Theory and methods for sufficient cause interactions

描述（由申请人提供）：拟议研究的目的是发展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，以及分析和解释基因-基因和基因-环境相互作用的遗传流行病学研究。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必对应于任何生物学或物理意义上的相互作用。充分原因框架参考了实际的因果机制，被称为充分原因，涉及到产生结果。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。有时不能从数据中确定协同作用；当数据确实暗示协同作用必须存在时，就说存在一个充分的原因相互作用。通过拟议的研究发展的理论和方法导致对充分原因相互作用的经验检验，从而构成对单一因果机制中两个或多个原因共同存在的检验。研究的目的是扩展关于二分类暴露的充分原因框架的理论，发展有序和分类暴露的充分原因相互作用的理论，为充分原因相互作用的存在开发多个强大的半参数测试，并表征那些暴露错误分类的形式，其中充分原因相互作用的测试产生有效的结论。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，在该框架中概念化这些机械相互作用，并提供对此类相互作用进行经验测试的方法。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认识。研究充分原因相互作用对理解标准基因-基因和基因-环境相互作用测试和研究设计的机制影响的影响将被探索，所开发的方法将应用于砷对健康影响纵向研究中的几个数据集。该研究将对相互作用概念和测量误差对因果推理的影响的统计文献取得重要进展。整个研究计划将有助于我们对因果关系概念的理解，这些概念构成了因果推理统计文献的基础，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。

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.