Optimal Dose-Response Learning

最佳剂量反应学习

• 批准号: 1536717
• 负责人: Archis Ghate
• 金额: $ 28.79万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1536717&HistoricalAwards=false
• 关键词: Optimal; Dose; Response; Learning

Medical treatment for diseases such as rheumatoid arthritis, hepatitis C, and cancer often requires the administration of doses in multiple sessions. Higher doses achieve better disease-control but have a higher risk of side effects. Lower doses have lesser side effects but may lead to inadequate disease-control. Since each patient's response to treatment is uncertain, the need to effectively balance this trade-off pervades all of medicine. Consequently, within the field of personalized medicine, there has been a recent surge of interest in the idea of response-guided dosing. The goal is to administer the right dose to the right patient at the right time, based on the observed evolution of each patient's disease condition. To attain this goal, it is crucial to better-learn patients' dose- response as treatment progresses. Expert panels and government regulatory bodies have therefore called for analytical tools to facilitate such learning-while-doing. The research objective of this award is to develop a mathematically rigorous, theoretical and computational framework for optimal dose-response learning while treating a cohort of patients in clinical trials for response-guided dosing. Millions of patients in the U.S. suffer from diseases that require multiple-session treatments. Thus, if successful, the mathematical framework in this award has the potential for a considerable societal impact.More specifically, this project plans to use Bayesian stochastic dynamic programming formulations and approximate solution methods rooted in convex programming to facilitate response-learning and dosing decisions. The state in these models equals the cohort's disease conditions and decisions equal the doses administered. Disutility functions model the cohort's aversion to doses and to the disease conditions reached at the end of the trial. The decision-maker's prior belief is assumed to be conjugate to the dose-response parameter's distribution. The information state thus equals the prior's hyperparameters and updates via a simple formula. The decision-make's goal is to minimize the total expected disutility of the doses administered and of the disease conditions reached. Exact solution of this formulation is computationally intractable. Two approximate control schemes called semi-stochastic certainty equivalent control and certainty equivalent control are therefore planned. Structural properties such as monotonicity, stationarity, and separability of the resulting dosing policies will be analyzed and exploited for efficient solution. Variations such as optimal stopping problems, model selection problems, and problems with imperfect measurements will be studied. Clinical data on rheumatoid arthritis will be employed to calibrate the models, and to validate and compare the dosing policies derived via computer simulations.

类风湿性关节炎、丙型肝炎和癌症等疾病的药物治疗通常需要多次给药。更高的剂量可以更好地控制疾病，但副作用的风险也更高。较低剂量的副作用较小，但可能导致疾病控制不足。由于每个患者对治疗的反应是不确定的，因此需要有效地平衡这种权衡遍及所有医学。因此，在个性化医疗领域内，最近对响应引导给药的想法的兴趣激增。目标是根据观察到的每个患者的疾病状况的演变，在正确的时间向正确的患者给予正确的剂量。为了实现这一目标，随着治疗的进展，更好地了解患者的剂量反应至关重要。因此，专家小组和政府监管机构呼吁提供分析工具，以促进这种边干边学。该奖项的研究目标是开发一个数学上严格的，理论和计算框架，用于最佳剂量反应学习，同时在临床试验中治疗一组患者，以进行反应指导给药。在美国，数百万患者患有需要多次治疗的疾病。因此，如果成功的话，该奖项中的数学框架具有相当大的社会影响力。更具体地说，该项目计划使用贝叶斯随机动态规划公式和基于凸规划的近似解方法，以促进响应学习和剂量决策。这些模型中的状态等于队列的疾病状况，并且决策等于施用的剂量。负效用函数模拟队列对剂量和试验结束时达到的疾病状况的厌恶。假设决策者的先验信念与剂量反应参数的分布共轭。因此，信息状态等于先验的超参数，并通过一个简单的公式进行更新。决策者的目标是使给药剂量和达到的疾病状况的总预期负效用最小化。这个公式的精确解在计算上是难以处理的。因此，两个近似控制方案称为半随机确定性等效控制和确定性等效控制计划。结构特性，如单调性，平稳性，和分离性的给药策略将被分析和利用有效的解决方案。变化，如最佳停止问题，模型选择问题，并与不完美的测量问题将进行研究。类风湿性关节炎的临床数据将用于校准模型，并验证和比较通过计算机模拟得出的给药策略。