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Testing for a Sweet Spot in Randomized Trials

Author

Listed:
  • Donald A. Redelmeier

    (Department of Medicine, University of Toronto, Toronto, ON, Canada
    Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
    Institute for Clinical Evaluative Sciences
    Division of General Internal Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada)

  • Deva Thiruchelvam

    (Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
    Institute for Clinical Evaluative Sciences)

  • Robert J. Tibshirani

    (Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
    Department of Statistics, Stanford University, Stanford, CA, USA)

Abstract

Introduction Randomized trials recruit diverse patients, including some individuals who may be unresponsive to the treatment. Here we follow up on prior conceptual advances and introduce a specific method that does not rely on stratification analysis and that tests whether patients in the intermediate range of disease severity experience more relative benefit than patients at the extremes of disease severity (sweet spot). Methods We contrast linear models to sigmoidal models when describing associations between disease severity and accumulating treatment benefit. The Gompertz curve is highlighted as a specific sigmoidal curve along with the Akaike information criterion (AIC) as a measure of goodness of fit. This approach is then applied to a matched analysis of a published landmark randomized trial evaluating whether implantable defibrillators reduce overall mortality in cardiac patients ( n = 2,521). Results The linear model suggested a significant survival advantage across the spectrum of increasing disease severity (β = 0.0847, P

Suggested Citation

  • Donald A. Redelmeier & Deva Thiruchelvam & Robert J. Tibshirani, 2022. "Testing for a Sweet Spot in Randomized Trials," Medical Decision Making, , vol. 42(2), pages 208-216, February.
    • Handle: RePEc:sae:medema:v:42:y:2022:i:2:p:208-216
    DOI: 10.1177/0272989X211025525
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    References listed on IDEAS

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    1. James J. Bailey & Morrison Hodges & Timothy R. Church, 2007. "Decision to Implant a Cardioverter Defibrillator after Myocardial Infarction: The Role of Ejection Fraction v. Other Risk Factor Markers," Medical Decision Making, , vol. 27(2), pages 151-160, March.
    2. Anirban Basu & Josh J. Carlson & David L. Veenstra, 2016. "A Framework for Prioritizing Research Investments in Precision Medicine," Medical Decision Making, , vol. 36(5), pages 567-580, July.
    3. Abualbishr Alshreef & Nicholas Latimer & Paul Tappenden & Ruth Wong & Dyfrig Hughes & James Fotheringham & Simon Dixon, 2019. "Statistical Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Systematic Review of Method," Medical Decision Making, , vol. 39(8), pages 910-925, November.
    4. Elliott Tolbert & Michael Brundage & Elissa Bantug & Amanda L. Blackford & Katherine Smith & Claire Snyder, 2018. "Picture This: Presenting Longitudinal Patient-Reported Outcome Research Study Results to Patients," Medical Decision Making, , vol. 38(8), pages 994-1005, November.
    5. Regina Kwon & Larry A. Allen & Laura D. Scherer & Jocelyn S. Thompson & Madiha F. Abdel-Maksoud & Colleen K. McIlvennan & Daniel D. Matlock, 2018. "The Effect of Total Cost Information on Consumer Treatment Decisions: An Experimental Survey," Medical Decision Making, , vol. 38(5), pages 584-592, July.
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