AN ADAPTIVE BAYESIAN APPROACH TO JOINTLY MODELING RESPONSE AND TOXICITY IN PHAS

阶段性响应和毒性联合建模的自适应贝叶斯方法

• 批准号: 7601385
• 负责人: ROGER S. DAY
• 金额: $ 0.03万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7601385
• 关键词: Attention; Bayesian Method; Biological; Computer Retrieval of Information on Scientific Projects Database; Computer Simulation; Data; Decision Theory; Dose; Event; Funding; Grant; Individual; Institution; Malignant Neoplasms; Modeling; Normal Statistical Distribution; Outcome; Patients; Phase; Phase I Clinical Trials; Population; Probability; Research; Research Personnel; Resources; Source; Toxic effect; United States National Institutes of Health; base; design; response

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We present a new adaptive Bayesian method for dose-finding in phase I clinical trials based on both response and toxicity under the assumption that the thresholds of response and toxicity jointly follow a bivariate log-normal distribution. Responses are rare in cancer trials. But biological responses may be common, and may help decide how aggressive a phase I escalation should be. In an ideal decision theory framework, the choice of dose for each successive patient would incorporate what is best for the patient, together with the value of the information to be obtained for the trial. However, to evaluate the latter explicitly would be computationally extremely difficult. For simplicity, we restrict attention to the probability of each outcome for the next patient only. The model assumes that response and toxicity events happen depending on the respective dose thresholds for the individual, and provides a framework for incorporating prior information about the population threshold distribution, as well as accumulated data. The next dose can be assigned to maximize expected utility. A simple utility function places positive utility only on the co-occurrence of response and non-toxicity. Computer simulation results show that the proposed design reliably chooses the preferred dose under different scenarios and different priors.

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