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.

DOI: https://doi.org/10.1016/j.csda.2007.06.016

Abstract

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.

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About

• Item ORO ID
• 22533
• Item Type
• Journal Item
• ISSN
• 0167-9473
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  CASE0307	Not Set	EPSRC (Engineering and Physical Sciences Research Council)
  Not Set	Not Set	GlaxoSmithKline Biologicals
  Not Set	070346	Wellcome Trust

• Keywords
• asymptotic bias; asymptotic variance; bootstrap; randomization test; self-controlled case series method; simulation; small-sample performance
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2007 Elsevier B.V.
• Depositing User
• Sarah Frain