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

Authors

  • Branko Miladinovic 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 PubMed articlesGoogle scholar articles
  • Ambuj Kumar Division and Center for Evidence-Based Medicine and Outcomes Research, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USADepartment of Hematology and Health Outcomes Research, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA PubMed articlesGoogle scholar articles
  • Rahul Mhaskar 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 PubMed articlesGoogle scholar articles
  • Benjamin Djulbegovic Division and Center for Evidence-Based Medicine and Outcomes Research, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USADepartment of Hematology and Health Outcomes Research, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Branko Miladinovic; bmiladin{at}health.usf.edu
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Citation

Miladinovic B, Kumar A, Mhaskar R, et al
Benchmarks for detecting ‘breakthroughs’ in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation

Publication history

  • Received March 12, 2014
  • Revised September 16, 2014
  • Accepted September 25, 2014
  • First published October 21, 2014.
Online issue publication 
October 21, 2014
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