Flexible Bayesian approaches to causal inference with multilevel survival data and multiple treatments
利用多级生存数据和多种治疗进行因果推理的灵活贝叶斯方法
基本信息
- 批准号:10442178
- 负责人:
- 金额:$ 22.55万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-07-01 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Project Summary
Combining comparative effectiveness research (CER) and dissemination and implementation research is playing
an increased role in public health and health care service by allowing practitioners to make informed decisions
about treatments and improving adoption of evidence-based practices. In circumstances where CER questions
do not lend themselves to direct experimentation or in implementation trials where incomplete adoption of in-
tervention occurs, causal inference tools for “field data” are recommended for evaluating treatment effects. The
increased complexities in large national electronic health databases pose challenges for statistical analyses and
demand approaches beyond conventional causal inference techniques, which have traditionally focused on bi-
nary treatment. Given the wealth of information captured in large-scale data, it is rare that treatment regimens
are defined in terms of two treatments only. The data are typically pooled from treating facilities across the nation
with considerable variability in the institutional effect. Although it has been established that popular tools for bi-
nary treatment are inappropriate for the multiple treatment setting, and that ignoring the multilevel data structure
can bias the estimate of the treatment effect, few alternative methods have been proposed to deal with both
complications simultaneously. The first aim of our proposed project is to develop a novel and flexible Bayesian
approach to estimating the causal effects of multiple treatments on survival with clustered data. We then fully
investigate the operating characteristics of our proposed method in a variety of simulated scenarios and contrast
it with approaches often used in practice. For causal estimates to be unbiased, researchers commonly make the
assumption of no unmeasured confounding (UMC). Though highly recommended with binary treatment, there
is no known implementation or framework for sensitivity analysis with multiple treatments and multilevel survival
data. The second aim of our project is to develop and apply a flexible and interpretable Bayesian approach to
assessing the sensitivity of causal estimates to possible departures from the assumption of no UMC, at both
cluster- and individual-level. This approach is capable of gauging the amount of unobserved confounding needed
to change the direction of the observed treatment effects Our project will apply the developed methods in the first
two aims to a large representative high-risk localized prostate cancer population, drawn from the de-identified
National Cancer Data Base, to evaluate the average causal effects of three popular treatment options on survival
and evaluate how unmeasured confounding might alter causal conclusions. We also will estimate treatment het-
erogeneity and identify distinct subgroups of patients for which a treatment is effective or harmful. Our methods
will establish the effectiveness component and lay the groundwork for building the cost-effectiveness models,
and provide evidence for further investigations of variations in intervention implementation and modifications in
recommendations for treatments leading to different patient outcomes. To facilitate the dissemination of our work,
we will share the underlying statistical code via an R package.
项目概要
将比较有效性研究(CER)与传播和实施研究相结合正在发挥作用
通过允许从业者做出明智的决定,在公共卫生和医疗保健服务中发挥更大的作用
在 CER 提出质疑的情况下,关于治疗和改进循证实践的采用。
不适合直接实验或实施试验,其中不完全采用in-
当干预发生时,建议使用“现场数据”的因果推理工具来评估治疗效果。
大型国家电子卫生数据库日益复杂,给统计分析和统计带来了挑战
需求方法超越了传统的因果推理技术,传统上侧重于双向
鉴于大规模数据中捕获的大量信息,治疗方案很少见。
仅根据两种治疗来定义数据通常来自全国各地的治疗设施。
尽管已经确定流行的双向工具具有很大的可变性。
单一处理不适合多重处理设置,并且忽略了多级数据结构
可能会对治疗效果的估计产生偏差,但很少有人提出替代方法来处理这两种情况
我们提出的项目的首要目标是开发一种新颖且灵活的贝叶斯模型。
然后我们充分利用聚类数据来估计多种治疗对生存的因果影响。
研究我们提出的方法在各种模拟场景中的运行特性并进行对比
为了使因果估计不偏不倚,研究人员通常会采用实践中常用的方法。
假设没有不可测量的混杂因素(UMC),尽管强烈建议采用二元处理,但有
没有已知的实现或框架用于多种治疗和多水平生存的敏感性分析
我们项目的第二个目标是开发和应用灵活且可解释的贝叶斯方法。
评估因果估计对可能偏离无 UMC 假设的敏感性,
这种方法能够衡量所需的未观察到的混杂因素的数量。
改变观察到的治疗效果的方向我们的项目将首先应用开发的方法
两个目标是从未识别的人群中抽取大量具有代表性的高风险局部前列腺癌人群
国家癌症数据库,评估三种流行治疗方案对生存的平均因果影响
并评估未测量的混杂因素如何改变因果结论。我们还将估计治疗方法。
我们的方法对不同的患者亚组进行治疗有效或有害。
将建立有效性部分并为构建成本效益模型奠定基础,
并为调查干预实施的变化和修改提供进一步的证据
导致不同患者结果的治疗建议 为了促进我们工作的传播,
我们将通过 R 包共享底层统计代码。
项目成果
期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Correlates of cancer prevalence across census tracts in the United States: A Bayesian machine learning approach.
