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Bayesian Adaptive Designs

Bayesian Methods in Clinical Trials

In clinical research, Bayesian statistical methods 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.

Making use of relevant prior information can reduce sample sizes (or shorten trial lengths) required to meet objectives and so reduce overall development costs. But there are advantages beyond the costs.  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 randomisation to treatment could be altered to favour 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. And again, more efficient use of data can lead to lower overall costs. In general, adaptive trial designs are easier to implement within the Bayesian framework. 

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. In addition, 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 / Bayesian methodology to the particular research programme and design stage of that programme.


Our Bayesian Study Design Resources

Bayesian Study Design: The Pragmatic Solution for Phase II Clinical Development

If you are planning a Phase II study, you may find this 8-page article useful as it looks at how the Bayesian framework provides solutions for clinical development teams.


Bayesian Study Design: Using Interim Analyses to Improve Efficiency in Drug Development

This second article expands on how the Bayesian framework is highly appropriate for planning and executing interim analyses in your clinical trial study design.

Latest Blogs on Bayesian Statistics in Clinical Trials