IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v49y2000i1p19-30.html
   My bibliography  Save this article

Models for papilloma multiplicity and regression: applications to transgenic mouse studies

Author

Listed:
  • D. B. Dunson

Abstract

In cancer studies that use transgenic or knockout mice, skin tumour counts are recorded over time to measure tumorigenicity. In these studies cancer biologists are interested in the effect of endogenous and/or exogenous factors on papilloma onset, multiplicity and regression. In this paper an analysis of data from a study conducted by the National Institute of Environmental Health Sciences on the effect of genetic factors on skin tumorigenesis is presented. Papilloma multiplicity and regression are modelled by using Bernoulli, Poisson and binomial latent variables, each of which can depend on covariates and previous outcomes. An EM algorithm is proposed for parameter estimation, and generalized estimating equations adjust for extra dependence between outcomes within individual animals. A Cox proportional hazards model is used to describe covariate effects on the onset of tumours.

Suggested Citation

  • D. B. Dunson, 2000. "Models for papilloma multiplicity and regression: applications to transgenic mouse studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 19-30.
    • Handle: RePEc:bla:jorssc:v:49:y:2000:i:1:p:19-30
    DOI: 10.1111/1467-9876.00176
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9876.00176
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9876.00176?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Debajyoti Sinha & Tapabrata Maiti, 2004. "A Bayesian Approach for the Analysis of Panel-Count Data with Dependent Termination," Biometrics, The International Biometric Society, vol. 60(1), pages 34-40, March.
    2. David B. Dunson & Gregg E. Dinse, 2000. "Distinguishing Effects on Tumor Multiplicity and Growth Rate in Chemoprevention Experiments," Biometrics, The International Biometric Society, vol. 56(4), pages 1068-1075, December.
    3. Paul S. Albert & Joanna H. Shih, 2003. "Modeling Tumor Growth with Random Onset," Biometrics, The International Biometric Society, vol. 59(4), pages 897-906, December.
    4. David B. Dunson & Donna D. Baird, 2002. "A Proportional Hazards Model for Incidence and Induced Remission of Disease," Biometrics, The International Biometric Society, vol. 58(1), pages 71-78, March.

    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:jorssc:v:49:y:2000:i:1:p:19-30. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.