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1.
Stephen J. Ruberg Frank E. Harrell Jr. Margaret Gamalo-Siebers Lisa LaVange J. Jack Lee Karen Price 《The American statistician》2019,73(1):319-327
ABSTRACTThe cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making. 相似文献
2.
Multi-valued strategy-proof social choice rules 总被引:10,自引:4,他引:6
In this paper we introduce a new definition of strategy-proofness for multi-valued social choice correspondences. We prove
two Gibbard-Satterthwaite type results for strategy-proof social choice correspondences. These results show that allowing
multiple outcomes as social choices will not necessarily lead to an escape from the Gibbard-Satterthwaite impossibility theorem.
Received: 24 January 2001/Accepted: 19 March 2001 相似文献
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A sample size justification should be given for all clinical investigations. However, sometimes the objective of a trial is to estimate an effect with a view to planning a later definitive study. This paper describes the calculations for designing studies where one wishes to adopt an estimation approach through using confidence intervals around the overall response. Calculations are given for data anticipated to take a Normal form. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
5.
Professor Stephen Senn Dr Dipti Amin Professor Rosemary A. Bailey Professor Sheila M. Bird FFPH Dr Barbara Bogacka Mr Peter Colman Dr rew Garrett Professor rew Grieve Professor Sir Peter Lachmann FRS FMedSci 《Journal of the Royal Statistical Society. Series A, (Statistics in Society)》2007,170(3):517-579
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Stephen Walker 《Statistics and Computing》1995,5(4):311-315
Laud et al. (1993) describe a method for random variate generation from D-distributions. In this paper an alternative method using substitution sampling is given. An algorithm for the random variate generation from SD-distributions is also given. 相似文献
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Christian P. Robert Xiao-Li Meng Jesper Møller Jeffrey S Rosenthal C Jennison M. A Hurn F Al-Awadhi Peter McCullagh Christophe Andrieu Arnaud Doucet Petros Dellaportas Ioulia Papageorgiou Ricardo S Ehlers Elena A Erosheva Stephen E Fienberg Jonathan J Forster Roger C Gill Nial Friel Peter Green David Hastie R King Hans R Künsch N. A. Lazar C Osinski 《Journal of the Royal Statistical Society. Series B, Statistical methodology》2003,65(1):39-55