Sufficient Sample Threshold for Effective Bayesian Applications
The Effective, Efficient and Ethical Way Forward
As clinical trial experts know all too well, it is very difficult to tell ahead of time exactly when there will be sufficient information to justify stopping a clinical trial.
One can make the case that whenever that minimum level of information has been reached, there are reasons to stop the trial. at that theoretical point, call it the Sufficient Information Threshold, all the information possibly relevant to the study will have been collected: information about the primary endpoint, any secondary endpoints, and safety data to construct a risk-benefit profile.
It could also include early phase information relevant to designing optimal late-phase studies. The Sufficient Information Threshold is therefore a conceptual point that can enable us to begin asking questions about the efficiency, ethical quality and effectiveness of a clinical study.
Read the paper to learn:
How the concept of the Sufficient Information Threshold can aid teams in determining whether their trial has accumulated sufficient data for their goals.
How clinical development teams can utilize a simple format to frame complex questions that can arise during the development cycle to ensure trials are efficient, effective, and ethical.
- How decision-makers can work with statisticians to utilize Bayesian predictive power and also conditional power, to see if clinical trials have achieved the Sufficient Information Threshold.
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Pantelis Vlachos, PhD
Vice President, Customer Success
Esha Senchaudhuri, PhD
Associate Director, Content Marketing
Kyle Wathen, PhD
Vice President, Scientific Strategy & Innovation
Yannis Jemiai, PhD
Chief Scientific Officer