Leveraging External Information in Clinical Trials
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
Course Information
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.
If you work in the Public Sector, use code "CTPubSec2024" for a reduced price.
If you are a student, use code "CTStudent2024" for a reduced price.
For more information on this course, please contact llahub@newcastle.ac.uk or enquire here
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Course Dates
Course Start Date: Tuesday, May 28, 2024 8:00 AM
Course End Date : Wednesday, May 29, 2024 7:00 PM
Enrolment Dates
Enrolment Start Date: Tuesday, January 16, 2024 8:00 AM
Enrolment End Date : Sunday, May 26, 2024 10:00 PM
Course Location
Newcastle University
Course Tutor/s
Course Delivery Type
Face to Face 
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Price per attendee is £640.00 (zero VAT)