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.

精密医学是一种方法，可让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）来量身定制治疗方案。借助当今的现代技术，可以识别可能与某些疾病及其治疗相关的重要生物标志物（通常称为预测生物标志物）要容易得多。为了设计有效的精密医学临床研究，应纳入这些有用的预测生物标志物。在该项目中，研究人员对与精密医学的临床试验有关的可行设计和统计推断进行了系统的全面研究。该项目将介绍和研究创新的设计和统计方法，并将研究如何在一些实际临床试验中实施拟议的设计和统计方法。该项目的目的是引入新的设计，并为精密医学的自适应随机临床试验提供新颖的统计方法。最近，遗传学的进步允许鉴定与某些疾病有关的基因（生物标志物）。这些生物标志物（称为预测生物标志物）可用于预测特定治疗的反应，并确认其预测能力取决于精心设计的临床研究。为了开发精确的医学，需要进行临床试验的突破设计，以便可以将预测性生物标志物纳入以促进治疗选择。需要提前和创新的统计方法来匹配精密医学临床试验的特殊特征。该项目着重于临床试验的设计以及对精度医学的相应统计推断。研究者介绍了协变量调整的自适应设计的创新类别，研究这些自适应设计的统计特性，开发了用于统计推断的新方法并获得其特性，并将这些方法应用于实际问题。