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oapen-20.500.12657-902632024-05-17T02:21:17Z Self-Controlled Case Series Studies Farrington, Paddy Whitaker, Heather Ghebremichael-Weldeselassie, Yonas Relative Incidence;SCCS Method;case-control studies;SCCS;cohort studies;Risk Period;epidemiology;Time Invariant Covariates;exposure;MMR Vaccine;vaccinations;MMR Vaccination;drug reactions;Monte Carlo Standard Error;Heather Whitaker;Time Invariant Confounders;Yonas Ghebremichael Weldeselassie;Non-homogeneous Poisson Process;Case Crossover Method;Primary Time Line;Smoothing Parameter;Asymptotic Relative Efficiency;Hib Vaccine;Hexavalent Vaccines;Spline Model;Sample Size Formula;Data Sets thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine::MBNS Epidemiology and Medical statistics 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. 2024-05-16T09:25:26Z 2024-05-16T09:25:26Z 2018 book 9781032095530 9780429957512 9780429957529 9780429491313 9781498781596 https://library.oapen.org/handle/20.500.12657/90263 eng Chapman & Hall/CRC Biostatistics Series application/pdf Attribution-NoDerivatives 4.0 International 9780429957536.pdf Taylor & Francis Chapman and Hall/CRC 10.1201/9780429491313 10.1201/9780429491313 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb 9781032095530 9780429957512 9780429957529 9780429491313 9781498781596 Chapman and Hall/CRC 377 open access
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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.
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