Self-Controlled Case Series Studies

This book provides the first comprehensive account of the self-controlled case series (SCCS) method, a statistical method for investigating associations between outcome events and time-varying exposures.

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.

Autorentext

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.

Klappentext

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.

Inhalt

1. Introduction. 2. Epidemiological overview. 3. The SCCS likelihood. 4. The standard SCCS model 5. Checking model assumptions 6. Further SCCS models. 7. Extensions of the SCCS model. 8. Design and presentation of SCCS studies.