Methods for studying treatment heterogeneity using large observational databases

使用大型观察数据库研究治疗异质性的方法

• 批准号: 9038278
• 负责人: Shirley Wang
• 金额: $ 24.9万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9038278
• 关键词: Methods; studying; treatment; heterogeneity; using

We propose a multi-phase program of research to develop, validate, apply and disseminate methods for estimating and communicating individual level heterogeneity in effectiveness and safety (for a range of patient/clinician identified outcomes of interest) for non-randomized comparative effectiveness research. The research program will translate theory to practice, and produce a template for clinical decision support tools that could be used to facilitate personalized medicine and informed decision making while addressing the personal characteristics, conditions and preferences of individual patients. Recent innovations in statistical methods for analyzing randomized clinical trials involve prediction of individual patient probabilities of experiencing benefit and/or harm. During the K99 phase of the award, I will work with my mentors and collaborators as we develop methods for non-randomized comparative effectiveness research which allow us to predict heterogeneous individual level treatment effects based on a constellation of patient characteristics. During this time, I will participate in the Postdoctoral Medical Informatics Research Training program at Harvard Medical School with a focus on training related to clinical and population health informatics. I will also be an active participant in education and training on topics such as patient/stakeholder engagement through the Brigham and Women's Hospital (BWH) Center for Patient Centered Comparative Effectiveness Research (PCERC). During the R00 phase of the award, I will solicit input from patient and physician members of the Patient and Family Advisory Councils (PFAC) at BWH PCERC regarding choices between alternative lipid lowering therapeutic strategies. I will work with them to identify safety and effectiveness outcomes that are priority concerns for the stakeholder groups they represent and understand what they expect from personalized treatment information. We will apply validated methods for estimating individual level treatment heterogeneity in studies using large, diverse healthcare databases. These studies will be designed to address the patient and provider identified priority questions. There will be continuous input from patients and providers as I work on developing clinical decision support tools designed to communicate relevant evidence, using metrics that healthcare consumers understand. I will use the skills developed through the Medical Informatics Training Program and collaborate with members of the Clinical Informatics Department at BWH to implement and test an evidence-based clinical decision support tool that is compatible with the hospital informatics infrastructure. This tool could be used during the office visit to help physicians and patients discuss the expected risks and benefits that are particular to each patient at a “critical moment”, during the office visit when they the treatment decision is being made.

我们提出了一个多阶段的研究计划，以开发，验证，应用和传播方法， 估计和传达有效性和安全性的个体水平异质性（对于一系列 患者/临床医生确定的感兴趣的结果）进行非随机比较有效性研究。的 研究计划将理论转化为实践，并为临床决策支持工具提供模板 可用于促进个性化医疗和知情决策，同时解决 患者的个人特征、状况和偏好。 随机临床试验统计分析方法的最新创新涉及个体预测 患者受益和/或伤害的概率。在K99阶段，我将与 我的导师和合作者，因为我们开发了非随机比较有效性研究的方法， 这使我们能够根据患者的星座来预测异质个体水平的治疗效果， 特色在此期间，我将参加博士后医学信息学研究培训 哈佛医学院的一个项目，重点是与临床和人口健康相关的培训 信息学.我还将积极参与有关患者/利益相关者等主题的教育和培训 通过布莱根妇女医院（BWH）以患者为中心的比较中心参与 有效性研究（PCERC）。在授予R 00阶段，我将征求患者的意见， BWH PCERC的患者和家庭咨询委员会（PFAC）的医生成员就选择 不同的降脂治疗策略。我将与他们一起确定安全性， 有效性结果是他们所代表和理解的利益相关者群体优先关注的问题 他们对个性化治疗信息的期望。我们将采用经过验证的方法来估算 使用大型、多样化医疗保健数据库的研究中个体水平的治疗异质性。这些研究将 旨在解决患者和提供者确定的优先问题。会有持续的输入 当我致力于开发临床决策支持工具时， 相关证据，使用医疗保健消费者理解的指标。我会用我在这里学到的技能 医学信息学培训计划，并与临床信息学部门的成员合作 在BWH实施和测试基于证据的临床决策支持工具，该工具与 医院信息化基础设施。该工具可在门诊期间使用，以帮助医生和患者 在“关键时刻”讨论每名患者特有的预期风险和受益， 当他们做出治疗决定时，他们会去办公室。