang & Yuansong Zhao & Lei Nie & Jonathon Vallejo & Ying Yuan, 2023. "SAM: Self‐adapting mixture prior to dynamically borrow information from historical data in clinical trials," Biometrics, The International Biometric Society, vol. 79(4), pages 2857-2868, December.
- Roland Brown & Yingling Fan & Kirti Das & Julian Wolfson, 2021. "Iterated multisource exchangeability models for individualized inference with an application to mobile sensor data," Biometrics, The International Biometric Society, vol. 77(2), pages 401-412, June.
- Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
- Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
- Nicholas M. Kiefer, 2011.
"Default estimation, correlated defaults, and expert information,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 173-192, March.
- Kiefer, Nicholas M., 2008. "Default Estimation, Correlated Defaults, and Expert Information," Working Papers 08-02, Cornell University, Center for Analytic Economics.
- Ross Gruetzemacher & Kang Bok Lee & David Paradice, 2024. "Calibration training for improving probabilistic judgments using an interactive app," Futures & Foresight Science, John Wiley & Sons, vol. 6(2), June.
- Miller, Joshua Benjamin & Sanjurjo, Adam, 2018. "How Experience Confirms the Gambler's Fallacy when Sample Size is Neglected," OSF Preprints m5xsk, Center for Open Science.
- Jingyi Zhang & Nolan A. Wages & Ruitao Lin, 2024. "SFU: Surface-Free Utility-Based Design for Dose Optimization in Cancer Drug Combination Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(3), pages 854-881, December.
- Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
More about this item
Keywords
Bayesian trial; semiparametric; elicitation; sample size; phase II;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14245-:d:959249. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.