A Translational Framework for Methodological Rigor to Improve Patient Centered Ou

项目摘要：医疗管理数据库的创建提供了前所未有的 有机会评估治疗策略的临床有效性，以及治疗的质量和效率。 在大规模和异质人群中的医疗保健提供系统。减少健康的必要性 目前，对不平等和评估保健提供系统的需求从未如此之大。这些评估必须在 以最高的方法学严谨性，最好的数据，必须让患者参与， 成为我们医疗系统的一部分。 我是哈佛公共管理学院的生物统计学教授和信息技术副院长 健康（HSPH）。1999 - 2009年，我在约翰霍普金斯大学工作期间， 空气污染和健康大型数据库的分析方法（见项目2）。我已经积累了经验 通过对医疗保险数据的分析，以及它们与其他数据源（如美国航空）的地理和时间联系， 污染、天气和社会经济状况。在我职业生涯的这一阶段（从博士后研究员到 教授），我已经开发了用于分析这些大数据的统计方法（例如， 测量和未测量混杂因素的调整，贝叶斯分层模型，因果推断 方法和缺失数据方法）。2009年，我被聘为系主任。 哈佛公共卫生学院的生物统计学。在我上任的头两年里，我一直在 在行政管理（信息技术副院长），导师（共同， NIEHS资助的环境生物统计学培训补助金的主要研究者）和癌症研究的主要研究者（NIEHS资助的 重新提交NCI P01癌症信息学）。我已经开始与同事开展新的合作 在Dana Farber癌症研究所（DFCI）（Schrag，亚历山大，布洛克博士）和医疗保健政策（HCP）， 哈佛医学院（HMS）（Normand博士）。 由于在一个新的环境和新的同事，我开始意识到，我的专业知识， 医疗保险数据的分析，在因果推理方法的发展，以评估健康福利的 环境干预措施可以很容易地运输和加强，以解决关键的CER问题， 重要性特别是，由于个人原因，也由于围绕着 随着医疗保健改革的深入，我越来越有兴趣更好地了解索赔数据的使用如何能够 解决有关如何为晚期癌症患者提供最佳护理的问题。事实上，对于这些 如何提供最好的护理，实现最好的结果，同时， 成本非常具有挑战性。这方面的知识差距很大， 在存在几个伴随因素的情况下提供护理（例如，多种治疗， 医疗团队、姑息治疗）不能仅通过随机临床试验来解决。的联动和 分析医疗保健管理数据库（如医疗保险），其中我已经开发的专业知识， 环境科学家，提供了前所未有的机会，以评估临床有效性 治疗策略，以及癌症研究中卫生保健提供系统的质量和效率。 我正在寻求这个K18：1）提高我的能力，以追求未来的研究，以病人为中心的结果， 癌症患者的生命末期（EOL）护理; 2）指导越来越希望获得 参与CER; 3）更有效地与临床研究者和利益相关者合作; 4）进一步 开发统计方法，以便这些复杂的CER问题可以用最高的方法来解决 僵硬具体目标是：1）参加一个激烈的，指导性的职业发展经验， 比较有效性（CER），特别关注癌症（见项目4和指导计划）。的 建议的培训将直接针对我的研究计划（项目11）中详细说明的具体目标; 2)开展一个研究项目，重点是解决如何最好地为油井提供护理的关键挑战 定义人群：诊断为胶质母细胞瘤的老年人。这将是迄今为止研究的最大种群 （N = 24，142，A部分Medicare数据，N = 9，343 SEER-Medicare，N = 9，320，Medicaid-肿瘤登记，N = GBM cases）.该研究计划将是一个工具，体验，第一手，如何提供一个坚实的证据基础， 影响政策和改善老年晚期癌症患者的医疗保健体验（项目11）; 3） 利用当地丰富的癌症CER和EOL专业知识，利用著名科学的监督 利益相关者咨询委员会。该委员会包括卫生保健政策、CER、肿瘤学、健康 差异、姑息治疗、神经肿瘤学和医疗报销决策。审计委员会还 包括两名患者倡导者和普利策奖获奖记者专家在EOL（戴安娜萨格）（项目4）。 该项目的成功完成将提供能力建设（数据和方法）， 数据和方法方面的差距，并解决在提供保健服务方面至关重要的问题， 迄今为止研究的最大老年GBM患者人群的健康差异。数据、方法和 研究结果也将推动CER在其他癌症人群中的研究。该职业发展奖还将 为我提供了多学科的专业知识和富有成效的合作，培训下一个必要的 变革和组织振兴方案CER中的一代科学家。

Project summary: The creation of healthcare administrative databases provides the unprecedented opportunity to evaluate the clinical effectiveness of treatments strategies, and the quality and efficiency of health care delivery systems in large and heterogeneous populations. The imperatives to reduce health disparities and to evaluate health care delivery systems have never been greater. These assessments must be done with the highest methodological rigor, with the best possible data, must engage patients, and must become a routine part of our health system. I am a Professor of Biostatistics and Associate Dean of Information Technology at the Harvard School of Public Health (HSPH). During my career at Johns Hopkins University, 1999-2009, I have developed statistical methods for the analysis of large databases on air pollution and health (see item 2). I have gained experience with the analysis of Medicare data and their linkage by geography and time to other data sources, such us air pollution, weather, and socioeconomic status. During this phase of my career (from post-doctoral fellow to Professor), I have developed statistical methods for the analysis of these large data (e.g. methods for the adjustment of measured and unmeasured confounders, Bayesian hierarchical models, causal inference methods, and missing data methods.) In 2009 I was recruited as a Full Professor in the Department of Biostatistics at Harvard School of Public Health. Within the first two years of my appointment, I have been awarded leadership positions in administration (Associated Dean of Information Technology), mentorship (co- PI of a NIEHS funded training grant in Environmental Biostatistics) and in cancer research (PI of a resubmission of NCI P01 on Cancer Informatics). I have started to develop new collaborations with colleagues at the Dana Farber Cancer Institute (DFCI) (Drs. Schrag, Alexander, Block) and Health Care