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Use of Tumour Lethality to Interpret Tumorigenicity Experiments Lacking Cause‐Of‐Death Data

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  • Stephen W. Lagakos
  • Thomas A. Louis

Abstract

A popular statistical test for comparing control and exposed groups in tumorigenicity experiments requires, for each animal found to have a tumour at death, an indication of whether the tumour caused death. Two other popular tests do not require cause‐of‐death information but assume that the tumour being investigated is either instantly lethal or non‐lethal. For lack of alternatives, these two tests are also routinely used for tumours of intermediate lethality because cause‐of‐death information is not available in most experiments. However, when the risks of death from non‐tumour causes in the control and exposed groups differ, both tests are biased for tumours of intermediate lethality and can give very different indications about the tumorigenicity of the compound being tested. to help to resolve some of the problems with the interpretation of the non‐lethal and rapidly lethal tests, we derive an incomplete data analogue of the cause‐of‐death test for situations where the cause is not available, and with examples illustrate its use in the interpretation of tumorigenicity experiments.

Suggested Citation

  • Stephen W. Lagakos & Thomas A. Louis, 1988. "Use of Tumour Lethality to Interpret Tumorigenicity Experiments Lacking Cause‐Of‐Death Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(2), pages 169-179, June.
    • Handle: RePEc:bla:jorssc:v:37:y:1988:i:2:p:169-179
    DOI: 10.2307/2347336
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    Cited by:

    1. Francisco Louzada-Neto, 1999. "Polyhazard Models for Lifetime Data," Biometrics, The International Biometric Society, vol. 55(4), pages 1281-1285, December.
    2. Mazucheli, Josmar & Louzada-Neto, Francisco & Achcar, Jorge A., 2001. "Bayesian inference for polyhazard models in the presence of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 1-14, November.
    3. Ma, Ling & Hu, Tao & Sun, Jianguo, 2016. "Cox regression analysis of dependent interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 79-90.
    4. William J. Reed, 2011. "A flexible parametric survival model which allows a bathtub-shaped hazard rate function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1665-1680, August.
    5. Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
    6. Moon, Hojin & Ahn, Hongshik & Kodell, Ralph L. & Pearce, Bruce A., 1999. "A comparison of a mixture likelihood method and the EM algorithm for an estimation problem in animal carcinogenicity studies," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 227-238, August.
    7. Sanjib Basu & Ram C. Tiwari, 2010. "Breast cancer survival, competing risks and mixture cure model: a Bayesian analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 307-329, April.
    8. Jessica Y. Mancuso & Hongshik Ahn & James J. Chen & James P. Mancuso, 2002. "Age-Adjusted Exact Trend Tests in the Event of Rare Occurrences," Biometrics, The International Biometric Society, vol. 58(2), pages 403-412, June.
    9. 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.

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