A Translational Framework for Methodological Rigor to Improve Patient Centered Ou

描述(由申请人提供)：医疗保健管理数据库的创建提供了前所未有的机会来评估治疗策略的临床有效性，以及在大量和不同种类的人群中医疗保健提供系统的质量和效率。缩小健康差距和评估卫生保健提供系统的紧迫性从未像现在这样迫切。这些评估必须以最高的方法严谨进行，使用尽可能好的数据，必须让患者参与，并必须成为我们卫生系统的常规部分。我是哈佛大学公共卫生学院(HSPH)生物统计学教授和信息技术副院长。在我1999-2009年在约翰·霍普金斯大学的职业生涯中，我开发了分析空气污染与健康的大型数据库的统计方法(见项目2)。我在分析医疗保险数据及其按地理和时间与其他数据来源(如美国空气污染、天气和社会经济状况)的联系方面积累了经验。在我职业生涯的这一阶段(从博士后到教授)，我开发了用于分析这些大型数据的统计方法(例如，已测量和未测量混杂因素的调整方法、贝叶斯分层模型、因果推断方法和缺失数据方法)。2009年，我被哈佛大学公共卫生学院生物统计学系聘为全职教授。在我被任命的头两年里，我获得了行政管理(信息技术副院长)、导师(NIEHS资助的环境生物统计学培训基金的共同PI)和癌症研究(NCI P01癌症信息学重新提交的PI)的领导职位。我已经开始与达纳·法伯癌症研究所(DFCI)(Schrag、Alexander、Block博士)和哈佛医学院(HMS)医疗保健政策(HCP)(Normand博士)的同事开展新的合作。由于身处新环境，并与新同事在一起，我开始意识到，我在分析医疗保险数据、开发用于评估环境干预措施的健康益处的因果推理方法方面的专业知识可以很容易地移植和增强，以解决CER中至关重要的问题。特别是，无论是出于个人原因，还是因为围绕医疗改革的讨论日益增多，我越来越有兴趣更好地了解索赔数据的使用如何解决有关如何最好地为晚期癌症患者提供护理的问题。事实上，对于这些人群来说，如何提供最好的医疗服务，实现最好的结果，同时控制医疗成本，是非常具有挑战性的。这一领域的知识差距是巨大的，在存在几个伴随因素(例如，多种治疗、医疗队之间的协调、姑息治疗)的情况下，关于提供护理的最佳方式的问题不能仅通过随机临床试验来解决。作为环境科学家，我对医疗保健管理数据库(例如Medicare)的链接和分析提供了前所未有的机会来评估治疗策略的临床有效性，以及癌症研究中医疗保健提供系统的质量和效率。我寻求这一K18是为了：1)提高我未来从事以患者为中心的癌症患者临终关怀(EOL)结果研究的能力；2)指导越来越希望参与CER的初级生物统计学家；3)更有效地与临床研究人员和利益相关者合作；4)进一步开发统计方法，以便能够以最严格的方法解决这些复杂的CER问题。具体目标是：1)参加相对有效性(CER)中紧张的、有指导的职业发展经历，特别关注癌症(见项目4和指导计划)。拟议的培训将直接针对我的研究计划中详述的具体目标(项目11)；2)开展一个研究项目，重点解决如何最好地为定义明确的人群：被诊断为胶质母细胞瘤的老年人提供护理的关键挑战。这将是迄今为止研究的最大人群(N=24,142，A部分Medicare数据，N=9,343 SEER-Medicare，N=9,320，Medicaid-Tumor注册表，N=GBM病例)。研究计划将是一种工具，用于亲身体验如何提供坚实的证据基础来影响政策，并改善患有晚期癌症的老年人的医疗保健体验(项目11)；3)利用当地在癌症CER和EOL方面的丰富专业知识，利用著名的科学和利益攸关方咨询委员会的监督。该委员会包括卫生保健政策、CER、肿瘤学、健康差异、姑息治疗、神经肿瘤学和卫生保健报销决策方面的专家。委员会还包括两名患者权益倡导者和一名获得普利策奖的在线新闻专家(戴安娜·苏格)(项目4)。该项目的成功完成将提供能力建设(数据和方法)，确定重要的数据和方法差距，并解决迄今为止所研究的最大老年GBM患者群体在保健提供和健康差距方面至关重要的问题。数据、方法和结果也将推动CER在其他癌症人群中的研究。这一职业发展奖还将为我提供多学科的专业知识和富有成效的合作，为PCOR培训下一代CER科学家。

DESCRIPTION (provided by applicant): 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 wit 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.