%0 Journal Article
%A Branko Miladinovic
%A Ambuj Kumar
%A Rahul Mhaskar
%A Benjamin Djulbegovic
%T Benchmarks for detecting ‘breakthroughs’ in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation
%D 2014
%R 10.1136/bmjopen-2014-005249
%J BMJ Open
%P e005249
%V 4
%N 10
%X 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.
%U https://bmjopen.bmj.com/content/bmjopen/4/10/e005249.full.pdf