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Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal

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  • Finkelstein, M.O.
  • Levin, B.
  • Robbins, H.

Abstract

Objectives. The accepted sine qua non for estimating the difference in efficacy between a new and a standard treatment is a randomized controlled clinical trial. Yet in some situations it is either practically or ethically impossible to conduct such a trial. For example, patients who are desperately ill may decline to participate when they learn they may not receive the new treatment, especially when that treatment is readily available outside the experimental protocol. Likewise, in a prophylactic trial of a promising vaccine, recruitment of persons at greater risk may falter or fail. Our objective is to demonstrate that a rigorous comparison of treatments may still be attainable. Methods. The features of a controlled clinical or prophylactic trial are reviewed from the perspectives of Food and Drug Administration regulations, ethical considerations, and practical problems. Results. An explicit risk-based allocation method of design and analysis is proposed, one guaranteeing that all subjects at greater risk will receive the new treatment. Conclusions. Under certain conditions, a risk-based allocation trial can furnish consistent estimates of both standard and experimental treatment effects for those at greater risk while avoiding certain difficulties caused by randomized treatment allocation.

Suggested Citation

  • Finkelstein, M.O. & Levin, B. & Robbins, H., 1996. "Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal," American Journal of Public Health, American Public Health Association, vol. 86(5), pages 691-695.
  • Handle: RePEc:aph:ajpbhl:1996:86:5:691-695_0
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    Cited by:

    1. Vivian C. Wong & Peter M. Steiner & Thomas D. Cook, 2013. "Analyzing Regression-Discontinuity Designs With Multiple Assignment Variables," Journal of Educational and Behavioral Statistics, , vol. 38(2), pages 107-141, April.
    2. David M. Kent & A. Mark Fendrick & Kenneth M. Langa, 2004. "New and Dis-Improved: On the Evaluation and Use of Less Effective, Less Expensive Medical Interventions," Medical Decision Making, , vol. 24(3), pages 281-286, June.
    3. Geneletti, Sara & O'Keeffe, Aidan G. & Sharples, Linda D. & Richardson, Sylvia & Baio, Gianluca, 2015. "Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data," LSE Research Online Documents on Economics 65600, London School of Economics and Political Science, LSE Library.
    4. Cook, Thomas D., 2008. ""Waiting for Life to Arrive": A history of the regression-discontinuity design in Psychology, Statistics and Economics," Journal of Econometrics, Elsevier, vol. 142(2), pages 636-654, February.
    5. Markus Frölich & Blaise Melly, 2013. "Identification of Treatment Effects on the Treated with One-Sided Non-Compliance," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 384-414, November.

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