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. 2014 Feb 17:15:61.
doi: 10.1186/1745-6215-15-61.

An investigation of the impact of futility analysis in publicly funded trials

Benjamin G O Sully, Steven A Julious  1 , Jon Nicholl
Affiliations

An investigation of the impact of futility analysis in publicly funded trials

Benjamin G O Sully et al. Trials. .

Abstract

Background: Publicly funded trials regularly fail to recruit their target sample size or find a significant positive result. Adaptive clinical trials which may partly mediate against the problems are not often applied. In this paper we investigate the potential of a form of adaption in a clinical trial - a futility analysis - to see if it has potential to improve publicly funded trials.

Methods: Outcome data from trials funded by two UK bodies, the Health Technology Assessment (HTA) programme and the UK Medical Research Council (MRC), were collected. These data were then used to simulate each trial with a single futility analysis using conditional power, undertaken after 50% to 90% of the patients had been recruited. Thirty-three trials recruiting between 2002 and 2008 met the inclusion criteria. Stopping boundaries of conditional powers of 20%, 30% and 40% were considered and outcomes included the number of trials successfully stopped and number of patients saved.

Results: Inclusion of a futility analysis after 75% of the patients had been recruited would have potentially resulted in 10 trials, which went on to have negative results, correctly stopping for futility using a stopping boundary of 30%. A total of 807 patients across all the trials would potentially have been saved using these futility parameters. The proportion of studies successfully recruiting would also have increased from 45% to 64%.

Conclusions: A futility assessment has the potential to increase efficiency, save patients and decrease costs in publicly funded trials. While there are logistical issues in undertaking futility assessments we recommend that investigators should aim to include a futility analysis in their trial design wherever possible.

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Figures

Figure 1
Figure 1
Flow of trials through the study.
Figure 2
Figure 2
Conditional power of each trial as simulated patients are recruited. Black lines indicate 30% and 40% conditional power boundaries.
See this image and copyright information in PMC

References

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