BAYESIAN APPROACHES FOR MISSINGNESS AND CAUSALITY IN CANCER AND BEHAVIOR STUDIES

癌症和行为研究中缺失和因果关系的贝叶斯方法

基本信息

批准号：
9437722
负责人：
Michael J Daniels
金额：
$ 29万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-01 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9437722
关键词：
Address Affect Bayesian Analysis Bayesian Method Behavior Therapy Behavioral Mechanisms Behavioral trial Biomedical Research Cessation of life Clinical Clinical Research Clinical Trials Code Collaborations Collection Complex Data Development Ensure Equation Etiology Event Future Grant Health Individual Intervention Joints Literature Malignant Neoplasms Malignant neoplasm of brain Manuscripts Mediating Mediation Mediator of activation protein Methodology Methods Modeling Outcome Process Public Health Publications Recording of previous events Reporting Research Personnel Risk Science Time Translations Treatment Effectiveness Uncertainty United States National Academy of Sciences Weight maintenance regimen Work Writing base behavior change behavioral study computer code conditioning direct application experience follow-up improved innovation interest intervention effect mortality risk novel novel strategies public health relevance semiparametric smoking cessation symposium treatment effect web page web site

项目摘要

DESCRIPTION (provided by applicant): This proposal will develop novel Bayesian approaches to handle missingness and conduct causal inference for important problems in biomedical research with particular relevance to cancer and behavioral studies. Missing data is a major problem in clinical studies. Of late, more effort is spent to try to minimize the amount of missingness, but it remains a problem. We will address several pressing complications in the analysis of incomplete data in clinical settings as documented in a recent National Academies of Science report, including assessing model fit to the observed data, developing Bayesian approaches for auxiliary covariates, and nonparametric modeling of nonignorable missingness. The mechanisms of treatment effectiveness are of particular interest in behavioral trials. Specifically, how do different processes mediate the effect of an intervention? This can facilitate constructing future interventions. However, determining the causal effect of such 'mediators' on the outcomes is difficult. We will develop new approaches to identify these effects in complex settings with multiple mediators and longitudinal mediators for which little work has been done. Another important question is how to define and identify causal effects of interventions on outcomes in the setting of semi-competing risks. Semi-competing risks occur in studies where a progression endpoint may be pre-empted by death or censored due to loss to follow-up or study termination. Subjects who experience a progression event are also followed for survival, which may be censored. Data of this form has been termed semi-competing risks data. This paradigm is particularly relevant to certain brain cancer trials, where the semi-competing risks are death and cerebellar progression. For all these settings, a Bayesian approach is ideal as it allows one to appropriately characterize uncertainty about invariable assumptions (which are present in all these problems). The methods developed here will help answer numerous important clinical questions including the mechanisms of behavior change, both in weight management and smoking cessation, via the ability to appropriately assess mediation, and the joint causal effect of treatment on time to death and cerebellar progression in brain cancer. We will disseminate code for these methods (via the PI's webpage) to ensure the methods will be readily usable by investigators in their own studies. The history of the PI's collaboration with the PI's of the individual clinical studies and the statistician co-investigators will help the team produce the best science and facilitate dissemination of our clinical findings and new methods to the appropriate audience via both subject matter publications and presentations at relevant conferences.

描述（由申请人提供）：该提案将开发新颖的贝叶斯方法来处理缺失，并对生物医学研究中的重要问题（特别是与癌症和行为研究相关的问题）进行因果推断。数据缺失是临床研究中的一个主要问题。最近，人们花费了更多的努力来尽量减少缺失的数量，但这仍然是一个问题。我们将解决美国国家科学院最近的一份报告中记录的临床环境中不完整数据分析中的几个紧迫的并发症，包括评估模型与观察数据的拟合度、开发辅助协变量的贝叶斯方法以及不可忽略缺失的非参数建模。治疗效果的机制在行为试验中特别令人感兴趣。具体来说，不同的过程如何调节干预的效果？这可以方便构建未来的干预措施。然而，确定此类“中介因素”对结果的因果影响很困难。我们将开发新的方法来识别复杂环境中的这些影响，其中包括多个中介和纵向中介，而这方面的工作还很少。另一个重要问题是如何定义和识别半竞争风险背景下干预措施对结果的因果影响。半竞争风险发生在进展终点可能因死亡而提前或因失访或研究终止而被审查的研究中。经历进展事件的受试者也会被追踪生存情况，这可能会被审查。这种形式的数据被称为半竞争风险数据。这种范例与某些脑癌试验特别相关，其中半竞争风险是死亡和小脑进展。对于所有这些设置，贝叶斯方法是理想的，因为它允许人们适当地描述不变假设（存在于所有这些问题中）的不确定性。这里开发的方法将有助于回答许多重要的临床问题，包括体重管理和戒烟中行为改变的机制，通过适当评估中介的能力，以及治疗对死亡时间和大脑小脑进展的联合因果效应癌症。我们将传播这些方法的代码（通过 PI 的网页），以确保研究人员可以在自己的研究中轻松使用这些方法。 PI 与各个临床研究的 PI 和统计学家联合研究者的合作历史将帮助团队产生最好的科学成果，并通过主题出版物和演示文稿促进我们的临床发现和新方法向适当的受众传播。相关会议。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Note on Monotonicity in Repeated Attempt Selection Models.

