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      Estimation of clinical trial success rates and related parameters

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          SUMMARY

          Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

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          Trends in clinical success rates.

          Katarzyna Smietana, Marcin Siatkowski, Martin N. Møller (2016)
          Article 0 comments Cited 86 times – based on 0 reviews     Review now
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            Productivity in pharmaceutical-biotechnology R&D: the role of experience and alliances.

            Paulo Pereira, Patricia Danzon, Sean Nicholson (2005)
            Using data on over 900 firms for the period 1988-2000, we estimate the effect on phase-specific biotech and pharmaceutical R&D success rates of a firm's overall experience, its experience in the relevant therapeutic category, the diversification of its experience across categories, the industry's experience in the category, and alliances with large and small firms. We find that success probabilities vary substantially across therapeutic categories and are negatively correlated with mean sales by category, which is consistent with a model of dynamic, competitive entry. Returns to experience are statistically significant but economically small for the relatively straightforward phase 1 trials. We find evidence of large, positive and diminishing returns to a firm's overall experience (across all therapeutic categories) for the larger and more complex late-stage trials that focus on a drug's efficacy. There is some evidence that a drug is more likely to complete phase 3 if developed by firms whose experience is focused rather than broad (diseconomies of scope). There is evidence of positive knowledge spillovers across firms for phase 1. However, for phase 2 and phase 3 the estimated effects of industry-wide experience are negative, which may reflect either higher Food and Drug Administration (FDA) approval standards in crowded therapeutic categories or that firms in such categories must pursue more difficult targets. Products developed in an alliance tend to have a higher probability of success, at least for the more complex phase 2 and phase 3 trials, and particularly if the licensee is a large firm.
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              Trial watch: Clinical trial cycle times continue to increase despite industry efforts

              Linda Martin, Melissa Hutchens, Conrad Hawkins (2017)
              One key issue facing pharmaceutical clinical development organizations has been increasing clinical trial cycle times. Despite substantial effort and attention from the industry on this issue, overall development timelines continue to increase, at both the programme and study levels. Indeed, cycle time continues to be
              Article 0 comments Cited 14 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Biostatistics
                Journal ID (iso-abbrev): Biostatistics
                Journal ID (publisher-id): biosts
                Title: Biostatistics (Oxford, England)
                Publisher: Oxford University Press
                ISSN (Print): 1465-4644
                ISSN (Electronic): 1468-4357
                Publication date (Print): April 2019
                Publication date (Electronic): 31 January 2018
                Publication date PMC-release: 31 January 2018
                Volume: 20
                Issue: 2
                Pages: 273-286
                Affiliations
                [1 ] MIT Computer Science and Artificial Intelligence Laboratory & Department of Electrical Engineering and Computer Science, Cambridge, MA 02139, USA and MIT Sloan School of Management and Laboratory for Financial Engineering, Cambridge, MA 02142, USA
                [2 ] MIT Computer Science and Artificial Intelligence Laboratory & Department of Electrical Engineering and Computer Science, Cambridge, MA 02139, USA, MIT Sloan School of Management and Laboratory for Financial Engineering, Cambridge, MA 02142, USA, and AlphaSimplex Group, LLC, Cambridge, MA 02142, USA
                Author notes
                To whom correspondence should be addressed. alo-admin@ 123456mit.edu
                Article
                Publisher ID: kxx069
                DOI: 10.1093/biostatistics/kxx069
                PMC ID: 6409418
                PubMed ID: 29394327
                SO-VID: 5ef44017-efa0-4d9c-93a3-d57262c87230
                Copyright © © The Author 2018. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 16 March 2017
                Date revision requested : 18 November 2017
                Date accepted : 26 November 2017
                Page count
                Pages: 14
                Funding
                Funded by: MIT Laboratory for Financial Engineering
                Categories
                Subject: Articles

                ScienceOpen disciplines: Biostatistics
                Keywords: clinical phase transition probabilities,clinical trial statistics,probabilities of success
                Data availability:
                ScienceOpen disciplines: Biostatistics
                Keywords: clinical phase transition probabilities, clinical trial statistics, probabilities of success

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