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，花费了更多的时间来尝试最大程度地减少失踪程度，但仍然是一个问题。我们将在分析临床环境中不完整数据的分析中，如最近的一份国家科学报告中所记录的，包括评估观察到的数据的模型T，开发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的研究人员将帮助团队通过相关会议的主题出版物和演讲来提供最佳科学，并促进我们的临床发现和新方法传播给适当的受众。