美国各人口普查区癌症患病率的相关性:贝叶斯机器学习方法。
- DOI:
- 发表时间:2022-08
- 期刊:
- 影响因子:0
- 作者:Niu, Li;Hu, Liangyuan;Li, Yan;Liu, Bian
- 通讯作者:Liu, Bian
A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA.
针对多种治疗和二元结果的未测量混杂因素的灵活敏感性分析方法,适用于 SEER-Medicare 肺癌数据。
- DOI:
- 发表时间:2022-06
- 期刊:
- 影响因子:0
- 作者:Hu, Liangyuan;Zou, Jungang;Gu, Chenyang;Ji, Jiayi;Lopez, Michael;Kale, Minal
- 通讯作者:Kale, Minal
Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.
基于树的机器学习可识别和理解社区层面中风的主要决定因素。
- DOI:
- 发表时间:2020
- 期刊:
- 影响因子:5.4
- 作者:Hu, Liangyuan;Liu, Bian;Ji, Jiayi;Li, Yan
- 通讯作者:Li, Yan
A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.
使用具有聚类观察的大型数据集评估因果治疗对患者生存影响的异质性的灵活方法。
- DOI:
- 发表时间:2022-11-12
- 期刊:
- 影响因子:0
- 作者:Hu, Liangyuan;Ji, Jiayi;Liu, Hao;Ennis, Ronald
- 通讯作者:Ennis, Ronald
A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.
聚类生存数据的新方法:治疗效果异质性估计和变量选择。
- DOI:
- 发表时间:2024-01
- 期刊:
- 影响因子:0
- 作者:Hu; Liangyuan
- 通讯作者:Liangyuan
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Liangyuan Hu其他文献
Liangyuan Hu的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Liangyuan Hu', 18)}}的其他基金
Bayesian machine learning for causal inference with incomplete longitudinal covariates and censored survival outcomes
用于不完整纵向协变量和审查生存结果的因果推理的贝叶斯机器学习
- 批准号:
10445648 - 财政年份:2022
- 资助金额:
$ 22.55万 - 项目类别:
Bayesian machine learning for causal inference with incomplete longitudinal covariates and censored survival outcomes
用于不完整纵向协变量和审查生存结果的因果推理的贝叶斯机器学习
- 批准号:
10620291 - 财政年份:2022
- 资助金额:
$ 22.55万 - 项目类别:
Flexible Bayesian approaches to causal inference with multilevel survival data and multiple treatments
利用多级生存数据和多种治疗进行因果推理的灵活贝叶斯方法
- 批准号:
10056850 - 财政年份:2020
- 资助金额:
$ 22.55万 - 项目类别:
相似国自然基金
基于机器学习和贝叶斯优化算法的药物结晶溶剂设计方法
- 批准号:22308228
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
基于高维组学数据的贝叶斯多水平stacking融合预测模型构建方法与应用研究
- 批准号:82373688
- 批准年份:2023
- 资助金额:49 万元
- 项目类别:面上项目
农田生物量遥感估算模型中输入不确定性的贝叶斯优化方法研究
- 批准号:42301386
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
面向偏微分方程求解的贝叶斯神经算子理论及方法研究
- 批准号:62306176
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
复杂环境下基于贝叶斯学习的分布式网络自适应滤波理论与方法
- 批准号:
- 批准年份:2022
- 资助金额:30 万元
- 项目类别:青年科学基金项目
相似海外基金
Bayesian approaches to identify persons with osteoarthritis in electronic health records and administrative health data in the absence of a perfect reference standard
在缺乏完美参考标准的情况下,贝叶斯方法在电子健康记录和管理健康数据中识别骨关节炎患者
- 批准号:
10665905 - 财政年份:2023
- 资助金额:
$ 22.55万 - 项目类别:
Developing Novel Bayesian Track Before Detect Approaches for Maritime Big Data Challenges
在检测方法之前开发新颖的贝叶斯轨迹应对海事大数据挑战
- 批准号:
2889729 - 财政年份:2023
- 资助金额:
$ 22.55万 - 项目类别:
Studentship
Novel Coalescent Approaches for Studying Evolutionary Processes
研究进化过程的新联合方法
- 批准号:
10552480 - 财政年份:2023
- 资助金额:
$ 22.55万 - 项目类别:
Integrative approaches defining the ontogeny, maintenance, and immune response dynamics of marginal-zone B cells
定义边缘区 B 细胞个体发育、维持和免疫反应动力学的综合方法
- 批准号:
10660534 - 财政年份:2023
- 资助金额:
$ 22.55万 - 项目类别:
Bayesian integrative approaches for high-dimensional imaging and genomic data
高维成像和基因组数据的贝叶斯综合方法
- 批准号:
RGPIN-2019-04810 - 财政年份:2022
- 资助金额:
$ 22.55万 - 项目类别:
Discovery Grants Program - Individual