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Bayesian Nonparametric Methodology for CER: Instrumental Variables Models

CER 的贝叶斯非参数方法：工具变量模型

基本信息

批准号：
8036807
负责人：
Purushottam W Laud
金额：
$ 116.92万
依托单位：
MEDICAL COLLEGE OF WISCONSIN
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-27 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8036807
关键词：
Address Cardiovascular Diseases Categories Clinical Trials Clinical Trials Design Computational algorithm Consultations Controlled Clinical Trials Data Databases Development Disease Effectiveness Elderly Enrollment Equation Equilibrium Event Family Hand Health Individual Infection Insurance Intervention Joints Laboratories Least-Squares Analysis Link Literature Logistic Regressions Logistics Malignant Neoplasms Markov Chains Medical Medicare Methodology Methods Modality Modeling Monte Carlo Method Observational Study Odds Ratio Operative Surgical Procedures Outcome Patients Performance Pharmaceutical Preparations Pharmacotherapy Physicians Placebos Population Population Intervention Probability Prophylactic treatment Randomized Recurrence Registries Relative Risks Research Personnel Sampling Science Scientist Services Source Staging Target Populations Techniques Time Variant Work abstracting alternative treatment base cancer diagnosis comparative effectiveness compare effectiveness effectiveness measure expectation hazard improved interest mortality neoplasm registry novel strategies response simulation theories tool

项目摘要

DESCRIPTION (provided by applicant): Comparing effectiveness of medical interventions, for example drug therapies of surgical procedures, is an ongoing undertaking for medical scientists. As the patient response to any intervention varies from individual to individual, statistical methodology is necessary for any comparison. An ideal tool for this purpose is the controlled clinical trial with patients randomized to treatment. The randomization creates a balance in the many unmeasured factors beyond the treatment that cause variation in patient response. It also allows the calculation, under competing hypotheses of comparative effectiveness, of expectations and probabilities on which decisions can be based. Typically, however, patients enrolled in a clinical trial are drawn from and represent only a small segment of the target population for the intervention. To measure the effectiveness of any intervention in the unselective population at large, it is possible to use other excellent sources of relevant information such cancer registries at national and state levels. However, patients in these databases have chosen their treatment, as opposed to being externally and randomly assigned to it. It is well known that analyzing observational data with techniques designed for clinical trials leads to biased estimation of comparative effectiveness. Two general approaches have been developed for use in observational studies: propensity score matching and employing instrumental variables. This project aims to develop, for the second approach, new model-based methodology with minimal distributional assumptions in two outcome categories that are widely used in comparative effectiveness of medical interventions. These are: a binary outcome such as post-surgical infection or 30-day mortality, and a time-to-event outcome such as survival time after cancer diagnosis or disease recurrence time. The specific aims of the study are: 1. Develop inference methodology, including computational algorithms, for logistic and probit regression for binary outcomes, using the Bayesian nonparametric approach for instrumental variables. 2. Develop inference methodology, including computational algorithms, for time-to-event outcomes, using the Bayesian nonparametric approach for Cox proportional hazards regression with endogenous regressors predictable by instrumental variables. 3. Using repeated data simulation, compare performance of the methods developed in specific aims 1 and 2 with currently available linear asymptotic methods. As a result of the proposed work, methods for assessing comparative effectiveness of medical interventions from observational data will be improved substantially. The methods will apply to most diseases. Currently, many studies are conducted for various cancers, cardiovascular diseases and geriatric conditions. PUBLIC HEALTH RELEVANCE: To improve the health of the nation, it is important to find out which drugs and medical treatments work better. State and national registries, and other large databases, contain much information that can be used for this purpose. This project will substantially improve currently available scientific methods of making conclusions from this information.

描述（由申请人提供）：比较医学干预措施的有效性，例如药物治疗和外科手术，是医学科学家正在进行的一项工作。由于患者对任何干预措施的反应因人而异，因此进行任何比较都需要统计方法。实现这一目的的一个理想工具是对随机接受治疗的患者进行对照临床试验。随机化在许多无法测量的因素中创造了平衡，这些因素超出了导致患者反应变化的治疗。它还允许在相互竞争的相对有效性假设下，计算决策所依据的期望和概率。然而，通常情况下，参加临床试验的患者来自并且只代表干预目标人群的一小部分。为了衡量任何干预措施在非选择性人群中的有效性，可以使用其他优秀的相关信息来源，如国家和州一级的癌症登记处。然而，这些数据库中的患者已经选择了他们的治疗方法，而不是外部随机分配的治疗方法。众所周知，用为临床试验设计的技术分析观察数据会导致对比较有效性的估计有偏倚。在观察性研究中，有两种常用的方法：倾向得分匹配和使用工具变量。该项目旨在为第二种方法开发新的基于模型的方法，在两种结果类别中采用最小的分布假设，广泛用于医疗干预的比较有效性。它们是：二元结果，如手术后感染或30天死亡率，以及时间到事件的结果，如癌症诊断后的生存时间或疾病复发时间。本研究的具体目的是：1。开发推理方法，包括计算算法，为二元结果的逻辑和概率回归，使用贝叶斯非参数方法为工具变量。2. 开发推理方法，包括计算算法，用于时间到事件的结果，使用贝叶斯非参数方法进行Cox比例风险回归，内生回归量可由工具变量预测。3. 使用重复数据模拟，比较具体目标1和2中开发的方法与当前可用的线性渐近方法的性能。由于拟议的工作，将大大改进根据观察数据评估医疗干预措施相对有效性的方法。这些方法适用于大多数疾病。目前，针对各种癌症、心血管疾病和老年疾病进行了许多研究。