Methods for studying treatment heterogeneity using large observational databases

描述（由申请人提供）：我们提出了一项多阶段研究计划，旨在开发、验证、应用和传播用于估计和交流有效性和安全性（针对一系列患者/临床医生确定的感兴趣结局）个体水平异质性的方法，用于非随机比较有效性研究。该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，同时解决个体患者的个人特征，条件和偏好。随机临床试验统计分析方法的最新创新涉及预测个体患者受益和/或伤害的概率。在K99阶段，我将与我的导师和合作者一起开发非随机比较有效性研究的方法，使我们能够根据患者特征的星座预测异质个体水平的治疗效果。在此 在此期间，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。我还将积极参与教育和培训，例如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）进行患者/利益相关者参与。在该奖项的R 00阶段，我将征求BWH PCERC患者和家庭咨询委员会（PFAC）的患者和医生成员关于替代降脂治疗策略之间的选择的意见。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。我们将采用经验证的方法，在使用大型、多样化医疗保健数据库的研究中估计个体水平的治疗异质性。这些研究旨在解决患者和提供者确定的优先问题。在我开发临床决策支持工具的过程中，患者和医疗服务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传达相关证据。我将使用通过医疗信息学培训计划开发的技能，并与BWH临床信息学部门的成员合作，实施和测试与医院信息化基础设施兼容的循证临床决策支持工具。该工具可在诊室访视期间使用，以帮助医生和患者讨论预期的风险和受益，这些风险和受益在诊室访视期间做出治疗决定时的“关键时刻”对每位患者而言都是特定的。

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.