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Spline-based self-controlled case series method

Ghebremichael-Weldeselassie, Yonas; Whitaker, Heather J. and Farrington, C. Paddy (2017). Spline-based self-controlled case series method. Statistics in Medicine (Early Access).

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DOI (Digital Object Identifier) Link: https://doi.org/10.1002/sim.7311
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Abstract

The self-controlled case series (SCCS) method is an alternative to study designs such as cohort and case control methods and is used to investigate potential associations between the timing of vaccine or other drug exposures and adverse events. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounding variables that could modify the true association between exposure and adverse event. Time-varying confounders such as age, on the other hand, are not automatically controlled and must be allowed for explicitly. The original SCCS method used step functions to represent risk periods (windows of exposed time) and age effects. Hence, exposure risk periods and/or age groups have to be prespecified a priori, but a poor choice of group boundaries may lead to biased estimates. In this paper, we propose a nonparametric SCCS method in which both age and exposure effects are represented by spline functions at the same time. To avoid a numerical integration of the product of these two spline functions in the likelihood function of the SCCS method, we defined the first, second, and third integrals of I-splines based on the definition of integrals of M-splines. Simulation studies showed that the new method performs well. This new method is applied to data on pediatric vaccines.

Item Type: Journal Item
Copyright Holders: 2017 John Wiley & Sons Ltd
ISSN: 0277-6715
Project Funding Details:
Funded Project NameProject IDFunding Body
Software tools and online resources for the self-controlled case series method and its extensionsMR/L009005/1Medical research council
Keywords: integral of I-splines; M-splines; nonparametric SCCS; smooth risk functions
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 49385
Depositing User: Heather Whitaker
Date Deposited: 12 May 2017 10:50
Last Modified: 20 Jun 2017 10:42
URI: http://oro.open.ac.uk/id/eprint/49385
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