关于重复尝试选择模型中单调性的注释。

DOI：
发表时间：
2020-01
期刊：
影响因子：
0.8
作者：
Park, Seunghwan;Daniels, Michael J
通讯作者：
Daniels, Michael J

A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies with Auxiliary Covariates.

具有辅助协变量的纵向研究中的半参数贝叶斯方法。

DOI：
发表时间：
2020
期刊：
影响因子：
0
作者：
Zhou, Tianjian;Daniels, Michael J;Müller, Peter
通讯作者：
Müller, Peter

Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death.

在 MNAR 退出和死亡队列研究中使用贝叶斯半参数 G 计算进行因果推断。

DOI：
发表时间：
2021-03
期刊：
影响因子：
0
作者：
Josefsson, Maria;Daniels, Michael J
通讯作者：
Daniels, Michael J

A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout.

一种贝叶斯半参数方法，用于从带有丢失的纵向数据推断总体部分条件均值。

DOI：
发表时间：
2023-04-14
期刊：
影响因子：
0
作者：
Josefsson, Maria;Daniels, Michael J;Pudas, Sara
通讯作者：
Pudas, Sara

A Bayesian parametric approach to handle missing longitudinal outcome data in trial-based health economic evaluations.

一种贝叶斯参数方法，用于处理基于试验的健康经济评估中缺失的纵向结果数据。

DOI：
发表时间：
2020-02
期刊：
影响因子：
0
作者：
Gabrio, Andrea;Daniels, Michael J;Baio, Gianluca
通讯作者：
Baio, Gianluca

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Michael J Daniels其他文献

An Exploration of Fixed and Random Effects Selection for Longitu- Dinal Binary Outcomes in the Presence of Non-ignorable Dropout 3.2 Variable Selection in Missing Data Mechanism 4 Simulation Studies

不可忽略丢失情况下纵向二元结果的固定和随机效应选择的探索 3.2 缺失数据机制中的变量选择 4 模拟研究

DOI：
发表时间：
期刊：
影响因子：
0
作者：
Ning Li;Michael J Daniels;Gang Li;R. Elashoff
通讯作者：
R. Elashoff

Dietary assessment and estimation of intakedensitiesMichael

膳食评估和摄入密度估计Michael

DOI：
发表时间：
1999
期刊：
影响因子：
0
作者：
Michael J Daniels;A. Carriquiry
通讯作者：
A. Carriquiry

Extent of aortic coverage and incidence of spinal cord ischemia after thoracic endovascular aneurysm repair.

胸主动脉瘤腔内修复术后主动脉覆盖范围和脊髓缺血的发生率。

DOI：
发表时间：
2008
期刊：
Annals of Thoracic Surgery
影响因子：
4.6
作者：
R. Feezor;T. Martin;P. Hess;Michael J Daniels;T. Beaver;C. Klodell;W. A. Lee
通讯作者：
W. A. Lee

Effects of an Intervention to Increase Bed Alarm Use to Prevent Falls in Hospitalized Patients

增加床报警器使用以预防住院患者跌倒的干预措施的效果

DOI：
发表时间：
2012
期刊：
Annals of Internal Medicine
影响因子：
39.2
作者：
R. Shorr;A. Chandler;L. Mion;T. Waters;Minzhao Liu;Michael J Daniels;L. Kessler;Stephen T. Miller
通讯作者：
Stephen T. Miller