Theory and methods for sufficient cause interactions

描述（由申请人提供）：拟定研究的目的是开发用于测试充分原因相互作用的理论和方法。这些方法将有助于识别生物系统中的机制相互作用，并在基因-基因和基因-环境相互作用的遗传流行病学研究的分析和解释。众所周知，统计模型中相互作用的存在取决于所采用的模型，而且统计相互作用不需要对应于任何生物学或物理学意义上的相互作用。充分原因框架指的是导致结果的实际因果机制，即充分原因。当两个或两个以上的二元原因参与同一因果机制时，协同作用被称为存在。有时协同作用不能从数据中识别;当数据确实暗示协同作用必须存在时，则认为存在充分的原因相互作用。通过拟议的研究开发的理论和方法，导致足够的原因相互作用的实证检验，从而构成一个单一的因果机制中的两个或两个以上的原因的联合存在的测试。研究的目的是扩展理论的充分原因框架二分法曝光，充分原因互动的有序和分类曝光的理论，开发多个强大的半参数检验的存在下，充分原因的相互作用，并表征这些形式的曝光错误分类的测试充分原因的相互作用产生有效的结论。该研究将提供一套可用于识别生物系统中机械相互作用的技术，并将开发一个理论框架来概念化这些机械相互作用，并提供经验测试此类相互作用的方法。该研究将有助于确定基因-基因和基因-环境相互作用的机制，从而增加我们对遗传机制的理解。充分的原因相互作用的研究，了解标准的基因-基因和基因-环境相互作用的测试和研究设计的机械影响的影响将被探讨和开发的方法将被应用到几个数据集的砷的健康影响纵向研究。这项研究将取得重要进展的统计文献的概念的相互作用和测量误差的因果推理的影响。整体研究计划将有助于我们理解因果关系的概念，这些概念构成了因果推断中统计文献的基础，并被应用于医学，流行病学，心理学，遗传学，计算机科学，哲学，社会学，教育和经济学。 公共卫生相关性：在拟议的研究中开发的统计方法将是有用的，在确定生物系统中的机制相互作用，并在基因-基因和基因-环境相互作用的遗传流行病学研究的分析和解释。这些方法将应用于砷对健康影响纵向研究的数据，以提供有关砷暴露可能导致疾病的潜在病理生理学和机制的知识。这项研究将取得重要进展的统计文献的概念的相互作用和测量误差的因果推理的影响。

DESCRIPTION (provided by applicant): The objective of the proposed research is to develop theory and methods for testing for sufficient cause inter- actions. The methods will be useful in identifying mechanistic interactions in biological systems and in and in the analysis and interpretation of studies in genetic epidemiology of gene-gene and gene-environment interactions. It is well known both that the presence of an interaction in a statistical model depends on the model being employed and furthermore that a statistical interaction need not correspond to an interaction in any biologically or physically meaningful sense. The sufficient cause framework makes reference to the actual causal mechanisms, referred to as sufficient causes, involved in bringing about the outcome. When two or more binary causes participate in the same causal mechanism, synergism is said to be present. Sometimes synergism cannot be identified from data; when data do imply that synergism must be present then a sufficient cause interaction is said to be present. The theory and methods developed through the proposed research lead to empirical tests for sufficient cause interactions and thus constitute tests for the joint presence of two or more causes in a single causal mechanism. The aims of the research are to extend the theory concerning the sufficient cause framework for dichotomous exposures, to develop theory for sufficient cause interaction for ordinal and categorical exposures, to develop multiply robust semiparametric tests for the presence of sufficient cause interactions, and to characterize those forms of exposure misclassification for which tests for sufficient cause interactions yield valid conclusions. The research will provide a set of techniques that can be used to identify mechanistic interactions in biological systems and will develop both a theoretical framework in which to conceptualize these mechanistic interactions and provide methods to empirically test for such interactions. The research will be useful in identifying mechanistic gene-gene and gene-environment interactions which could increase our understanding of genetic mechanisms. The implications of the research on sufficient cause interactions for understanding the mechanistic implications of standard gene-gene and gene-environment interaction tests and study designs will be explored and the methods developed will be applied to several data sets in the Health Effects of Arsenic Longitudinal Study. The research will make important advances to the statistical literature on the concept of interaction and on the implications of measurement error for causal inference. The overall research program will contribute to our understanding of the concepts of causation which form the foundation of the statistical literature in causal inference and which are being employed in medicine, epidemiology, psychology, genetics, computer science, philosophy, sociology, education and economics. PUBLIC HEALTH RELEVANCE: The statistical methodology developed in the proposed research will be useful in identifying mechanistic interactions in biological systems and in the analysis and interpretation of studies in genetic epidemiology of gene-gene and gene-environment interactions. The methods will be applied to data in the Health Effects of Arsenic Longitudinal Study in order to provide knowledge about the underlying pathophysiology and mechanisms by which arsenic exposure may lead to diseases. The research will make important advances to the statistical literature on the concept of interaction and on the implications of measurement error for causal inference.