Software tools and online resources for the self-controlled case series method and its extensions

自我控制病例序列（SCCS）方法是一种统计技术，用于量化暴露（如药物）与结果（如可能与暴露相关或不相关的不良事件）之间的关联。该方法是一种替代标准技术，如队列研究和病例对照研究，通常用于量化这种关联。SCCS方法在某些情况下可能具有吸引力，因为与标准技术不同，它不会因研究期间固定的变量（例如遗传因素、社会经济背景或潜在健康状况）的混淆而产生偏差。当一个变量与暴露和结果相关联时，就会发生混淆，并扭曲了明显的兴趣关联。这种方法特别适合用于从大型数据库收集的数据，这些数据可能是由于与研究无关的原因而收集的，例如管理数据库的情况就是如此。在这类数据库中，可能无法获得关于重要混杂因素的信息，例如一个人是否吸烟、其社会经济背景或一般健康状况。这意味着在标准方法中不能考虑到这些变量的影响——而在SCCS方法中，这是自动实现的。SCCS方法在过去十年中越来越受欢迎，现在经常用于研究药物对人群的影响的医学领域，称为药物流行病学。然而，SCCS方法的优势是以强有力的假设为代价的，这些假设在任何特定环境中可能有效，也可能无效。在过去的几年里，SCCS方法的扩展已经被开发出来，以满足某些假设被违反的情况，同时仍然保留了自动控制固定混杂因素的基本特征。然而，与基本的SCCS方法相比，这些扩展中的大多数应用起来要复杂得多，并且在标准的商业或学术软件包中不可用。这极大地限制了主要专业知识不是统计的研究人员对SCCS方法的这些扩展的使用。该项目的目的是在这些软件包（特别是R， STATA和SAS）中提供程序和文档，使研究人员能够更容易地使用这些技术。我们的目标是提供全面的在线资源，包括软件程序，它们的应用示例，包括合适的数据，当假设不满足时可能出错的示例，以及描述如何运行程序的文档，以及其他人如何使用该方法的信息。所有材料和节目将免费提供。我们亦会按需要发展新方法，以填补现有方法的不足，并帮助用户在知情的情况下选择他们应该使用的SCCS模型。我们期望这些结果对从事药物流行病学工作的医学研究人员和应用统计学家，以及更广泛的流行病学工作有直接的帮助。我们迄今为基本SCCS方法提供的资源的经验表明，这些资源得到了很好的利用。我们期望这项工作的主要影响是提高用SCCS方法进行的研究的质量和数量，从而有助于提供更好的证据来支持医疗决策。

The self-controlled case series (SCCS) method is a statistical technique to quantify the association between an exposure, such as a drug, and an outcome, such as an adverse event that might or might not be related to the exposure. The method is an alternative to standard techniques such as cohort studies and case-control studies which are commonly used to quantify such associations.The SCCS method can be attractive in certain circumstances because, unlike standard techniques, it is not subject to bias due to confounding by variables that are fixed in time for the duration of the study (for example, genetic factors, socio-economic context, or underlying health condition). Confounding can occur when a variable is associated with both the exposure and the outcome, and distorts the apparent association of interest. The method is particularly attractive for use with data from large databases that may have been assembled for reasons unrelated to the study, as is the case with administrative databases, for example. In such databases, information on important confounders, such as whether a person smokes or not, or their socio-economic background, or general state of health, may not be available. This means that the effect of these variables can't be allowed for in standard methods - whereas in the SCCS method, this is achieved automatically.The SCCS method has gained in popularity over the past decade, and is now often used in the area of medicine that deals with the effects of pharmaceutical drugs in populations, known as pharmacoepidemiology. However, the advantages of the SCCS method come at a price in the form of strong assumptions, that may or may not be valid in any particular setting. Over the past years, extensions of the SCCS method have been developed to cater for situations in which some of these assumptions are violated - while still retaining the essential feature of controlling automatically for fixed confounders.However, most of these extensions are a lot more complicated to apply than the basic SCCS method, and are not available in standard commercial or academic software packages. This greatly limits the use of these extensions of the SCCS method by researchers whose primary expertise is not statistics. The aim of the project is to provide programs and documentation within such software packages (notably R, STATA and SAS) to enable researchers to make use of these techniques more readily.We aim to provide comprehensive online resources including software programs, examples of their application including suitable data, examples of what might go wrong when assumptions are not met, together with documentation to describe how to run the programs, and information on how others have used the method. All materials and programs will be provided free of charge. We will also develop new methodology, as required, to plug gaps in the methods available, and to help users make informed choices about the SCCS models they should use. We expect the results to be of direct use to medical researchers and applied statisticians working in pharmacoepidemiology, and in epidemiology more widely. Our experience with the resources we have so far made available for the basic SCCS method shows that they are well used. The major impact we expect from this work is to improve the quality and quantity of studies undertaken with the SCCS method, and hence to contribute to providing better evidence underpinning medical decisions.