IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v172y2009i1p213-236.html
   My bibliography  Save this article

Sequential case series analysis for pharmacovigilance

Author

Listed:
  • Mounia N. Hocine
  • Patrick Musonda
  • Nick J. Andrews
  • C. Paddy Farrington

Abstract

Summary. The self‐controlled case series method is used to evaluate drug safety, particularly the safety of paediatric vaccines with respect to rare adverse reactions. We propose a group sequential version of the method for prospective surveillance of drug safety. We focus on the surveillance of new vaccines. We develop methods that are based on the sequential probability ratio test applied at predetermined surveillance intervals, using both simple and composite alternative hypotheses. We investigate the properties of the methods analytically in a simple setting and by simulations in more realistic scenarios. The methods are applied to data on influenza vaccine and Bell's palsy, and to data on measles, mumps and rubella vaccine and bleeding disorders.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:1:p:213-236
    DOI: 10.1111/j.1467-985X.2008.00555.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-985X.2008.00555.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-985X.2008.00555.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Wenzheng Huang, 2004. "Stepwise likelihood ratio statistics in sequential studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 401-409, May.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. Rex Shen & Keran Moll & Ying Lu & Lu Tian, 2023. "A seasonality‐adjusted sequential test for vaccine safety surveillance," Biometrics, The International Biometric Society, vol. 79(4), pages 3533-3548, December.
    7. 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.
    8. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:172:y:2009:i:1:p:213-236. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.