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BMJ Open. 2014 Oct 21;4(10):e005249. doi: 10.1136/bmjopen-2014-005249.

Benchmarks for detecting 'breakthroughs' in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation.

Author information

1
Division and Center for Evidence-Based Medicine and Outcomes Research, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA.
2
Division and Center for Evidence-Based Medicine and Outcomes Research, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA Department of Hematology and Health Outcomes Research, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

Abstract

OBJECTIVE:

To understand how often 'breakthroughs,' that is, treatments that significantly improve health outcomes, can be developed.

DESIGN:

We applied weighted adaptive kernel density estimation to construct the probability density function for observed treatment effects from five publicly funded cohorts and one privately funded group.

DATA SOURCES:

820 trials involving 1064 comparisons and enrolling 331,004 patients were conducted by five publicly funded cooperative groups. 40 cancer trials involving 50 comparisons and enrolling a total of 19,889 patients were conducted by GlaxoSmithKline.

RESULTS:

We calculated that the probability of detecting treatment with large effects is 10% (5-25%), and that the probability of detecting treatment with very large treatment effects is 2% (0.3-10%). Researchers themselves judged that they discovered a new, breakthrough intervention in 16% of trials.

CONCLUSIONS:

We propose these figures as the benchmarks against which future development of 'breakthrough' treatments should be measured.

KEYWORDS:

BIOTECHNOLOGY & BIOINFORMATICS; EPIDEMIOLOGY; STATISTICS & RESEARCH METHODS

PMID:
25335959
PMCID:
PMC4208055
DOI:
10.1136/bmjopen-2014-005249
[Indexed for MEDLINE]
Free PMC Article

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