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Clinical and prophylactic trials with assured new treatment for those at greater risk: II. Examples

Author

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

Abstract

Objectives. The preceding article proposed an assured treatment design that would address certain diffculties in recruiting person who are at greater disk into randomized clinical trials. The purpose of this article is to illustrate the statistical validity of the design in a practical setting. Methods. Three actual randomized clinical trials were considered as case studies: in each, the data that would have been obtained under assured allocation were identified. Then, with only these data, together with a reasonable choice of model describing the response of subjects under standard treatment as a function of initial severity, the treatment effect was estimated for the subjects at greater risk. The estimates were compared with Conventional estimates for the sicker patients randomized in the original trials. Results. In each case, the estimates produced in the assured treatment trial were close to those observed in the randomized trial. Conclusions. Risk-based allocation trials deserve serious consideration when randomized clinical trials are difficult or impossible to rd The proposed designs and analyses would allow physicians to other persons at greater risk assurance that they would receive the new treatment, while researchers would retain the abaility to draw valid statistical conclusions about treatment efficacy.

Suggested Citation

  • Finkelstein, M.O. & Levin, B. & Robbins, H., 1996. "Clinical and prophylactic trials with assured new treatment for those at greater risk: II. Examples," American Journal of Public Health, American Public Health Association, vol. 86(5), pages 696-702.
  • Handle: RePEc:aph:ajpbhl:1996:86:5:696-702_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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