MORE TICKETS ADDED.

Due to the popularity of this Training Course, we are delighted to announce that we have added a further 20 places to access the recorded sessions only, for a period of one month. Delegates who purchase these tickets will not be able to participate in the live sessions* with our presenter, but will receive a link to view the recorded sessions later on that same day. All delegates will also receive a copy of the book, upon which this Course is based.

*Due to a lack of access to the live sessions, the recording-only tickets are offered at a slightly reduced price to reflect this.

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Date: Monday 4th, Wednesday 6th & Friday 8th July 2022

Time:4th 09:30-12:00, 6th&8th 10:00-12:00 BST

Location: This course will take place online.

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.

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