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Semiparametric analysis of case series data

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  • C. P. Farrington
  • H. J. Whitaker

Abstract

Summary. The case series model for estimating the association between an age‐dependent exposure and an outcome event requires information only on cases and implicitly adjusts for all age‐independent multiplicative confounders, while allowing for an age‐dependent base‐line incidence. In the paper the model is presented in greater generality than hitherto, including more general discussion of its derivation, underlying assumptions, applicability, limitations and efficiency. A semiparametric version of the model is developed, in which the age‐specific relative incidence is left unspecified. Modelling covariate effects and testing assumptions are discussed. The small sample performance of this model is studied in simulations. The methods are illustrated with several examples from epidemiology.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:5:p:553-594
    DOI: 10.1111/j.1467-9876.2006.00554.x
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    Cited by:

    1. Stephen Senn & Dipti Amin & Rosemary A. Bailey & Sheila M. Bird & Barbara Bogacka & Peter Colman & Andrew Garrett & Andrew Grieve & Peter Lachmann, 2007. "Statistical issues in first‐in‐man studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 517-579, July.
    2. Musonda, Patrick & Hocine, Mounia N. & Whitaker, Heather J. & Farrington, C. Paddy, 2008. "Self-controlled case series analyses: Small-sample performance," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1942-1957, January.
    3. 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.
    4. C.-Y. Huang & J. Qin & M.-C. Wang, 2010. "Semiparametric Analysis for Recurrent Event Data with Time-Dependent Covariates and Informative Censoring," Biometrics, The International Biometric Society, vol. 66(1), pages 39-49, March.
    5. 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.
    6. 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.
    7. Olli Saarela & James A. Hanley, 2015. "Case-base methods for studying vaccination safety," Biometrics, The International Biometric Society, vol. 71(1), pages 42-52, March.
    8. Mounia N. Hocine & Patrick Musonda & Nick J. Andrews & C. Paddy Farrington, 2009. "Sequential case series analysis for pharmacovigilance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 213-236, January.

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