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

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.

SUBMITTER: Miladinovic B 

PROVIDER: S-EPMC4208055 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature