Bayesian Nonparametric Methodology for CER: Instrumental Variables Models

描述（由申请人提供）：比较医疗干预的有效性，例如外科手术的药物治疗，是医学科学家正在进行的工作。由于患者对任何干预措施的反应因人而异，因此统计方法对于任何比较都是必要的。实现这一目的的理想工具是对随机接受治疗的患者进行对照临床试验。随机化在治疗之外的许多未测量因素中创造了平衡，这些因素导致患者反应的变化。它还允许在相对有效性的竞争假设下计算决策所依据的期望和概率。然而，通常情况下，临床试验中招募的患者仅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。然而，这些数据库中的患者已经选择了他们的治疗，而不是被外部随机分配到它。众所周知，用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偏倚估计。两种一般的方法已经开发用于观察性研究：倾向评分匹配和采用工具变量。该项目旨在为第二种方法开发基于模型的新方法，在两种结果类别中具有最小的分布假设，这些结果类别广泛用于医疗干预措施的比较有效性。这些措施是：二元结果，如术后感染或30天死亡率，以及事件发生时间结果，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，为二进制结果的逻辑和概率回归，使用贝叶斯非参数方法的工具变量。2.使用贝叶斯非参数方法进行考克斯比例风险回归，使用工具变量可预测的内源性回归量，开发事件发生时间结局的推断方法，包括计算算法。3.使用重复数据模拟，将具体目标1和2中开发的方法的性能与当前可用的线性渐近方法进行比较。由于拟议的工作，根据观察数据评估医疗干预措施相对有效性的方法将得到大幅改进。这些方法适用于大多数疾病。目前，对各种癌症、心血管疾病和老年病进行了许多研究。 公共卫生相关性：为了改善国民的健康，重要的是要找出哪些药物和医疗效果更好。州和国家登记册以及其他大型数据库包含许多可用于这一目的的信息。该项目将大大改进目前从这些信息中得出结论的科学方法。

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.