A trivariate continual reassessment method for phase I/II trials of toxicity, efficacy, and surrogate efficacy.
Recently, many Bayesian methods have been developed for dose finding when simultaneously modeling both toxicity and efficacy outcomes in a blended phase I/II fashion. A further challenge arises when all the true efficacy data cannot be obtained quickly after the treatment so that surrogate markers are instead used (e.g., in cancer trials). We propose a framework to jointly model the probabilities of toxicity, efficacy, and surrogate efficacy given a particular dose. Our trivariate binary model is specified as a composition of two bivariate binary submodels. In particular, we extend the bivariate continual reassessment method (CRM), as well as utilize a particular Gumbel copula. The resulting trivariate algorithm utilizes all the available data at any given time point and can flexibly stop the trial early for either toxicity or efficacy. Our simulation studies demonstrate that our proposed method can successfully improve dosage targeting efficiency and guard against excess toxicity over a variety of true model settings and degrees of surrogacy.
Division of Biostatistics, University of Minnesota, USA.
Wei
W
Zhong
Joseph S
JS
Koopmeiners
Bradley P
BP
Carlin
D000465:Algorithms; D000970:Antineoplastic Agents / Q000009:adverse effects / Q000627:therapeutic use; D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000706:statistics & numerical data*; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D008223:Lymphoma / Q000188:drug therapy; D020714:Maximum Tolerated Dose; D012107:Research Design / Q000706:statistics & numerical data*
D000970:Antineoplastic Agents