TL;DR: In this article, the authors compare a nonparametric test, which uses interim sacrifice to avoid such assumptions, with these tests using simulation based on the EDOl data, and show that in the presence of a significant difference in the mortality rate with treatment, commonly used methods could fail to maintain the nominal significance level.
Abstract: Commonly used tests to detect carcinogenic potential of a test compound make extreme assumptions about the lethality of tumors, due to their occult nature. In this paper we compare a nonparametric test, which uses interim sacrifice to avoid such assumptions, with these tests using simulation based on the EDOl data. Results indicate that in the presence of a significant difference in the mortality rate with treatment, commonly used methods could fail to maintain the nominal significance level. However, when there is no difference in the mortality rate, such procedures are robust to the underlying assumptions about the lethality of tumors and more powerful than the nonparametric test using interim sacrifice.
TL;DR: A simple compartmental model is described that does not involve the cause of death and requires only one interim sacrifice, in addition to the usual terminal kill, to ensure that the tumour incidence rates can be estimated.
Abstract: SUMMARY Analyses of carcinogenicity experiments involving occult (hidden) tumours are usually based on causeof -death information or the results of many interim sacrifices. A simple compartmental model is described that does not involve the cause of death. The method of analysis requires only one interim sacrifice, in addition to the usual terminal kill, to ensure that the tumour incidence rates can be estimated. One advantage of the approach is demonstrated in the analysis of glomerulosclerosis following exposure to ionizing radiation. Although the semiparametric model involves fewer parameters, estimates of key functions derived in this analysis are similar to those obtained previously by using a nonparametric method that involves many more parameters.