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Zusatztext "As an epidemiologist who has worked for some 40 years in the field of vaccine safety I have first-hand experience of the huge impact that the innovative self –controlled case series method developed in the mid-1990s by Professor Paddy Farrington has had in this field. The SCCS method enabled researchers such as me to rapidly conduct robust studies to assess whether there was an increased risk of a particular adverse event after a vaccine without the need to use the more laborious and bias-prone case control and cohort method methods. The SCCS has wider application than just studies of vaccine safety and is now an essential component of the methodological tool kit available to epidemiologists interested in assessing risks, particularly those that are short term and potentially related to an intervention such as vaccination, environmental exposure such as an infection or life style activity such as strong exercise. This well-written book should be on the shelves of all public health institutions and required reading for any aspiring statistician."—Professor Elizabeth Miller, Public Health England"'Self-controlled Case Series Studies: A Modelling Guide with R’ is essential reading for anyone who has ever used or is contemplating using the SCCS design in their research. This informative book provides a clear and concise commentary on the SCCS method, its initial use in vaccine safety studies and its eventual foray into medication safety studies. The text delves into the importance of the assumptions of the method and critically, describes what you can do when those assumptions are not met. The inclusion of elegant R code throughout and description of the SSCS package provides everything researchers need to know to implement their own studies, check their assumptions and interpret their results. This book will help to solidify researchers understanding of the SCCS method and will surely motivate further methodological developments to generate robust evidence of the safety of medicines and vaccines in the real world."—Nicole Pratt, Associate Professor, University of South Australia"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 important approach to overcoming between-person confounding that is increasingly widely used to assess the effects of health related exposures. This book provides an invaluable practical guide to implementing the approach in R. As well as detailed guidance, the book covers the assumptions and limitations of the approach, and will thus help ensure the validity of resulting analyses. I thoroughly recommend it to anyone planning to use the case series approach."—Liam Smeeth, Professor of Clinical Epidemiology, London School of Hygiene and Tropical Medicine"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 compr...
