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Self-controlled case series with multiple event types

Author

Listed:
  • Ghebremichael-Weldeselassie, Yonas
  • Whitaker, Heather J.
  • Douglas, Ian J.
  • Smeeth, Liam
  • Farrington, C. Paddy

Abstract

Self-controlled case series methods for events that may be classified as one of several types are described. When the event is non-recurrent, the different types correspond to competing risks. It is shown that, under circumstances that are likely to arise in practical applications, the SCCS multi-type likelihood reduces to the product of the type-specific likelihoods. For recurrent events, this applies whether or not the marginal type-specific counts are dependent. As for the standard SCCS method, a rare disease assumption is required for non-recurrent events. Several forms of this assumption are investigated by simulation. The methods are applied to data on MMR vaccine and convulsions (febrile and non-febrile), and to data on thiazolidinediones and fractures (at different sites).

Suggested Citation

  • Ghebremichael-Weldeselassie, Yonas & Whitaker, Heather J. & Douglas, Ian J. & Smeeth, Liam & Farrington, C. Paddy, 2017. "Self-controlled case series with multiple event types," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 64-72.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:64-72
    DOI: 10.1016/j.csda.2016.10.010
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    References listed on IDEAS

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    1. 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.
    2. S. Rabe-Hesketh & A. Skrondal & H. K. Gjessing, 2008. "Biometrical Modeling of Twin and Family Data Using Standard Mixed Model Software," Biometrics, The International Biometric Society, vol. 64(1), pages 280-288, March.
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