Benchmarks for detecting 'breakthroughs' in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation. Issue 10 (21st October 2014) - Miladinovic, Branko; Kumar, Ambuj; Mhaskar, Rahul; Djulbegovic, Benjamin
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.