Novel and Rigorous Statistical Learning and Inference for Comparative Effectiveness Research with Complex Data

复杂数据比较有效性研究的新颖而严格的统计学习和推理

• 批准号: 10635323
• 负责人: Zhiqiang Tan
• 金额: $ 34.66万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10635323
• 关键词: Anticoagulants; Applied Research; Atrial Fibrillation; Atrial Flutter; Calibration; Collaborations; Comparative Effectiveness Research; Complex; Confounding Factors (Epidemiology); Data; Dipeptidyl Peptidases; Documentation; Drug Prescriptions; Effectiveness; Grant; Health; Healthcare; Machine Learning; Measures; Medical; Medicare; Medicine; Methods; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Observational Study; Oral; Outcome; Patients; Pharmaceutical Preparations; Population; Predisposition; Probability; Process; Property; Research; Research Personnel; Resort; Risk Factors; Safety; Schedule; Schizophrenia; Statistical Data Interpretation; Statistical Methods; Statistical Models; Therapeutic; Uncertainty; Variant; Work; care outcomes; comparative effectiveness study; comparative treatment; complex data; computing resources; data resource; design; epidemiology study; hazard; health care delivery; high dimensionality; improved; inhibitor; instrument; machine learning method; model building; multidimensional data; novel; random forest; safety study; software development; statistical learning; statistics; treatment effect; treatment group; treatment strategy; user friendly software

Project Summary Comparative effectiveness research (CER) in medicine is commonly conducted to discover and provide infor- mation on possible differences between alternative drugs or treatments in their effectiveness and safety. Such information, if reliable and accurate, can help patients, clinicians, and other healthcare stakeholders to make better-informed healthcare decisions and improve healthcare delivery and outcomes. However, drawing valid and relevant inferences about treatment effects from observational studies involves effort and expertise from both subject-matter researchers and statisticians. On one hand, causal inference relies on structural assump- tions. Two prominent classes of such assumptions are unconfoundedness or instrument variable (IV) assump- tions. On the other hand, granted the structural assumptions, causal inference also requires statistical modeling and estimation of population properties and associations from empirical data. The problem of statistical learning and inference can be challenging, while allowing a large number of candidate regressors such as main effects and interactions of covariates. The objective of our research is to develop, evaluate, and disseminate a new set of theoretically rigorous, numerically automated, and practically useful methods of statistical learning and inference for estimating treatment effects in CER with complex, high-dimensional data. Three specific aims are (1) high-dimensional inference about population and subpopulation average treatment effects under uncon- foundedness with multi-valued treatments, (2) high-dimensional inference about local average treatment effects and IV-dependent average treatment effects on the treated with multi-valued instruments and treatments, and (3) high-dimensional inference about average treatment effects such as contrasts between survival and hazard probabilities with longitudinal and survival data. We will investigate applications of the new methods to several comparative effectiveness and safety studies including a recent study on comparative treatment strategies in schizophrenia and an ongoing project to evaluate the therapeutic exchangeability of same-class drugs, for ex- ample, direct oral anticoagulants among patients with atrial fibrillation or atrial flutter or dipeptidyl peptidase-4 inhibitors among patients with type 2 diabetes, while exploiting IVs created by the design of the Medicare pre- scription drug benefit. We will develop and publicly release user-friendly computer software including transparent documentation for direct implementation of the new methods.

项目概要 医学中的比较有效性研究（CER）通常是为了发现和提供信息 替代药物或治疗方法在有效性和安全性方面可能存在的差异。这样的 如果信息可靠且准确，可以帮助患者、临床医生和其他医疗保健利益相关者做出 更明智的医疗保健决策并改善医疗保健服务和结果。然而，绘图有效 观察性研究中关于治疗效果的相关推论涉及来自以下机构的努力和专业知识： 主题研究人员和统计学家。一方面，因果推理依赖于结构假设 系统蒸发散。此类假设的两个突出类别是无混杂性或工具变量（IV）假设 系统蒸发散。另一方面，考虑到结构假设，因果推断也需要统计建模 以及根据经验数据估计人口属性和关联。统计学习的问题 推理可能具有挑战性，同时允许大量候选回归变量，例如主效应 和协变量的相互作用。我们研究的目标是开发、评估和传播一种新的 一套理论上严谨、数字自动化且实用的统计学习方法 使用复杂的高维数据推断 CER 中的治疗效果。三个具体目标 是（1）关于未控制下人群和亚人群平均治疗效果的高维推断 多值治疗的基础，（2）局部平均治疗效果的高维推断 对使用多值仪器和治疗进行治疗的依赖于 IV 的平均治疗效果，以及 (3) 关于平均治疗效果的高维推断，例如生存与危险之间的对比 纵向和生存数据的概率。我们将研究新方法在几个方面的应用 比较有效性和安全性研究，包括最近一项关于比较治疗策略的研究 精神分裂症和一个正在进行的项目，旨在评估同类药物的治疗可互换性，例如 房颤或心房扑动患者充足的直接口服抗凝剂或二肽基肽酶 4 2 型糖尿病患者中使用抑制剂，同时利用 Medicare pre- 处方药的好处。我们将开发并公开发布用户友好的计算机软件，包括透明的 直接实施新方法的文档。