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的研究项目，用于开发、验证、应用和传播评估和交流非随机比较有效性研究中有效性和安全性的个体水平异质性的方法（针对一系列患者/临床医生确定的感兴趣的结果）。该研究项目将理论转化为实践，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。最近在分析随机临床试验的统计方法方面的创新涉及预测个体患者经历获益和/或伤害的概率。在该奖项的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。在这

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.