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）：该提案将开发出新颖的贝叶斯方法来处理失踪性并为生物医学研究中的重要问题进行因果推断，并与癌症和行为研究特别相关。缺少数据是临床研究中的主要问题。最近，花费了更多的精力来尝试最大程度地减少失踪程度，但这仍然是一个问题。我们将在分析临床环境中不完整数据的分析中解决一些紧迫的并发症，如最近的一份国家科学报告中所记录的，包括评估拟合观察到的数据的模型，开发了用于辅助协变量的贝叶斯方法以及对不可差异遗失的非参数建模。在行为试验中，治疗有效性的机制特别感兴趣。具体来说，不同的过程如何介导干预的效果？这可以促进构建未来的干预措施。但是，确定这种“介体”对结果的因果影响很困难。我们将开发新的方法，以在复杂的环境中与多个调解人和纵向调解人一起识别这些效果。另一个重要的问题是如何在半竞争风险的情况下定义和确定干预措施对结果的因果影响。半竞争风险发生在研究终点可能会因死亡而被审查或审查导致随访或研究终止而受到审查的研究。还遵循了经历进步事件的受试者的生存，这可能会受到审查。该表格的数据已称为半竞争风险数据。该范式与某些脑癌试验特别相关，在某些脑癌试验中，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选择，因为它允许人们适当地表征有关不变假设的不确定性（所有这些问题中都存在）。此处开发的方法将有助于回答许多重要的临床问题，包括通过适当评估调解的能力以及治疗对时间对死亡时间和小脑在脑癌中的治疗的关节因果作用，包括体重管理和戒烟的行为改变机制。我们将为这些方法（通过PI的网页）传播代码，以确保研究人员在自己的研究中很容易使用这些方法。 PI与个人临床研究的PI合作的历史以及统计学家共同研究人员将通过相关会议上的主题出版物和演讲来帮助团队制作最佳科学，并促进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.

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