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

• 批准号: 9041551
• 负责人: Michael J Daniels
• 金额: $ 12.35万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-18 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9041551
• 关键词: Address; Bayesian Analysis; Bayesian Method; Behavior Therapy; Behavioral; Behavioral trial; Biomedical Research; Cations; 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; novel; novel strategies; semiparametric; smoking cessation; symposium; treatment effect; web page; web site

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 e ort 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 t 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 de ne 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。我们将解决在最近的美国国家科学院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，包括评估观察数据的模型 t、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 和统计学家共同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。