Estimating the magnitude of carcinogenic effects in long-term animal studies

Carcinogenicity studies seek to compare the incidence of tumours in animals exposed to the substance under investigation and animals used as controls. The conventional method of analysis is the Peto test, which assumes that tumours are either instantly fatal or have no effect on mortality and requires a judgement to be made regarding the lethality of each tumour. Such an assumption seems unrealistic and the judgement is often difficult to make and unreliable. The need for such a judgement and the assumption of extreme lethality can be removed by using parametric multi-state models. In this modelling approach the transition of animals between the states 'alive without a tumour', 'alive with a tumour' and 'dead' is modelled mathematically. This paper compares the Peto test with tests based on two parametric multi-state models in terms of the sensitivity of the tests to detect carcinogenicity. The sensitivity, or power, is shown to be low for commonly used numbers of animals, depending chiefly on the expected total number of animals with tumours. The Omar and Whitehead multi-state model is found to be slightly more powerful than the Dewanji et al. model and at least as powerful as the Peto test. Provided the parametric assumptions are appropriate, this method thus gives a test that is more sensitive than the Peto test and enables estimation of tumour onset and mortality rates without the requirement of tumour lethality judgements.