Bayesian approaches for missingness and causality in cancer and behavior studies

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，人们花费了更多的精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t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。另一个重要的问题是如何定义和识别干预措施对半竞争性风险的因果影响。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。我们将分发这些方法的代码（通过PI的网页），以确保研究者在自己的研究中可以随时使用这些方法。 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。

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.