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Bayesian Methods in Clinical Design

Take advantage of prior information to make decisions to reduce trial costs and improve trial efficiency.

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In clinical research, Bayesian statistics provide a framework in which information beyond that collected in a particular clinical trial can be used to make statistical inferences about the treatment outcomes. Prior information (from previous trials, scientific research or “expert opinion”) can be combined with information as it is accrued during a trial, as well as with the usual data available on completion of the trial, to make efficient and timely inferences about the safety and/or efficacy of a treatment or therapy.

Benefits of Bayesian Methods in Clinical Design

Due to the expensive nature of clinical trials, more and more pharmaceutical companies are becoming interested in Bayesian methods; and with on-going algorithmic development and improved computational speeds, these methods are becoming increasingly accessible and accepted.


  • Reduced costs - Making use of relevant prior information can reduce sample sizes (or shorten trial lengths) required to meet objectivesreducing overall development costs.  
  • Improved trial design efficiency - Use of data as they are collected (either through interim analyses or continual reassessment methods) allows the trial design to be adapted to improve design efficiency. For example, ineffective treatment arms could be dropped; further treatment arms could be introduced; the trial could be stopped early (due to established futility /efficacy); or randomization to treatment could be altered to favor the more effective treatment. Such adaptations are attractive to both researchers and patients, by making more efficient use of patient resource and potentially treating patients more effectively.  In general adaptive clinical trial designs are easier to implement within the Bayesian framework.  
  • Frequentist designs may not always work - Bayesian methods have particular advantages in rare disease scenarios where traditional sample sizes can be difficult – if not impossible - to achieve. The standard frequentist methodology of hypothesis testing in a clinical trial may not always be the best approach and Bayesian methods allow alternative approaches to be considered. 


At Quanticate our statistical consultants are experienced in clinical study designs and have delivered multiple trial analyses using Bayesian methods.  We can provide expert advice on Bayesian adaptive designs with an approach that typically includes: 

The choice of an appropriate prior based on previous studies and other existing knowledge of the treatment/therapy

Statistical modelling to inform safe and efficient dose escalation towards a MTD

The collection of robust data to demonstrate the safety/efficacy of a treatment; employing an adaptive approach to allow budgets to be used more efficiently

An adaptive design to keep sample sizes low whilst treating patients as effectively as possible

Interim Analyses using predictive probabilities to stop a trial either if the treatment is proving to be ineffective (futility) or if the treatment has already proven itself to be effective before completion (efficacy)

Best advice on the suitability of an adaptive design or Bayesian methodology to the particular research programme and design stage of that programme

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