New Covariate-Adjusted Response-Adaptive Designs and Associated Methods for Statistical Inference

新的协变量调整响应自适应设计和相关统计推断方法

• 批准号: 1612970
• 负责人: Feifang Hu
• 金额: $ 15万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1612970&HistoricalAwards=false
• 关键词: New; Covariate; Adjusted; Response; Adaptive

Precision medicine is an approach that allows physicians to tailor a treatment regimen based on an individual patient's characteristics (which could be biomarkers or other covariates). With today's modern technology, it is much easier to identify important biomarkers (usually called predictive biomarkers) that may associate with certain diseases and their treatments. To design an efficient clinical study of precision medicine, one should incorporate these useful predictive biomarkers. In this project, the investigator undertakes a systematic and comprehensive study of feasible designs and statistical inference related to clinical trials of precision medicine. The project will introduce and study innovative designs and statistical methods and will investigate how to implement the proposed designs and statistical methods in some real clinical trials. The objective of this project is to introduce new designs and to provide novel statistical methods for adaptive randomized clinical trials of precision medicine. Recently, advances in genetics have permitted identification of genes (biomarkers) that are linked to certain diseases. These biomarkers (called predictive biomarkers) could be used to predict the response of a specific treatment and the confirmation of their predictive power relies on carefully designed clinical studies. To develop precision medicine, groundbreaking designs are needed for clinical trials so that predictive biomarkers can be incorporated to facilitate treatment selection. Advance and innovative statistical methods are required to match special features of clinical trials of precision medicine. This project focuses on the designs of clinical trials and the corresponding statistical inference for precision medicine. The investigator introduces innovative classes of covariate-adjusted response-adaptive designs, studies the statistical properties of these adaptive designs, develops new methods for statistical inference and obtains their properties, and applies these methods to practical problems.

精准医学是一种允许医生根据个体患者的特征（可能是生物标志物或其他协变量）量身定制治疗方案的方法。有了今天的现代技术，识别可能与某些疾病及其治疗有关的重要生物标志物（通常称为预测性生物标志物）要容易得多。为了设计一个有效的精准医学临床研究，人们应该结合这些有用的预测性生物标志物。在本项目中，研究者对精准医学临床试验的可行性设计和统计推断进行系统、全面的研究。该项目将引入和研究创新的设计和统计方法，并将研究如何在一些实际的临床试验中实施所提出的设计和统计方法。本项目旨在为精准医学的适应性随机临床试验引入新的设计和提供新的统计方法。最近，遗传学的进步使鉴定与某些疾病有关的基因（生物标志物）成为可能。这些生物标记物（称为预测性生物标记物）可用于预测特定治疗的反应，其预测能力的确认依赖于精心设计的临床研究。为了发展精准医疗，需要突破性的临床试验设计，以便将预测性生物标志物纳入治疗选择中。为了适应精准医学临床试验的特点，需要采用先进、创新的统计方法。本项目主要研究精准医学的临床试验设计及相应的统计推断。研究者引入了协变量调整响应自适应设计的创新类别，研究了这些自适应设计的统计性质，开发了新的统计推断方法并获得了它们的性质，并将这些方法应用于实际问题。