权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Evaluating effects of complex treatments in chronic disease using large observational datasets

使用大型观察数据集评估慢性病复杂治疗的效果

基本信息

批准号：
MR/S017968/1
负责人：
Ruth Keogh
金额：
$ 100.3万
依托单位：
London School of Hygiene & Tropical Medicine
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FS017968%2F1
关键词：
Evaluating effects complex treatments chronic

项目摘要

Patients with chronic conditions and their carers face difficult questions about the effects of treatments: What are the long-term effects of multiple treatments used in combination? What are the expected health outcomes for a given patient under different treatment choices? Randomized controlled trials are the gold standard for estimating treatment effects, but their ability to answer complex questions is limited. They are typically restricted to a subset of the eventual treatment population, have short follow-up, usually do not consider several treatments in combination, and can be unethical. Large longitudinal observational datasets from disease registries and electronic health records provide the means to gain understanding of treatment effects that would not be feasible in a trial. However, to do this we have to successfully overcome the fundamental difficulty that those who received a given treatment were prescribed it for a reason, and hence the groups who do and do not receive treatment are not directly comparable. How to handle this is a highly active area of research. This fellowship will develop and evaluate statistical methods needed to address key questions about the effects of treatments on health outcomes using large observational datasets. It will also tackle crucial questions about the effects of treatments on health outcomes for patients with cystic fibrosis (CF) using data from the UK CF Registry. CF is an inherited, chronic, progressive, and life-shortening condition affecting about 10,000 in the UK. People with CF need intensive treatment and support from health services and families. The UK CF Registry captures data on nearly all UK CF approximately annually and the data contain demographic information, information on treatments used, clinical measurements, and dates of birth, diagnosis and death.Key statistical advancements will include methods enabling estimation of the effects of multiple treatments used in combination on health outcomes; methods for answering questions about the impact of a one-time major intervention (e.g. lung transplantation) on survival in a way that is most relevant to patients and clinicians; and methods for providing personalised information to patients about their expected outcomes under different treatment choices. Patients with CF take many treatments daily, which is time consuming and unpleasant. Questions addressed in this fellowship will focus on those that could help reduce treatment burden and help patients and clinicians in making treatment decisions. We will investigate the impact of three commonly used treatments on lung function, and whether existing treatments are redundant in patients receiving Ivacaftor, which is a major new treatment in CF since 2012 and effective for patients with a certain genotype. Another key question will be about the impact of lung transplantation on survival in CF. This research will provide personalised information for patients about what their expected survival would be should they choose to be listed for lung transplant or not.In the second part of the fellowship, I will study the impact of treatments on health outcomes in other chronic disease areas of major public health importance, including type 2 diabetes, using the Clinical Practice Research Database (CPRD). CPRD is a database of over 11 million patients in the UK, obtained from General Practitioner records. It will be important to react to new questions arising in CF and in other chronic diseases. Further statistical developments will be an ongoing focus.This research will be led by the Future Leaders Fellow with assistance from a postdoctoral researcher. The host institution is the London School of Hygiene and Tropical Medicine. The Cystic Fibrosis Trust, a UK-wide charity, is a Project Partner. Several collaborators will provide expert input: they include statisticians, clinicians, and patient representatives.

慢性病患者及其护理人员面临着关于治疗效果的难题：联合使用多种治疗的长期效果是什么？在不同的治疗选择下，特定患者的预期健康结果是什么？随机对照试验是估计治疗效果的金标准，但它们回答复杂问题的能力有限。它们通常仅限于最终治疗人群的一个子集，随访时间短，通常不考虑联合使用几种治疗方法，并且可能不道德。来自疾病登记和电子健康记录的大型纵向观察数据集提供了了解治疗效果的方法，而这些方法在试验中是不可行的。然而，要做到这一点，我们必须成功地克服一个基本的困难，即那些接受某种治疗的人是有原因的，因此接受和不接受治疗的群体不能直接比较。如何处理这一问题是一个非常活跃的研究领域。该奖学金将开发和评估使用大型观察数据集解决治疗对健康结果影响的关键问题所需的统计方法。它还将利用英国囊性纤维化登记处的数据，解决有关治疗对囊性纤维化（CF）患者健康结果影响的关键问题。CF是一种遗传性、慢性、进行性和缩短寿命的疾病，在英国影响约10，000人。患有CF的人需要强化治疗和卫生服务和家庭的支持。英国CF登记处每年收集几乎所有英国CF的数据，数据包含人口统计信息、所用治疗信息、临床测量以及出生日期、诊断和死亡。关键的统计进步将包括能够估计多种治疗组合对健康结果影响的方法;回答有关一次性重大干预措施影响的问题的方法（例如肺移植）以与患者和临床医生最相关的方式对生存率的影响;以及用于向患者提供关于他们在不同治疗选择下的预期结果的个性化信息的方法。患有CF的患者每天都要接受许多治疗，这既耗时又令人不快。在这个奖学金解决的问题将集中在那些可以帮助减少治疗负担，并帮助患者和临床医生在作出治疗决定。我们将研究三种常用治疗方法对肺功能的影响，以及现有治疗方法在接受依伐卡托治疗的患者中是否多余，依伐卡托是自2012年以来CF的主要新治疗方法，对特定基因型患者有效。另一个关键问题是肺移植对CF患者生存率的影响。这项研究将为患者提供个性化的信息，让他们了解如果他们选择接受肺移植或不接受肺移植，他们的预期生存率将是多少。在研究的第二部分，我将使用临床实践研究数据库（CPRD）研究治疗对其他具有重大公共卫生重要性的慢性病领域（包括2型糖尿病）健康结局的影响。CPRD是英国超过1100万患者的数据库，从全科医生记录中获得。重要的是要对CF和其他慢性病中出现的新问题做出反应。进一步的统计发展将是一个持续的重点。这项研究将由未来领导者研究员领导，并由博士后研究员协助。主办机构是伦敦卫生和热带医学学院。囊性纤维化信托基金，一个英国范围内的慈善机构，是一个项目合作伙伴。几位合作者将提供专家意见：他们包括统计学家、临床医生和患者代表。