Leveraging External Information in Clinical Trials

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The ability to utilise external information (such as from disease cohorts, previous trials, and expert opinion) when designing and analysing clinical trials brings many benefits, including maximising the evidence provided by the trial, reducing the sample size required (particularly important for rare disease trials) and improving the generalisability of trial results.

The following topics will be included:

• Bayesian methods that form prior distributions from elicited and (multiple) external data sources.
• Bayesian and hybrid approaches (e.g. assurance) that account for uncertainty in sample size calculations.
• Methods that facilitate borrowing of historical information or data from within the same trial (e.g. master protocols).
• Frequentist methods (e.g. propensity score weighting) that use external data, such as cohort studies and routinely collected healthcare records, to for synthetic control groups and generalise results from less representative trials to wider patient populations.
• Application to real clinical trials, including trials for rare diseases.

As well as the necessary theory, we will cover computational approaches to implement the methods and practical issues, such as funder and regulator views.

For more information on this course, please contact llahub@newcastle.ac.uk or enquire here