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

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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.