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J Probab Stat. 2018;2018. doi: 10.1155/2018/8654173. Epub 2018 Nov 01.

A Bayesian Adaptive Design in Cancer Phase I Trials using Dose Combinations in the Presence of a Baseline Covariate.

Journal of probability and statistics

Márcio Augusto Diniz, Sungjin Kim, Mourad Tighiouart

Affiliations

  1. Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center 8700 Beverly Blvd, Los Angeles, CA 90048.

PMID: 30906326 PMCID: PMC6428433 DOI: 10.1155/2018/8654173

Abstract

A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curves is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the

Keywords: Baseline covariate; Cancer Phase I trials; Continuous dose; Discrete dose; Dose limiting toxicity; Drug combination; Escalation with overdose control; Maximum tolerated dose

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