A Bayesian method to estimate the optimal threshold of a marker used to select patients' treatment. (January 2020)

• Record Type: Journal Article
• Authors: Blangero, Yoann; Rabilloud, Muriel; Ecochard, René; Subtil, Fabien
• Is Part Of: Statistical methods in medical research. Volume 29:Number 1(2020)
• Page Start: 29
• Page End: 43
• Publication Date: 2020-01
• Subjects: Biomarker -- threshold -- expected utility -- treatment selection -- Bayesian -- predictive marker; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218821394
• Languages: English
• ISSNs: 0962-2802

Abstract

The use of a quantitative treatment selection marker to choose between two treatment options requires the estimate of an optimal threshold above which one of these two treatments is preferred. Herein, the optimal threshold expression is based on the definition of a utility function which aims to quantify the expected utility of the population (e.g. life expectancy, quality of life) by taking into account both efficacy (success or failure) and toxicity of each treatment option. Therefore, the optimal threshold is the marker value that maximizes the expected utility of the population. A method modelling the marker distribution in patient subgroups defined by the received treatment and the outcome is proposed to calculate the parameters of the utility function so as to estimate the optimal threshold and its 95% credible interval using the Bayesian inference. The simulation study found that the method had low bias and coverage probability close to 95% in multiple settings, but also the need of large sample size to estimate the optimal threshold in some settings. The method is then applied to the PETACC-8 trial that compares the efficacy of chemotherapy with a combined chemotherapy + anti-epidermal growth factor receptor in stage III colorectal cancer.