Logistic regression analysis of incidental-tumor data from animal carcinogenicity experiments

Abstract

Survival differences can have a substantial impact on the statistical comparison of tumor development in control and treated animals and thus should be taken into account routinely in the analysis of carcinogenicity data from laboratory experiments. However, the appropriate survival adjustment depends on whether the tumor of interest is fatal or incidental. The usual analysis of incidental tumors, which adjusts for survival by stratifying the animals according to age at death, has various shortcomings. Alternatively, logistic regression methods allow a continuous survival adjustment and furnish a convenient framework for solving many of the problems associated with the agestratified approach of grouping the data into time intervals. Logistic regression substitutes modeling the prevalence function for the arbitrary choice of time intervals, providing a survival adjustment (when the model holds) even when differential mortality might increase the bias or decrease the sensitivity of interval-based methods. The logistic analysis also can incorporate covariables which, if ignored, might confound the interpretation of the data. Several examples illustrate these potential advantages of basing the analysis of incidental tumors on logistic regression techniques.