Policy (HCP) at Harvard Medical School (HMS) (Dr. Normand). As a result of being in a new environment and with new colleagues I started to realize that my expertise in the analysis of Medicare data, in development of causal inference methods for the assessment of health benefits of environmental interventions could be easily transportable and enhanced to address questions in CER of critical importance. In particular, both for personal reasons and because of the growing discussions surrounding the health care reform, I became increasingly interested in better understanding how the use of claims data can address questions regarding ways to best deliver care for patients that have a terminal cancer. In fact, for these populations how to deliver the best care, achieve the best outcomes, and at the same time, containing medical costs is very challenging. The gaps of knowledge in this area are enormous and questions regarding best ways of deliver care in presence of several concomitant factors (e.g. multiple treatments, coordination among medical teams, palliative care) cannot be addressed with randomized clinical trials, only. The linkage and analysis of healthcare administrative databases (e.g. Medicare), of which I have developed expertise as environmental scientist, provides the unprecedented opportunity to evaluate the clinical effectiveness of treatments strategies, and the quality and efficiency of health care delivery systems in cancer research. I am seeking this K18 to: 1) improve my ability to pursue future research in patient centered outcomes in end of life (EOL) care for cancer patients; 2) mentor the junior biostatisticians that increasingly want to get involved in CER; 3) more effectively collaborate with clinical investigators and stakeholders and 4) further develop statistical methods so these complex CER questions can be addressed with the highest methodological rigor. The specific aims are: 1) Participate in an intense, mentored career development experience in comparative effectiveness (CER) with a special focus on cancer (see Item 4 and mentoring plan). The proposed training will be directly targeted to address the specific aims detailed in my research plan (Item 11); 2) Conduct a research project focused on addressing key challenges on how to best deliver care for a well defined population: elderly diagnosed with Glioblastoma. These will be the largest populations studied to date (N=24,142, Part A Medicare data, N=9,343 SEER-Medicare, and N=9,320, Medicaid-Tumor Registry, N=GBM cases). The research plan will be a vehicle to experience, first hand, how to provide a solid evidence base to impact policy and to improve health care experience of the elderly with an advanced cancer (Item 11); 3) Leverage the local wealth of expertise in CER in cancer and EOL using the oversight of a prestigious science and stakeholder advisory board. The board includes experts in health care policy, CER, oncology, health disparities, palliative care, neuro-oncology, and decision making for health care reimbursement. The board also includes two patient advocates and a Pulitzer Prize-winning journalist expert in EOL (Diana Sugg) (Item 4). The successful completion of this project will provide capacity building (data and methods), identify important data and methodological gaps, and address questions of paramount importance in health care delivery and health disparities for the largest population of elderly GBM patients studied to date. Data, methods, and results will also advance CER research in other cancer populations. This career development award will also provide me with multidisciplinary expertise and fruitful collaborations necessary for training the next generation of scientists in CER for PCOR.