Estimation and Testing of Tumor Incidence Rates in Experiments Lacking Cause‐of‐Death Data

Approximate nonparametric maximum likelihood estimation of the tumor incidence rate and comparison of tumor incidence rates between treatment groups are examined in the context of animal carcinogenicity experiments that have interval sacrifice data but lack cause‐of‐death information. The estimation procedure introduced by MALANI and VAN RYZIN (1988), which can result in a negative estimate of the tumor incidence rate, is modified by employing a numerical method to maximize the likelihood function iteratively, under the constraint that the tumor incidence rate is nonnegative. With the new procedure, estimates can be obtained even if sacrifices occur anywhere within an interval. The resulting estimates have reduced standard error and give more power to the test of two heterogeneous groups. Furthermore, a linear contrast of more than two groups can be tested using our procedure. The proposed estimation and testing methods are illustrated with an experimental data set.