• Open Access

Random Treatment Assignment Using Mathematical Equipoise for Comparative Effectiveness Trials

Authors

  • Harry P. Selker M.D., M.S.P.H.,

    1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA.
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  • Robin Ruthazer M.P.H.,

    1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA.
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  • Norma Terrin Ph.D.,

    1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA.
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  • John L. Griffith Ph.D.,

    1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA.
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  • Thomas Concannon M.A., Ph.D.,

    1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA.
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  • David M. Kent M.D., M.S.

    1. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA.
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HP Selker (hselker@tuftsmedicalcenter.org)

Abstract

In controlled clinical trials, random assignment of treatment is appropriate only when there is equipoise, that is, no clear preference among treatment options. However, even when equipoise appears absent because prior trials show, on average, one treatment yields superior outcomes, random assignment still may be appropriate for some patients and circumstances. In such cases, enrollment into trials may be assisted by real-time patient-specific predictions of treatment outcomes, to determine whether there is equipoise to justify randomization. The percutaneous coronary intervention thrombolytic predictive instrument (PCI–TPI) computes probabilities of 30-day mortality for patients having ST elevation myocardial infarction (STEMI), if treated with thrombolytic therapy (TT), and if treated with PCI. We estimated uncertainty around differences in their respective predicted benefits using the estimated uncertainty of the model coefficients. Using the 2,781-patient PCI–TPI development dataset, we evaluated the distribution of predicted benefits for each patient. For three typical clinical situations, randomization was potentially warranted for 70%, 93%, and 80% of patients. Predictive models may allow real-time patient-specific determination of whether there is equipoise that justifies trial enrollment for a given patient. This approach may have utility for comparative effectiveness trials and for application of trial results to clinical practice. Clin Trans Sci 2011; Volume 4: 10–16

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