Attribution of Tumor Lethality in the Absence of Cause - of -

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 sacriices but not cause-of-death data. For such experiments, this paper proposes a nonparametric maximum likelihood estimation method for estimating the distributions of time-to-onset-of and the time-to-death-from the tumor. The log-likelihood function is optimized using a constrained direct-search procedure. Using the maximum likelihood estimators, the number of fatal tumors in an experiment can be imputed. A Monte Carlo Simulation study is conducted for illustration. The accuracy of cause of death attributed by the proposed method tends to increase as the competing risks survival rate increases. By applying the proposed procedure to a real data set, the eeect of calorie restriction is investigated. In this study, we found calorie restriction delays the tumor-onset time signiicantly for pituitary tumors. The present method can result in substantial economic savings by relieving the need for case-by-case assignment of cause of death or context of observation by pathologists. The ultimate goal of the proposed method is to use the imputed number of fatal tumors to modify Peto's IARC test for application to tumorigenicity assays that lack cause-of-death data.