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）：本提案将发展新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进行因果推理，特别是与癌症和行为研究相关的问题。数据缺失是临床研究中的一个主要问题。最近，人们花了更多的精力来尽量减少失踪者的数量，但这仍然是一个问题。我们将解决临床环境中不完整数据分析中的几个紧迫问题，如最近美国国家科学院的一份报告所记载的，包括对观察数据的模型t进行评估，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有趣。具体来说，不同的过程是如何调节干预的效果的？这可以促进