Toxicologists frequently conduct toxicity experiments in which different treatment conditions are applied to groups of animals and the resulting mortality in each group is measured at a number of discrete time points over the course of the experiment. In this article, we develop and extend a number of diagnostic tools for the detection of mean misspecification, or systematic departures of the mean-link specification, in cumulative multinomial generalized linear models fit to such data. Several real data sets are used to illustrate these diagnostics. These tools help the analyst to differentiate between two sources of lack of fit in such models: mean misspecification and extra-multinomial variation or overdispersion.