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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）：该提案将开发新的贝叶斯方法来处理缺失，并对生物医学研究中与癌症和行为研究特别相关的重要问题进行因果推断。缺失数据是临床研究中的一个主要问题。最近，人们花了更多的努力来尽量减少遗漏的数量，但这仍然是一个问题。我们将解决几个紧迫的并发症，在临床环境中的不完整数据的分析记录在最近的美国国家科学院的报告，包括评估模型拟合观察到的数据，辅助协变量的贝叶斯方法的发展，和非参数建模的不可重复的缺失。治疗有效性的机制在行为试验中特别感兴趣。具体来说，不同的过程如何调节干预的效果？这可以促进构建未来的干预措施。然而，确定因果关系的影响，这样的“调解人”的结果是困难的。我们将开发新的方法来确定这些影响在复杂的设置与多个介质和纵向介质的工作很少已经完成。另一个重要问题是如何界定和确定在半竞争性风险的背景下干预措施对结果的因果影响。半竞争性风险发生在进展终点可能因死亡或因失访或研究终止而被删失的研究中。还对发生进展事件的受试者进行生存期随访，可能进行删失。该表的数据被称为半竞争性风险数据。这种模式与某些脑癌试验特别相关，其中半竞争性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关于不变假设（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，包括行为改变的机制，无论是在体重管理和戒烟，通过适当评估调解的能力，以及治疗对死亡时间和脑癌小脑进展的联合因果效应。我们将传播这些方法的代码（通过PI的网页），以确保研究者在自己的研究中可以轻松使用这些方法。 PI与各临床研究的PI和统计学家合作研究者的合作历史将有助于团队产生最佳科学，并促进通过主题出版物和相关会议上的演示向适当受众传播我们的临床发现和新方法。

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

从辍学的纵向数据中推断人口部分平均值的贝叶斯半参数方法。

DOI：
10.1093/biostatistics/kxab012
发表时间：
2023-04-14
期刊：
Biostatistics (Oxford, England)
影响因子：
0
作者：
通讯作者：

Handling Missing Data in Instrumental Variable Methods for Causal Inference.

处理因果推理工具变量方法中的缺失数据。

DOI：
10.1146/annurev-statistics-031017-100353
发表时间：
2019
期刊：
Annual review of statistics and its application
影响因子：
7.9
作者：
Kennedy,EdwardH;Mauro,JacquelineA;Daniels,MichaelJ;Burns,Natalie;Small,DylanS
通讯作者：
Small,DylanS

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：
10.1016/j.spl.2019.108585
发表时间：
2020
期刊：
Statistics & probability letters
影响因子：
0.8
作者：
Park,Seunghwan;Daniels,MichaelJ
通讯作者：
Daniels,MichaelJ

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

DOI：
10.1080/10618600.2019.1617159
发表时间：
2020
期刊：
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
影响因子：
0
作者：
Zhou T;Daniels MJ;Müller P
通讯作者：
Müller P

A Bayesian nonparametric approach to causal inference on quantiles.

DOI：
10.1111/biom.12863
发表时间：
2018-09
期刊：
Biometrics
影响因子：
1.9
作者：
Xu D;Daniels MJ;Winterstein AG
通讯作者：
Winterstein AG

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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

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