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

基本信息

  • 批准号:
    1612970
  • 负责人:
  • 金额:
    $ 15万
  • 依托单位:
  • 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

项目摘要

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。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Feifang Hu其他文献

Response-adaptive treatment randomization for multiple comparisons of treatments with recurrentevent responses
反应适应性治疗随机化,用于治疗与复发事件反应的多重比较
  • DOI:
    10.1177/09622802221095244
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Jingya Gao;Feifang Hu;Siu Hung Cheung;Pei-Fang Su
  • 通讯作者:
    Pei-Fang Su
Adaptive treatment allocation for comparative clinical studies with recurrent events data
使用复发事件数据进行比较临床研究的适应性治疗分配
  • DOI:
    10.1111/biom.13117
  • 发表时间:
    2019-09
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Jingya Gao;Pei‐Fang Su;Feifang Hu;Siu Hung Cheung
  • 通讯作者:
    Siu Hung Cheung
Statistical inference of adaptive randomized clinical trials for personalized medicine
个性化医疗适应性随机临床试验的统计推断
  • DOI:
    10.4155/cli.15.15
  • 发表时间:
    2015-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Feifang Hu;Yanqing Hu;Wei Ma;Lixin Zhang;Hongjian Zhu
  • 通讯作者:
    Hongjian Zhu
AI-Generated Synthetic Patient Data Helps in Evaluating Daratumumab Treatment Benefit in Multiple Myeloma Subgroups
  • DOI:
    10.1182/blood-2024-208174
  • 发表时间:
    2024-11-05
  • 期刊:
  • 影响因子:
  • 作者:
    Merav Bar;Andrew J. Cowan;Qian Shi;Zixuan Zhao;Zexin Ren;Feifang Hu;Will Ma
  • 通讯作者:
    Will Ma
Optimal responses-adaptive designs based on efficiency, ethic, and cost
基于效率、道德和成本的最佳响应自适应设计
  • DOI:
    10.4310/sii.2018.v11.n1.a9
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0.8
  • 作者:
    Chen Feng;Feifang Hu
  • 通讯作者:
    Feifang Hu

Feifang Hu的其他文献

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{{ truncateString('Feifang Hu', 18)}}的其他基金

Inference for High Dimensional Quantile Regression
高维分位数回归的推理
  • 批准号:
    1712760
  • 财政年份:
    2017
  • 资助金额:
    $ 15万
  • 项目类别:
    Continuing Grant
CAREER: A new and pragmatic framework for modeling and predicting conditional quantiles in data-sparse regions
职业:一种新的实用框架,用于在数据稀疏区域建模和预测条件分位数
  • 批准号:
    1525692
  • 财政年份:
    2014
  • 资助金额:
    $ 15万
  • 项目类别:
    Continuing Grant
Adaptive Design Based upon Covariate Information: New Designs and Their Properties
基于协变量信息的自适应设计:新设计及其属性
  • 批准号:
    1442192
  • 财政年份:
    2013
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
Adaptive Design Based upon Covariate Information: New Designs and Their Properties
基于协变量信息的自适应设计:新设计及其属性
  • 批准号:
    1209164
  • 财政年份:
    2012
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
New Developments in Estimation, Selection and Applications for Mixed Models
混合模型估计、选择和应用的新进展
  • 批准号:
    0906661
  • 财政年份:
    2009
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
Adaptive Designs and Sequential Monitoring
自适应设计和顺序监控
  • 批准号:
    0907297
  • 财政年份:
    2009
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
CAREER: Use of Covariate Information in Adaptive Designs
职业:在自适应设计中使用协变量信息
  • 批准号:
    0349048
  • 财政年份:
    2004
  • 资助金额:
    $ 15万
  • 项目类别:
    Continuing Grant
Power, Variability, and Optimality in Adaptive Designs
自适应设计中的强大功能、可变性和最优性
  • 批准号:
    0204232
  • 财政年份:
    2002
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant

相似海外基金

Covariate-adjusted Expected Shortfall under Data Heterogeneity
数据异质性下的协变量调整预期缺口
  • 批准号:
    2345035
  • 财政年份:
    2023
  • 资助金额:
    $ 15万
  • 项目类别:
    Standard Grant
Covariate-adjusted Expected Shortfall under Data Heterogeneity
数据异质性下的协变量调整预期缺口
  • 批准号:
    2310464
  • 财政年份:
    2023
  • 资助金额:
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Non-parametric estimation under covariate shift: From fundamental bounds to efficient algorithms
协变量平移下的非参数估计:从基本界限到高效算法
  • 批准号:
    2311072
  • 财政年份:
    2023
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Covariate adjustment in cluster randomised trials with binar y outcomes focussing on relative risks and risk difference
整群随机试验中的协变量调整,二元结果侧重于相对风险和风险差异
  • 批准号:
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Statistical Methods and Theory for Predictive Biomarker Study in Clinical Trials via Modeling and Analysis of Covariate Interactions
通过协变量相互作用建模和分析进行临床试验中预测生物标志物研究的统计方法和理论
  • 批准号:
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  • 财政年份:
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    Discovery Grants Program - Individual
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动态网络嵌入的协变量信息
  • 批准号:
    2741521
  • 财政年份:
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  • 资助金额:
    $ 15万
  • 项目类别:
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Collaborative Research: Covariate-Driven Approaches to Network Estimation
协作研究:协变量驱动的网络估计方法
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Mathematical modeling with imperfect/incomplete covariate information
具有不完美/不完整协变量信息的数学建模
  • 批准号:
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  • 财政年份:
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Statistical Methods and Theory for Predictive Biomarker Study in Clinical Trials via Modeling and Analysis of Covariate Interactions
通过协变量相互作用建模和分析进行临床试验中预测生物标志物研究的统计方法和理论
  • 批准号:
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    $ 15万
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    Discovery Grants Program - Individual
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具有不完美/不完整协变量信息的数学建模
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