On the size accuracy of standard tests in adaptive clinical trials

Host Institution:

La Trobe University

Title of Seminar:

On the size accuracy of standard tests in adaptive clinical trials

Speaker's Name:

Professor Chris J. Lloyd

Speaker's Institution:

Melbourne Business School

Time and Date:

Friday 8 November, 12.00pm (AEDT)

Seminar Abstract:

Adaptive clinical trials involve selection of the most promising treatments at the first stage and re-testing of the selected treatments at a second stage. There will be P-values for testing each treatment at stage one, and additional P-values for testing the selected treatments at stage two. The P-values need to be combined into a single summary P-value that accounts for the selection and the multiple stages.

Combining evidence from several (often two) stages is achieved through a so-called combination function. Accounting for selection is achieved through a so-called multiple comparison function such as the Bonferroni or Simes functions. When control of the family-wise error rate is required, these methods are inserted into the so-called close testing principle of Marcus et al (1976). All these methods give rise to valid and even exact tests provided that the P-values for each treatment are valid or exact.

The problem is that for discrete data most P-values are far from valid or exact. This is an amazing oversight and makes the elaborate theory for combining P-values rather moot. The purpose of this paper is to numerically and theoretically examine the extent to which combining basic tests statistics mitigates or magnifies the size violation of the final test.

 

 

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