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Self-controlled case series method: small sample performance

Musonda, Patrick; Hocine, Mounia N.; Whitaker, Heather J. and Farrington, C. Paddy (2008). Self-controlled case series method: small sample performance. Computational Statistics and Data Analysis, 52(4) pp. 1942–1957.

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Second-order expressions for the asymptotic bias and variance of the log relative incidence estimator are derived for the self-controlled case series model in a simplified scenario. The dependence of the bias and variance on factors such as the relative incidence and ratio of risk to observation period are studied. Small-sample performance of the estimator in realistic scenarios is investigated using simulations. It is found that, in scenarios likely to arise in practice, asymptotic methods are valid for numbers of cases in excess of 20–50 depending on the ratio of the risk period to the observation period and on the relative incidence. The application of Monte Carlo methods to self-controlled case series analyses is also discussed.

Item Type: Journal Item
Copyright Holders: 2007 Elsevier B.V.
ISSN: 0167-9473
Project Funding Details:
Funded Project NameProject IDFunding Body
CASE0307Not SetEPSRC (Engineering and Physical Sciences Research Council)
Not SetNot SetGlaxoSmithKline Biologicals
Not Set070346Wellcome Trust
Keywords: asymptotic bias; asymptotic variance; bootstrap; randomization test; self-controlled case series method; simulation; small-sample performance
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 22533
Depositing User: Sarah Frain
Date Deposited: 18 Aug 2010 10:21
Last Modified: 07 Dec 2018 12:43
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