A Bayesian dose-finding design for drug combination clinical trials based on the logistic model.
In early phase dose-finding cancer studies, the objective is to determine the maximum tolerated dose, defined as the highest dose with an acceptable dose-limiting toxicity rate. Finding this dose for drug-combination trials is complicated because of drug-drug interactions, and many trial designs have been proposed to address this issue. These designs rely on complicated statistical models that typically are not familiar to clinicians, and are rarely used in practice. The aim of this paper is to propose a Bayesian dose-finding design for drug combination trials based on standard logistic regression. Under the proposed design, we continuously update the posterior estimates of the model parameters to make the decisions of dose assignment and early stopping. Simulation studies show that the proposed design is competitive and outperforms some existing designs. We also extend our design to handle delayed toxicities.
INSERM, UMRS 1138, Equipe 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France; IRIS (Institut de Recherches Internationales Servier), Suresnes, France.
Marie-Karelle
MK
Riviere
Ying
Y
Yuan
Frédéric
F
Dubois
Sarah
S
Zohar
D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D054796:Drug Dosage Calculations; D004359:Drug Therapy, Combination; D006801:Humans; D016015:Logistic Models
Bayesian inference; dose finding; drug combination; oncology; phase I trial