Extension of Peto's test by attribution of tumor lethality in the absence of cause-of-death information

A new statistical testing approach is developed for rodent tumorigenicity assays that have a single terminal sacrifice or occasionally interim sacrifices but not cause-of-death data. For experiments that lack cause-of-death data, statistically imputed numbers of fatal tumors and incidental tumors are used to modify Peto's cause-of-death test which is usually implemented using pathologist-assigned cause-of-death information. The numbers of fatal tumors are estimated using a constrained nonparametric maximum likelihood estimation method. A new Newton-based approach under inequality constraints is proposed for finding the global maximum likelihood estimates. In this study, the proposed method is concentrated on data with a single sacrifice experiment without implementing further assumptions. The new testing approach may be more reliable than Peto's test because of the potential for a misclassification of cause-of-death by pathologists. A Monte Carlo simulation study for the proposed test is conducted to assess size and power of the test. Asymptotic normality for the statistic of the proposed test is also investigated. The proposed testing approach is illustrated using a real data set.