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Within‐individual dependence in self‐controlled case series models for recurrent events

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  • C. Paddy Farrington
  • Mounia N. Hocine

Abstract

Summary. The self‐controlled case series model may be used to analyse recurrent events when event times are conditionally independent given fixed or random individual effects. To test the hypothesis of within‐individual independence, the model is augmented by an association parameter for diagonal dependence, which provides the focus for a test of independence. Estimation methods are described, and simulations are presented to illustrate the power of the method in relevant scenarios, and to quantify the bias resulting from failure of the independence assumption. The methods are applied to two data sets, relating to a rare bleeding disorder and to myocardial infarction.

Suggested Citation

  • C. Paddy Farrington & Mounia N. Hocine, 2010. "Within‐individual dependence in self‐controlled case series models for recurrent events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 457-475, May.
    • Handle: RePEc:bla:jorssc:v:59:y:2010:i:3:p:457-475
    DOI: 10.1111/j.1467-9876.2009.00703.x
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    References listed on IDEAS

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    1. M. C. Jones & P. V. Larsen, 2004. "Multivariate distributions with support above the diagonal," Biometrika, Biometrika Trust, vol. 91(4), pages 975-986, December.
    2. C. P. Farrington & H. J. Whitaker, 2006. "Semiparametric analysis of case series data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(5), pages 553-594, November.
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    Cited by:

    1. Shawn E. Simpson & David Madigan & Ivan Zorych & Martijn J. Schuemie & Patrick B. Ryan & Marc A. Suchard, 2013. "Multiple Self-Controlled Case Series for Large-Scale Longitudinal Observational Databases," Biometrics, The International Biometric Society, vol. 69(4), pages 893-902, December.
    2. Shawn E. Simpson, 2013. "A Positive Event Dependence Model for Self-Controlled Case Series with Applications in Postmarketing Surveillance," Biometrics, The International Biometric Society, vol. 69(1), pages 128-136, March.

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