FDA along with other stakeholders in the pharmaceutical industry has been continuously exploring ways of containing costs of drug development. In its current structure, final outcomes of the trial becomes extremely pivotal to the sponsor and the CRO. Research has been ongoing to identify ways to optimise the process. One of the methods of optimisation is to make good use of all existing information about the trial.Bayesian statistics presents a perfect opportunity in such a scenario.
Frequentist and Bayesian methods are the two statistical methodologies that are applicable to the design and analysis of clinical trials:. Most of the current clinical trial designs are based on frequentist statistics. In frequentist statistics information from previous trials are used only in the design of a clinical trial. They don’t form part of the data analysis. However, 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.
The roots of Bayesian statistics lies in Bayes’ theorem. Bayes’ theorem arose from a publication in 1763 by Thomas Bayes. This theorem of Bayes was not published during his lifetime but only after his death, when his work was found in his desk by a friend. Bayesian statistics starts with a prior belief (expressed as a prior distribution), which is then updated with the new evidence to yield a posterior belief (also a probability distribution). Bayesian statistics provides a mathematical method for calculating the likelihood of a future event, given the knowledge from prior events. These methods, thus, directly address the question of how new evidence should change what we currently believe.
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Use of Bayesian statistics in drug development: Advantages and challenges
Sandeep K Gupta
Int J Appl Basic Med Res. 2012 Jan-Jun; 2(1): 3–6. doi: 10.4103/2229-516X.96789