Identifying optimal dynamic strategies for prostate cancer control

确定前列腺癌控制的最佳动态策略

• 批准号: 10671711
• 负责人: Barbra Anne Dickerman
• 金额: $ 19.09万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-11 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10671711
• 关键词: Acceleration; Area; Award; Big Data; Cancer Control; Classification; Clinical; Clinical Data; Communities; Comparative Effectiveness Research; Complex; Computer software; Data; Data Science; Databases; Detection; Development; Disease; Disease Progression; Documentation; Electronic Health Record; Environment; Epidemiology; Future; Genetic; Genetic Predisposition to Disease; Goals; Health; Indolent; Inherited; Intervention; Lead; Learning; Life Style; Link; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Mentors; Methods; Nonmetastatic; PSA screening; Patients; Persons; Phase; Positioning Attribute; Prevention; Prospective, cohort study; Recommendation; Recording of previous events; Research; Research Personnel; Screening Result; Shapes; Specific qualifier value; Statistical Methods; Time; Training; Treatment-related toxicity; Work; analytical method; analytical tool; anticancer research; cancer care; cancer prevention; career; career development; clinical care; clinical decision-making; comorbidity; cost effective; design; dietary; improved; innovation; interest; learning strategy; longitudinal database; men; multidisciplinary; novel; open source; personalized medicine; prevent; programs; prostate cancer prevention; randomized trial; screening; tool; trial design; tumor; tumor progression; user-friendly

PROJECT SUMMARY/ABSTRACT Big Data has the potential to revolutionize cancer research and care, but extracting the information it holds on the optimal strategies for cancer control will require cutting-edge tools in data science. The optimal strategies for cancer control will be dynamic strategies that adapt clinical decisions over time to a patient’s evolving clinical history. Unfortunately, conventional statistical methods cannot appropriately compare dynamic strategies, so we need methods specifically designed for this task: g-methods. G-methods have helped to shape clinical care in many areas, but they have not been systematically applied to cancer research. Further, while g-methods let us validly estimate the effect of pre-specified strategies, these may not be the optimal strategies. My overarching goal is to apply and further develop analytic methods to learn the optimal strategies for cancer control from complex longitudinal data and generate user-friendly, publicly-available software to make these methods available to the cancer research community. I will apply these methods to answer key clinical questions across the prostate cancer control continuum: 1) the optimal dietary and lifestyle strategies to prevent aggressive prostate cancer, 2) the optimal screening strategy following a baseline PSA test to maximize detection of aggressive disease while minimizing detection of indolent tumors, and 3) the optimal statin therapy strategy to maximize survival among men with nonmetastatic prostate cancer. This project will leverage data from a large prospective cohort study and a novel platform of electronic health records linked with genetic data. I will first apply g-methods to estimate the effects of recommended strategies for cancer control that a randomized trial would have limited feasibility to evaluate. I will then investigate whether novel methods that learn the optimal strategies from the data may lead to improved, targeted recommendations that get the right interventions to the right people at the right time. This innovative project will advance comparative effectiveness research for cancer care at the cutting edge of data science. I am optimally positioned to undertake this research based on my 1) expertise in cancer, epidemiology, and causal inference; 2) exceptional multidisciplinary mentoring team comprised of global leaders in their respective fields; and 3) unparalleled research environment to support my career development. Through this work, I will expand my expertise in new areas, including machine learning. The proposed research and training will help me achieve my long-term career goal to become an independent investigator and lead a transdisciplinary research program that integrates causal inference and machine learning to identify optimal strategies for cancer control. Leveraging rich, existing data, this proposal represents a significant opportunity to develop, apply, and disseminate powerful methods for big clinical data to accelerate progress in cancer research and care.

项目摘要/摘要 大数据有可能给癌症研究和治疗带来革命性的变化，但提取它持有的信息 癌症控制的最佳策略将需要数据科学中的尖端工具。最优策略 因为癌症控制将是动态的策略，使临床决策随着时间的推移而适应患者的演变 临床病史。不幸的是，传统的统计方法不能恰当地比较动态 战略，所以我们需要专门为这项任务设计的方法：G-方法。G-方法帮助我们 在许多领域形成了临床护理，但它们还没有系统地应用于癌症研究。此外， 虽然g-方法可以让我们有效地估计预先指定的策略的效果，但这些可能不是最优的。 战略。我的首要目标是应用并进一步发展分析方法来学习最优策略。 用于从复杂的纵向数据进行癌症控制，并生成用户友好的、公开可用的软件 将这些方法提供给癌症研究社区。 我将应用这些方法来回答前列腺癌控制过程中的关键临床问题：1) 预防侵袭性前列腺癌的最佳饮食和生活方式策略，2)最佳筛查策略 遵循基线PSA测试，以最大限度地检测侵袭性疾病，同时最大限度地减少对 惰性肿瘤，以及3)在非转移性男性中最大化生存的最佳他汀类药物治疗策略 前列腺癌。该项目将利用来自大型前瞻性队列研究的数据和一个新的平台 与基因数据相关联的电子健康记录。我将首先应用g-方法来估计 推荐的癌症控制策略，随机试验评估的可行性有限。我 然后将调查从数据中学习最佳策略的新方法是否会导致 改进的、有针对性的建议，在正确的时间向正确的人提供正确的干预措施。 这一创新项目将推进癌症护理的比较有效性研究， 数据科学。基于我在癌症方面的专业知识，我处于进行这项研究的最佳位置。 流行病学和因果推断；2)由全球 各自领域的领军人物；3)无与伦比的研究环境，支持我的职业发展。 通过这项工作，我将扩展我在新领域的专业知识，包括机器学习。建议数 研究和培训将帮助我实现成为一名独立调查员的长期职业目标 并领导一个整合了因果推理和机器学习的跨学科研究计划，以确定 癌症控制的最佳策略。利用丰富的现有数据，这项建议代表了一个重要的 有机会开发、应用和传播针对大数据的强大方法，以加快 癌症研究和护理。