RT Journal Article
SR Electronic
T1 Benchmarks for detecting ‘breakthroughs’ in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation
JF BMJ Open
JO BMJ Open
FD British Medical Journal Publishing Group
SP e005249
DO 10.1136/bmjopen-2014-005249
VO 4
IS 10
A1 Branko Miladinovic
A1 Ambuj Kumar
A1 Rahul Mhaskar
A1 Benjamin Djulbegovic
YR 2014
UL http://bmjopen.bmj.com/content/4/10/e005249.abstract
AB 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.