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Regression Analysis of Tumour Prevalence Data

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  • Gregg E. Dinse
  • S. W. Lagakos

Abstract

This paper proposes a logistic regression model for comparing treatment groups with respect to tumour prevalence. The prevalence test commonly used to compare treatments in animal tumorigenicity experiments (Hoel and Walburg, 1972; Peto et al., 1980) is essentially equivalent to a likelihood score test derived under a logistic model that expresses tumour prevalence as a function of time and treatment. The more general regression context suggests an alternative to the convention of grouping observations into arbitrarily chosen intervals. The model also incorporates covariates, provides a framework for estimating the strength of a dose‐response relationship and for testing a central assumption underlying the usual prevalence test, and is computationally simple to analyse.

Suggested Citation

  • Gregg E. Dinse & S. W. Lagakos, 1983. "Regression Analysis of Tumour Prevalence Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(3), pages 236-248, November.
    • Handle: RePEc:bla:jorssc:v:32:y:1983:i:3:p:236-248
    DOI: 10.2307/2347946
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    Cited by:

    1. A. John Bailer & Walter W. Piegorsch, 2000. "From Quantal Counts to Mechanisms and Systems: The Past, Present, and Future of Biometrics in Environmental Toxicology," Biometrics, The International Biometric Society, vol. 56(2), pages 327-336, June.
    2. Hao Liu & Jing Qin, 2018. "Semiparametric probit models with univariate and bivariate current†status data," Biometrics, The International Biometric Society, vol. 74(1), pages 68-76, March.
    3. Kenny S. Crump & D. Krewski & Y. Wang, 1998. "Estimates of the Number of Liver Carcinogens in Bioassays Conducted by the National Toxicology Program," Risk Analysis, John Wiley & Sons, vol. 18(3), pages 299-308, June.
    4. Li, Shuwei & Hu, Tao & Wang, Peijie & Sun, Jianguo, 2017. "Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 75-86.
    5. Shanshan Lu & Jingjing Wu & Xuewen Lu, 2019. "Efficient estimation of the varying-coefficient partially linear proportional odds model with current status data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 173-194, March.

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