A Translational Framework for Methodological Rigor to Improve Patient Centered Ou

描述（由申请人提供）：医疗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，以及在大型和异质人群中医疗保健提供系统的质量和效率。减少卫生差距和评估卫生保健提供系统的必要性从未如此迫切。这些评估必须以最严格的方法、尽可能最好的数据进行，必须让患者参与，并且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）的生物统计学教授和信息技术副院长。1999年至2009年在约翰霍普金斯大学工作期间，我开发了用于分析空气污染与健康大型数据库的统计方法（见项目2）。我在分析医疗保险数据以及按地理和时间与其他数据源（如美国空气污染、天气和社会经济状况）的联系方面获得了经验。在这一阶段（从博士后到教授），我开发了分析这些大数据的统计方法（如测量和未测量混杂因素的调整方法，贝叶斯层次模型，因果推理方法，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正教授。在我被任命的头两年里，我在行政管理（信息技术副院长）、指导（NIEHS资助的环境生物统计学培训补助金的共同PI）和癌症研究（重新提交NCI P01癌症信息学的PI）方面获得了领导职位。我已经开始与达纳法伯癌症研究所（DFCI）的同事开展新的合作。Schrag, Alexander, Block)和哈佛医学院（HMS）的卫生保健政策（HCP） （Dr. Normand）。由于在一个新的环境中，与新的同事在一起，我开始意识到我在医疗保险数据分析方面的专业知识，在开发评估环境干预对健康益处的因果推理方法方面的专业知识，可以很容易地转移和增强，以解决CER中至关重要的问题。特别是，由于个人原因和围绕医疗改革的讨论越来越多，我对更好地理解如何使用索赔数据来解决有关如何最好地为晚期癌症患者提供护理的问题越来越感兴趣。事实上，对于这些人群来说，如何提供最好的护理，取得最好的结果，同时控制医疗费用是非常具有挑战性的。这一领域的知识差距是巨大的，在存在多种伴随因素（例如多种治疗、医疗团队之间的协调、姑息治疗）的情况下，提供护理的最佳方式的问题无法仅通过随机临床试验来解决。作为环境科学家，我对医疗管理数据库（如医疗保险）的联系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。我寻求这个K18是为了：1)提高我在癌症患者临终关怀（EOL）中以患者为中心的未来研究的能力；2)指导越来越多想要参与CER的初级生物统计学家；3)更有效地与临床研究者和利益相关者合作，4)进一步发展统计方法，以便这些复杂的CER问题可以用最高的方法严密性来解决。具体的目标是：1)参加一个激烈的，指导的职业发展经验，比较有效性（CER），特别关注癌症（见第4项和指导计划）。拟议的培训将直接针对我的研究计划（item11）中详细说明的具体目标；2)开展一项研究项目，重点解决如何最好地为一个明确定义的人群提供护理的关键挑战：被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人群（N=24,142， A部分医疗保险数据，N=9,343, SEER-Medicare， N=9,320，医疗补助-肿瘤登记，N=GBM病例）。该研究计划将是亲身体验如何为影响政策和改善晚期癌症老年人的医疗保健体验提供坚实的证据基础的工具（项目11）；3)利用当地丰富的癌症和EOL领域的CER专业知识，由著名的科学和利益相关者咨询委员会监督。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。该委员会还包括两名患者维权人士和一位获得普利策奖的EOL记者专家（戴安娜·萨格）（第4项）。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的GBM老年患者群体在保健服务和健康差距方面最重要的问题。数据、方法和结果也将推动其他癌症人群的CER研究。这个职业发展奖也将为我提供多学科的专业知识和富有成效的合作，这是为PCOR培训下一代CER科学家所必需的。

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.