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Imagine that it’s been three years since the completion of a trial, and that suddenly a regulatory body calls into question the findings:
- Was a particular trial site operating properly?
- Can you clarify an aspect of the results?
- Why did you make a particular decision at an interim look?
Suddenly, your somewhat old data needs to be able to reproduce your initial findings. In such a case, how long would it take you to satisfy the regulatory body?
Professor LJ Wei holds that rules are for lawyers, not (necessarily) clinicians. When designing modern clinical trials, the impetus is often to use “efficient and reliable procedures, to obtain clinically interpretable results with respect to risk-benefit analysis…” Yet these efficient and reliable procedures are often just conventions and rules that provide information that is incomplete or difficult to make clinically interpretable.
In a presentation to the East User Group Meeting, Professor Wei identifies 11 problematic areas that currently challenge trial designers. After giving an overview of the challenges that arise in each, Professor Wei provides a few simple solutions about how to overcome them. All the solutions, however, require moving beyond the comfort zone of conventional procedures.
In the slides attached Wei discusses:
A recent Cytel Seminar on Adaptive Statistical Designs featured a talk by Michael Elashoff (Patient Profiles) on Multivariate Approaches for Risk-Based Monitoring. Elashoff, a former statistical reviewer at the Food and Drug Administration, recommended combining cluster and rules based methods for statistical monitoring. Such adaptive monitoring approaches can substantially reduce the time and expense of data monitoring while ensuring consistently high data quality.
Pantelis Vlachos, PhD, is a Director at Cytel Consulting. He works with a team of experts who regularly assist clinical study teams with the design and implementation of Bayesian methods. In this blog post, Pantelis describes key benefits of Bayesian analyses for clinical trials.