Methods for studying treatment heterogeneity using large observational databases

描述(由申请人提供)：我们提出了一个多阶段研究计划，以开发、验证、应用和传播用于非随机化比较有效性研究的评估和传达个体水平有效性和安全性异质性的方法(针对一系列患者/临床医生确定的感兴趣的结果)。该研究计划将把理论转化为实践，并为临床决策支持工具制作一个模板，该模板可用于促进个性化医疗和知情决策，同时解决个别患者的个人特征、状况和偏好。最近在分析随机临床试验的统计方法方面的创新包括预测个体患者经历益处和/或伤害的概率。在K99奖项阶段，我将与我的导师和合作者一起开发非随机化比较有效性研究的方法，使我们能够根据一系列患者特征预测不同个体水平的治疗效果。在此期间 这次，我将参加哈佛医学院的博士后医学信息学研究培训项目，重点是与临床和人口健康信息学相关的培训。我还将通过布里格姆妇女医院(BWH)以患者为中心的比较有效性研究中心(PCERC)积极参与有关患者/利益相关者参与等主题的教育和培训。在奖项的R00阶段，我将征求BWH PCERC患者和家庭咨询委员会(PFAC)的患者和医生成员对替代降脂治疗策略的选择提出意见。我将与他们合作，确定他们所代表的利益相关者群体优先关注的安全性和有效性结果，并了解他们对个性化治疗信息的期望。我们将在使用大型、多样化的医疗保健数据库的研究中应用经过验证的方法来估计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。在我致力于开发临床决策支持工具的过程中，患者和提供者将不断提供意见，这些工具旨在使用医疗保健消费者理解的指标来交流相关证据。我将使用通过医学信息学培训计划培养的技能，并与BWH临床信息学部门的成员合作，实施并测试一个与医院信息学基础设施兼容的循证临床决策支持工具。该工具可用于在就诊期间帮助医生和患者讨论每个患者在作出治疗决定的“关键时刻”所特有的预期风险和益处。

DESCRIPTION (provided by applicant): 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.