| Predictive probability of success and the assessment of futility in large outcomes trials. | |
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MedLine Citation:
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PMID: 17219755 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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We consider a class of futility rules based on a Bayesian approach for computing the predictive probability of success for large clinical trials, given a certain amount of observed data. This paper focuses on outcomes trials in particular, thus we are concerned with binary response variables. The proposed method determines the likelihood of observing a statistically significant treatment effect at the end of a study, conditional on the data observed at an interim time point and assuming that event rates governing future observations follow beta distributions. In particular, the prior distributions for the event rates of interest are updated based on the observed data at an interim time point, such that means and variances are intuitive functions of the data. Computational aspects will be discussed for the case in which event counts are functions of sample size and event rates only, and for situations in which they are functions of sample size, event rates, and exposure duration. We will discuss appropriate thresholds for declaring futility based on this approach, and the potential impact of overdispersion, a common phenomenon particularly in global outcomes trials. |
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Authors:
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Benjamin Trzaskoma; Andreas Sashegyi |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Journal of biopharmaceutical statistics Volume: 17 ISSN: 1054-3406 ISO Abbreviation: J Biopharm Stat Publication Date: 2007 |
Date Detail:
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Created Date: 2007-01-15 Completed Date: 2007-02-09 Revised Date: 2007-11-15 |
Medline Journal Info:
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Nlm Unique ID: 9200436 Medline TA: J Biopharm Stat Country: United States |
Other Details:
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Languages: eng Pagination: 45-63 Citation Subset: IM |
Affiliation:
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Eli Lilly and Company, Lilly Corporate Center, DC 6072, Indianapolis, IN 46285, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Anti-Infective Agents / therapeutic use Bayes Theorem* Clinical Trials Data Monitoring Committees Computer Simulation Controlled Clinical Trials as Topic / statistics & numerical data* Humans Medical Futility* Probability Protein C / therapeutic use Recombinant Proteins / therapeutic use Research Design Sample Size Sepsis / drug therapy Software Time Factors Treatment Outcome |
| Chemical | |
Reg. No./Substance:
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0/Anti-Infective Agents; 0/Protein C; 0/Recombinant Proteins; 0/drotrecogin alfa activated |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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