Self-Controlled Case Series Studies: A Modelling Guide with R(Chapman & Hall/CRC Biostatistics Series)

"The self-controlled case series has emerged as a key methodology for studying the effects of healthcare interventions. The overall literature around the self-controlled case series has exploded in recent years and this important and timely book pulls it all together in an effective and clear manner. It certainly belongs on the shelf (or beside the keyboard) of every analyst conducting observational studies in healthcare." —David Madigan, Columbia University Self-Controlled Case Series Studies: A Modelling Guide with R provides the first comprehensive account of the self-controlled case series (SCCS) method, a statistical technique for investigating associations between outcome events and time-varying exposures. The method only requires information from individuals who have experienced the event of interest, and automatically controls for multiplicative time-invariant confounders, even when these are unmeasured or unknown. It is increasingly being used in epidemiology, most frequently to study the safety of vaccines and pharmaceutical drugs. Key features of the book include: A thorough yet accessible description of the SCCS method, with mathematical details provided in separate starred sections. Comprehensive discussion of assumptions and how they may be verified. A detailed account of different SCCS models, extensions of the SCCS method, and the design of SCCS studies. Extensive practical illustrations and worked examples from epidemiology. Full computer code from the associated R package SCCS, which includes all the data sets used in the book. The book is aimed at a broad range of readers, including epidemiologists and medical statisticians who wish to use the SCCS method, and also researchers with an interest in statistical methodology. The three authors have been closely involved with the inception, development, popularisation and programming of the SCCS method.

Table of Contents:
Introduction Epidemiological overview The SCCS likelihood The standard SCCS model Checking model assumptions Further SCCS models Extensions of the SCCS model Design and presentation of SCCS studies

About the Author :
Paddy Farrington worked for 11 years at the Immunisation Division and Statistics Unit of what is now Public Health England, where he developed the self-controlled case series method. He joined the Open University in 1998, and was appointed Professor of Statistics in 2004. In 2013 he was awarded the Royal Statistical Society’s Bradford Hill medal. He is now Professor Emeritus, having retired from the Open University in 2015. Heather Whitaker has worked at The Open University since 2002, first as a research fellow, then lecturer, and was appointed senior lecturer in 2015. She has worked on the self-controlled case series method since 2004, and has contributed to its popularisation and development. Yonas Ghebremichael Weldeselassie is a senior research fellow at Warwick University medical school. He obtained his PhD degree in statistics from the Open University in the UK in 2014 and his master degree in biostatistics from Hasselt University, Belgium in 2010. Dr Yonas has previously worked as an assistant lecturer of statistics at Mekelle University in Ethiopia, and as a research associate at the Open University.

Review :
"The self-controlled case series has emerged as a key methodology for studying the effects of healthcare interventions. Professor Farrington and his colleagues introduced the self-controlled case series and have led the way in developing an impressive array of extensions. The overall literature around the self-controlled case series has exploded in recent years and this important and timely book pulls it all together in an effective and clear manner. The book includes extensive practical examples with R code and would be an ideal text for a master’s or doctoral-level course in a statistics or epidemiology program. A particular strength of the book is its relentless focus on model assumptions and model checking. It certainly belongs on the shelf (or beside the keyboard) of every analyst conducting observational studies in healthcare." —David Madigan, Columbia University "The Self-controlled case series method is an increasingly popular analysis method in modern epidemiological research. This approach is particularly useful when time invariant confounding is difficult to capture as is very often the case in research using electronic health records. This book is written by the team that invented and pioneered the method and it is a comprehensive guide to its use. The book is aimed at both statisticians and epidemiologists. All the essential concepts are presented at a level understandable to the non-statistician while further statistical detail is provided in optional starred sections that can be skipped without loss of continuity. The authors cover both the theoretical underpinnings of the method and also the practical aspects of conducting an analysis in R using an accompanying software package. They also detail a range of methods to check that the method is valid for an individual study and some extensions to the method that can deal with some instances where the standard analysis is not valid. In short, this is an essential reference for any epidemiologist interested in using the self-controlled case series method." —Adrian Root, Academic General Practitioner, London School of Hygiene & Tropical Medicine "The self-controlled case series (SCCS) is one of the most important self-controlled designs in observational research, but has often been misunderstood and even misapplied. This book, written by the team that has led the development of the SCCS from the beginning, is an essential guide to all aspects related to this method. It leaves no stone unturned when it comes to the current state of the art of the SCCS method, and even includes developments that have not been published elsewhere. For epidemiologists, the book describes the rationale behind the SCCS, and the requirements that need to be met for appropriate use of the method. For statisticians, every detail of the statistics behind the SCCS is included (luckily in clearly marked sections that are optional for those seeking a higher-level understanding of the method). I especially appreciate the fact that each bit of theory is accompanied by several real-world examples, complete with functional R code." —Dr. Martijn Schuemie, Population-Level Estimation Workgroup leader, Observational Health Data Science and Informatics (OHDSI) "This is a much-anticipated first book on the self-controlled case series (SCCS) method. Written by the originator of the method and its main developers, it covers the basic principles of the design before going on to show how it can be adapted to assess associations in a wide range of case-only studies. The book provides rigorous detail of the method, its likelihood and properties, whilst frequently applying it to data based on real examples including vaccine and drug safety, as well as environmental exposures. Of particular value is the accompanying R code, which will allow readers to fit and plot a wide range of SCCS models, many of which were previously unavailable in standard statistical software. This is the SCCS "user manual" and will provide new insights to those familiar with the method as well as a starting point for those wishing to learn about it and apply it in their field." —Professor Nick Andrews, Public Health England "The authors have written the first authoritative book on SCCS studies that will be useful to a wide spectrum of audiences. The diversity of data analysis examples throughout the text together with the R package will make this book a great study and reference text for applied researchers and graduate students in statistics,biostatistics, and epidemiology. The carefully organized sections on the technical formulation of model development, estimation and inference procedures serve as important foundational materials for stimulating further research in SCCS studies forfuture generations of researchers." —Danh V. Nguyen, University of California, Irvine