Leveraging External Information in Clinical Trials

7th July - 8th July 2025, Newcastle upon Tyne, UK

Dates

Monday 7th July (9.30am - 5.15pm) - Tuesday 8th July (9.00am - 4.00pm) 2025

Location

Fredrick Douglas Centre, Newcastle upon Tyne

Newcastle is easily accessible via train (frequent trains from London and Edinburgh) and air (Newcastle airport has multiple daily flights to London Heathrow, Schiphol, and Frankfurt).

Cost and Registration

Private sector: £640 Public sector: £425 Students: £320

Registration is open on the University’s storefront page. Payment can be made with credit/debit card or via invoice.

Registration includes lunch and refreshments over the two days and a course dinner on the evening of day 1.

Description

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. This course provides participants with a range of the latest statistical methods that can be used to incorporate external information and thus improve the efficiency and robustness of clinical trials. We also cover potential biases and pitfalls that may arise, and how to address them.

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.

Tutors

Lectures

  1. Limitations of clinical trials and how using external information could help

  2. Introduction to Bayesian inference

  3. Bayesian approaches to borrowing information

  4. Bayesian and hybrid approaches to designing trials

  5. Prior elicitation

  6. Bayesian adaptive designs

  7. Synthetic control approaches

Lectures each have an associated practical session.

FAQs

Q: Is the course running in person or online?

A: Currently we are only planning to run the course in person

Q: I’m not a statistician, will I benefit from the course?

A: The course is predominantly aimed at applied statisticians, but hopefully many parts of the course will be useful to non-statisticians! Some lectures may be more difficult but if you don’t mind some Greek letters and other mathematical symbols, you should be okay.

Quotes from previous participants

“I would absolutely recommend this course. The course covers why and how we could leverage external information in trials and includes many research led examples. I learned a lot from it.”

“Excellent. Builds up from basic to more complex aspects in a well paced and structured manner.”

“Fantastic, it covered the key topics that I need to know regarding Bayesian statistics in clinical trials, and also introduced me to a few new ones.”

“Really enjoyable course overall. Very interesting and engaging. Really enjoyed the discussion and the course meal was fantastic.”

“I really enjoyed the course, it was of great quality with a good breadth and depth of topics covered”