Objectives: When populations contain mixtures of cured and uncured patients, the use of traditional parametric approaches to estimate overall survival (OS) can be biased. Mixture cure models may reduce bias compared with traditional parametric models, but their accuracy is subject to certain conditions. Importantly, mixture cure models assume that that there is enough follow-up to identify individuals censored at the end of the follow-up period as cured. The purpose of this article is to describe biases that can occur when mixture cure models are used to estimate mean survival from data with limited follow-up. Methods: We analyzed 6 trials conducted by the SWOG Cancer Research Network Leukemia Committee. For each trial, we analyzed 2 data sets: the data released to the committee when the results of the trial were unblinded and a second data set with additional follow-up. We estimated mean OS using parametric survival models with and without a cure fraction. Results: When using mixture cure models, in 4 trials, estimates of mean OS were higher with the first analysis (with limited follow-up) compared with estimates from data with longer follow-up. In 1 trial, the reverse pattern was observed. In 1 trial, the cure estimate changed little with additional follow-up. Conclusions: Caution should be taken when using mixture cure models in scenarios with limited follow-up. The biases resulting from fitting these models may be exacerbated when the models are being used to extrapolate OS and estimate mean OS.