Testing for a Sweet Spot in Randomized Trials

Donald A. Redelmeier, Deva Thiruchelvam and Robert J. Tibshirani
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Donald A. Redelmeier: Department of Medicine, University of Toronto, Toronto, ON, Canada
Deva Thiruchelvam: Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
Robert J. Tibshirani: Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA

Medical Decision Making, 2022, vol. 42, issue 2, 208-216

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

Keywords: randomized trial; precision medicine; personalized medicine; disease severity; treatment responsiveness; Gompertz function (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:42:y:2022:i:2:p:208-216

DOI: 10.1177/0272989X211025525

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