Use of Bayesian statistics in drug development: Advantages and challenges
Mainly, two statistical methodologies are applicable to the design and analysis of clinical trials: frequentist and Bayesian. Most traditional clinical trial designs are based on frequentist statistics. In frequentist statistics prior information is utilized formally only in the design of a clinical...
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| Format: | Online |
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Medknow Publications & Media Pvt Ltd
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657986/ |
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pubmed-36579862013-06-17 Use of Bayesian statistics in drug development: Advantages and challenges Gupta, Sandeep K Educational Forum Mainly, two statistical methodologies are applicable to the design and analysis of clinical trials: frequentist and Bayesian. Most traditional clinical trial designs are based on frequentist statistics. In frequentist statistics prior information is utilized formally only in the design of a clinical trial but not in the analysis of the data. On the other hand, Bayesian statistics provide a formal mathematical method for combining prior information with current information at the design stage, during the conduct of the trial, and at the analysis stage. It is easier to implement adaptive trial designs using Bayesian methods than frequentist methods. The Bayesian approach can also be applied for post-marketing surveillance purposes and in meta-analysis. The basic tenets of good trial design are same for both Bayesian and frequentist trials. It has been recommended that the type of analysis to be used (Bayesian or frequentist) should be chosen beforehand. Switching to an analysis method that produces a more favorable outcome after observing the data is not recommended. Medknow Publications & Media Pvt Ltd 2012 /pmc/articles/PMC3657986/ /pubmed/23776799 http://dx.doi.org/10.4103/2229-516X.96789 Text en Copyright: © International Journal of Applied and Basic Medical Research http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Gupta, Sandeep K |
| spellingShingle |
Gupta, Sandeep K Use of Bayesian statistics in drug development: Advantages and challenges |
| author_facet |
Gupta, Sandeep K |
| author_sort |
Gupta, Sandeep K |
| title |
Use of Bayesian statistics in drug development: Advantages and challenges |
| title_short |
Use of Bayesian statistics in drug development: Advantages and challenges |
| title_full |
Use of Bayesian statistics in drug development: Advantages and challenges |
| title_fullStr |
Use of Bayesian statistics in drug development: Advantages and challenges |
| title_full_unstemmed |
Use of Bayesian statistics in drug development: Advantages and challenges |
| title_sort |
use of bayesian statistics in drug development: advantages and challenges |
| description |
Mainly, two statistical methodologies are applicable to the design and analysis of clinical trials: frequentist and Bayesian. Most traditional clinical trial designs are based on frequentist statistics. In frequentist statistics prior information is utilized formally only in the design of a clinical trial but not in the analysis of the data. On the other hand, Bayesian statistics provide a formal mathematical method for combining prior information with current information at the design stage, during the conduct of the trial, and at the analysis stage. It is easier to implement adaptive trial designs using Bayesian methods than frequentist methods. The Bayesian approach can also be applied for post-marketing surveillance purposes and in meta-analysis. The basic tenets of good trial design are same for both Bayesian and frequentist trials. It has been recommended that the type of analysis to be used (Bayesian or frequentist) should be chosen beforehand. Switching to an analysis method that produces a more favorable outcome after observing the data is not recommended. |
| publisher |
Medknow Publications & Media Pvt Ltd |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3657986/ |
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