Futility Monitoring in Clinical Trials
Ana M. Ortega‐Villa, Megan C. Grieco, Kevin Rubenstein, Jing Wang, Michael A. Proschan
TL;DR
This paper explains how to determine when a clinical trial is unlikely to succeed based on early data.
Contribution
The paper provides a tutorial on various statistical tools and concepts for evaluating futility in clinical trials.
Findings
The paper reviews conditional and predictive power as methods for futility assessment.
It introduces reverse conditional power and predicted interval plots as additional tools.
Beta spending functions are discussed for monitoring futility over time.
Abstract
At the beginning of a phase III clinical trial, there is great optimism. After all, the phase II trial results were encouraging. Then, early data from the phase III trial trend in the wrong way, but there is still an opportunity for the trend to reverse and become statistically significant by the end. At what point does optimism become denial of reality? How do we decide when a clinical trial is futile? What does futility even mean? This tutorial reviews different concepts and tools for evaluating futility, including conditional and predictive power, reverse conditional power, predicted interval plots, revised unconditional power, and beta spending functions.
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Reproductive Biology and Fertility · Ovarian function and disorders