Registration will close July 1, 2022 at

For the most part the planning of clinical trials is based on considerations of the power of a test of a given alternative hypothesis based on ideas introduced by Neyman and Pearson in 1933. As early as 1939, Jeffreys pointed out that if the true value was unknown, so was the power. Jeffreys suggested that to understand the true power of a study the conditional power values should be averaged with respect to their prior probabilities, an unconditional power. This idea was taken up in the 1980's by Spiegelhalter and colleagues and in the early 2000s by O'Hagan and Stevens who introduced the concept of assurance. All of this work uses unconditional as opposed to conditional probabilities.

Who is this event intended for? Statisticians working on the design of clinical trials.

What is the benefit of attending? Participants will learn how to use Expected Power, Average Power, Predicted Power, Probability of Success and Assurance, and Bayesian Power when planning clinical trials.

This course will take place online. Please note: Early Bird rates are available, valid until 17:00 BST on Friday 3rd June.

The course is based on a book with the same title in the Chapman Hall/CRC Biostatistics Series to appear in May 2022. All participants for this course will receive a copy of the book.

Topics covered include:

  • Expected Power, Average Power (AP), Predicted Power, Probability of Success and Assurance for a Simple Normal Model with Known Variance

-Bounds on AP and Assurance

-Sample Size for a Given AP/Assurance and Normalized Assurance.

-Applying Assurance to a Series of Studies

-Assurance for a Clinical Trial with a Single Interim Analysis

-Non-Inferiority Trials

  • AP in Non-Normal Settings – Unknown variance, Binary Data, Survival Analysis
  • Bayesian Power (BP)

-Bounds on BP

-Sample Size for a Given BP/Normalized BP

-Posterior Conditional Success Distributions

-Prior Distributions for Power and sample Size

  • Interim Predictions and Links to AP
  • AP with Multiple Decision Criteria – Normal Model with Known Variance

-Bounds on AP and Assurance

-Generalized Assurance

-Bayesian Approach to Multiple Decision Criteria.

-Posterior Conditional GO/NOGO/Pause/Distributions

  • Surety and Assurance in Estimation

-An Alternative to Power in sample Size Determination

-Unconditional Sample Sizing Based on CI width


  • Member Early-Bird


    Member Early Bird Price

    Buy Ticket
  • Member


    Member Price

    Buy Ticket
  • Non-Member Early-Bird


    Early Bird Price

    *Includes PSI membership for the remainder of the calendar year.

    Buy Ticket
  • Non-Member


    Public Price

    *Includes PSI membership for the remainder of the calendar year.

    Buy Ticket


Connect with 15 people attending this event

Register & Join the Community