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

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  • 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
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    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. 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.
    3. 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.
    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.

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