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.

精准医疗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）定制治疗方案的方法。随着现代技术的发展，识别可能与某些疾病及其治疗相关的重要生物标志物（通常称为预测性生物标志物）变得更加容易。为了设计一个有效的精准医学临床研究，应该结合这些有用的预测生物标志物。在本项目中，研究者对与精准医学临床试验相关的可行性设计和统计推断进行了系统和全面的研究。该项目将介绍和研究创新的设计和统计方法，并将探讨如何在一些真实的临床试验中实施所提出的设计和统计方法。该项目的目标是引入新的设计，并为精准医学的自适应随机临床试验提供新的统计方法。最近，遗传学的进步已经允许识别与某些疾病相关的基因（生物标志物）。这些生物标志物（称为预测性生物标志物）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。为了开发精准医学，临床试验需要突破性的设计，以便纳入预测性生物标志物，以促进治疗选择。精准医学临床试验的特点要求统计方法具有先进性和创新性。该项目的重点是临床试验的设计和相应的精准医学统计推断。研究者介绍了协变量调整的响应自适应设计的创新类，研究这些自适应设计的统计特性，开发新的统计推断方法并获得其特性，并将这些方法应用于实际问题。