Use of Bayesian statistics in drug development: Advantages and challenges
By S. Gupta. 2012
49 citations recorded by the source corpus.
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.
Subfields: Management Science and Operations Research, Statistics and Probability
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Citations in the corpus
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, background): “The Bayesian adaptive design may suffer from operational biases; thus, the confidentiality of the data needs to be maintained [161].”
- Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector (2023, background): “On the other hand, where knowledge is lacking, domain-agnostic generative AI methods are suitable; Bayesian reasoning and deep learning networks are considered well suited combination [51, 52].”