III: Medium: Meta-analysis reinterpreted using causal graphs

由于各种原因，包括研究的样本量小或研究人员不知道的混杂因素，研究的统计结论往往会产生误导。减少误导性结论可能性的一种方法是使用一种称为“荟萃分析”的技术将多项研究的结果结合起来。元分析是从科学数据中推断知识的最广泛使用的技术之一。荟萃分析研究背后的理念是，来自多个研究的综合统计结论反映了所有研究中的信息，并且更有可能是准确的。荟萃分析的结论被认为比单一研究的结论“更好”或“更有可能推广”。然而，这个概念还没有很好地形式化，而形式化这个问题是这个项目的目标。此外，现有的荟萃分析方法没有考虑到研究之间的相似性和差异性。利用这些异同可以提高meta分析的有效性。该项目利用了“因果推理”领域的最新发展，这是一项从数据中推断因果关系的研究。这些类型的推论利用一种称为因果图的图形来表示因果关系。本项目基于一种新型的因果图，即选择图，开发了一种元分析的替代框架。选择图正式表示研究之间的相似点和不同点。这个项目为元分析提供了一个统一的框架和强有力的方法论。在这个项目中开发的方法被应用到遗传研究中，在过去的几年里，荟萃分析已经发现了数千种与常见人类疾病有关的变异。因果图对认知科学、统计学、健康和社会科学中因果关系的教授和理解方式产生了重大影响。拟议的研究有望通过将方法转化为元分析（meta-analysis）来产生类似的影响，元分析是物理、生命和社会科学中统计推断的主力之一。由此产生的技术将用于对基因研究进行荟萃分析，从而发现与疾病有关的变异。该项目的成果，包括出版物、软件、数据集和课程材料将通过该项目的网址：http://zarlab.cs.ucla.edu/causal-meta-analysis/免费提供。

Statistical conclusions from research studies may often be misleading due to a variety of reasons including small sample sizes for the studies or confounding factors which are unknown to the investigators of the study. One way to reduce the possibility of misleading conclusions is to combine the results of multiple research studies using a technique referred to as "meta-analysis." Meta-analysis is one of the most widely used techniques to infer knowledge from data in science. The idea behind meta-analysis studies is that the combined statistical conclusions from multiple research studies reflect the information in all of the studies and are more likely to be accurate. The conclusions from meta-analyses are considered "better" or "more likely to generalize" than conclusions from single studies. However, this notion is not well formalized and formalizing this question is a goal of this project. In addition, existing meta-analysis methods do not take into account any knowledge of the similarities and differences between the studies. Taking advantage of these similarities and differences can improve the effectiveness of meta-analysis.This project takes advantage of recent developments in the area of "causal inference" which is the study inferring cause and effect relationships from data. These types of inferences utilizes a type of graph called a causal graph which graphically represents cause and effect relationships. This project develops an alternate framework for meta-analysis based on a novel type of causal graph, a selection graph. A selection graph formally represents the similarities and differences between the studies. This project provides a unifying framework and powerful powerful methodology for meta-analysis. The methods developed in this project are applied to genetic studies where meta-analyses have discovered thousands of variants involved in common human disease in the past few years.Causal graphs have had a major impact on the way causality is taught and understood in cognitive science, statistics, and the health and social sciences. The proposed research promises to have similar impacts by transforming the approach to meta-analysis, one of the work horses of statistical inference in the physical, life and social sciences. The resulting techniques will be used to perform meta-analyses of genetic studies which can lead to the discovery of variation involved in disease. The results of the project, including publications, software, data sets, and course materials will be made freely available through the project web site: http://zarlab.cs.ucla.edu/causal-meta-analysis/.