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Attribution of tumour lethality and estimation of the time to onset of occult tumours in the absence of cause‐of‐death Information

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  • H. Ahn
  • H. Moon
  • R. L. Kodell

Abstract

A new statistical approach is developed for estimating the carcinogenic potential of drugs and other chemical substances used by humans. Improved statistical methods are developed for rodent tumorigenicity assays that have interval sacrifices but not cause‐of‐death data. For such experiments, this paper proposes a nonparametric maximum likelihood estimation method for estimating the distributions of the time to onset of and the time to death from the tumour. The log‐likelihood function is optimized using a constrained direct search procedure. Using the maximum likelihood estimators, the number of fatal tumours in an experiment can be imputed. By applying the procedure proposed to a real data set, the effect of calorie restriction is investigated. In this study, we found that calorie restriction delays the tumour onset time significantly for pituitary tumours. The present method can result in substantial economic savings by relieving the need for a case‐by‐case assignment of the cause of death or context of observation by pathologists. The ultimate goal of the method proposed is to use the imputed number of fatal tumours to modify Peto’s International Agency for Research on Cancer test for application to tumorigenicity assays that lack cause‐of‐death data.

Suggested Citation

  • H. Ahn & H. Moon & R. L. Kodell, 2000. "Attribution of tumour lethality and estimation of the time to onset of occult tumours in the absence of cause‐of‐death Information," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 157-169.
  • Handle: RePEc:bla:jorssc:v:49:y:2000:i:2:p:157-169
    DOI: 10.1111/1467-9876.00185
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    Cited by:

    1. Ahn, Hongshik & Moon, Hojin & Kim, Sunyoung & Kodell, Ralph L., 2002. "A Newton-based approach for attributing tumor lethality in animal carcinogenicity studies," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 263-283, January.

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