title: 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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
authors.affiliation: 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.
authors.forename: Branko
authors.initials: B
authors.lastname: Miladinovic
authors.affiliation: 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.
authors.forename: Ambuj
authors.initials: A
authors.lastname: Kumar
authors.affiliation: 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.
authors.forename: Rahul
authors.initials: R
authors.lastname: Mhaskar
authors.affiliation: 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.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Djulbegovic
mesh_terms: D019985:Benchmarking; D002986:Clinical Trials as Topic; D006801:Humans; D009369:Neoplasms / Q000628:therapy*; D011336:Probability; D013223:Statistics as Topic; D016896:Treatment Outcome
keywords: BIOTECHNOLOGY & BIOINFORMATICS; EPIDEMIOLOGY; STATISTICS & RESEARCH METHODS