Literature DB >> 15737096

Two-dimensional dose finding in discrete dose space.

Kai Wang1, Anastasia Ivanova.   

Abstract

The objective of a Phase I trial with two agents is to find a set of maximum-tolerated dose combinations that yield a prespecified toxicity rate. In this article, we consider the case where several doses of one agent are fixed and the goal is to find the maximum-tolerated dose of the other agent to be used in combination with each of the doses of agent one. We propose a Bayesian design that uses a parsimonious working model for the dose-toxicity relationship. We show that the new design is more effective in identifying the maximum-tolerated combinations than one-dimensional designs applied at each dose level of one of the agents.

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Mesh:

Year:  2005        PMID: 15737096     DOI: 10.1111/j.0006-341X.2005.030540.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  37 in total

1.  Identifying a maximum tolerated contour in two-dimensional dose finding.

Authors:  Nolan A Wages
Journal:  Stat Med       Date:  2016-02-22       Impact factor: 2.373

2.  A comparative study of adaptive dose-finding designs for phase I oncology trials of combination therapies.

Authors:  Akihiro Hirakawa; Nolan A Wages; Hiroyuki Sato; Shigeyuki Matsui
Journal:  Stat Med       Date:  2015-05-13       Impact factor: 2.373

3.  A Bayesian Dose-finding Design for Drug Combination Trials with Delayed Toxicities.

Authors:  Suyu Liu; Jing Ning
Journal:  Bayesian Anal       Date:  2013-09-09       Impact factor: 3.728

4.  BAYESIAN PHASE I/II ADAPTIVELY RANDOMIZED ONCOLOGY TRIALS WITH COMBINED DRUGS.

Authors:  Ying Yuan; Guosheng Yin
Journal:  Ann Appl Stat       Date:  2011-01-01       Impact factor: 2.083

5.  A practical Bayesian design to identify the maximum tolerated dose contour for drug combination trials.

Authors:  Liangcai Zhang; Ying Yuan
Journal:  Stat Med       Date:  2016-08-31       Impact factor: 2.373

6.  Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology.

Authors:  Philippe B Pierrillas; Sylvain Fouliard; Marylore Chenel; Andrew C Hooker; Lena E Friberg; Mats O Karlsson
Journal:  AAPS J       Date:  2018-03-07       Impact factor: 4.009

7.  CRM2DIM: A SAS macro for implementing the dual-agent Bayesian continual reassessment method.

Authors:  Mohamed Amine Bayar; Anastasia Ivanova; Gwénaël Le Teuff
Journal:  Comput Methods Programs Biomed       Date:  2019-05-06       Impact factor: 5.428

8.  AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual-agent dose finding trials.

Authors:  Jiaying Lyu; Yuan Ji; Naiqing Zhao; Daniel V T Catenacci
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2018-06-13       Impact factor: 1.864

9.  Dose Finding for Drug Combination in Early Cancer Phase I Trials using Conditional Continual Reassessment Method.

Authors:  Márcio Augusto Diniz; Mourad Tighiouart
Journal:  J Biom Biostat       Date:  2017-11-27

10.  A Generalized Continual Reassessment Method for Two-Agent Phase I Trials.

Authors:  Thomas M Braun; Nan Jia
Journal:  Stat Biopharm Res       Date:  2013-01-01       Impact factor: 1.452

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