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P-hacking in clinical trials and how incentives shape the distribution of results across phases

Author

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  • Jérôme Adda

    (Department of Economics, Bocconi University, 20136 Milan, Italy; Bocconi Institute for Data Science and Analytics, Bocconi University, 20136 Milan, Italy; Innocenzo Gasparini Institute for Economic Research, Bocconi University, 20136 Milan, Italy)

  • Christian Decker

    (Department of Economics, University of Zurich, 8001 Zurich, Switzerland; UBS Center for Economics in Society, University of Zurich, 8001 Zurich, Switzerland)

  • Marco Ottaviani

    (Department of Economics, Bocconi University, 20136 Milan, Italy; Bocconi Institute for Data Science and Analytics, Bocconi University, 20136 Milan, Italy; Innocenzo Gasparini Institute for Economic Research, Bocconi University, 20136 Milan, Italy)

Abstract

Clinical research should conform to high standards of ethical and scientific integrity, given that human lives are at stake. However, economic incentives can generate conflicts of interest for investigators, who may be inclined to withhold unfavorable results or even tamper with data in order to achieve desired outcomes. To shed light on the integrity of clinical trial results, this paper systematically analyzes the distribution of P values of primary outcomes for phase II and phase III drug trials reported to the ClinicalTrials.gov registry. First, we detect no bunching of results just above the classical 5% threshold for statistical significance. Second, a density-discontinuity test reveals an upward jump at the 5% threshold for phase III results by small industry sponsors. Third, we document a larger fraction of significant results in phase III compared to phase II. Linking trials across phases, we find that early favorable results increase the likelihood of continuing into the next phase. Once we take into account this selective continuation, we can explain almost completely the excess of significant results in phase III for trials conducted by large industry sponsors. For small industry sponsors, instead, part of the excess remains unexplained.

Suggested Citation

  • Jérôme Adda & Christian Decker & Marco Ottaviani, 2020. "P-hacking in clinical trials and how incentives shape the distribution of results across phases," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(24), pages 13386-13392, June.
    • Handle: RePEc:nas:journl:v:117:y:2020:p:13386-13392
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    Cited by:

    1. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    2. Emeric Henry & Marco Loseto & Marco Ottaviani, 2022. "Regulation with Experimentation: Ex Ante Approval, Ex Post Withdrawal, and Liability," Management Science, INFORMS, vol. 68(7), pages 5330-5347, July.
    3. Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2022. "The Power of Tests for Detecting $p$-Hacking," Papers 2205.07950, arXiv.org, revised Jun 2023.
    4. Abel Brodeur & Nikolai Cook & Carina Neisser, 2022. "P-Hacking, Data Type and Data-Sharing Policy," ECONtribute Discussion Papers Series 200, University of Bonn and University of Cologne, Germany.

    More about this item

    Keywords

    clinical trials; drug development; selective reporting; p-hacking; economic incentives in research;
    All these keywords.

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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