LAUSR

The need to model a cure fraction, the proportion of a cohort not susceptible to the event of interest, arises in a variety of contexts including tumor relapse in oncology. Existing methodology assumes that follow‐up is long enough for all uncured subjects to have experienced the event of interest at the time of analysis, and researchers have demonstrated that fitting cure models without sufficient follow‐up leads to bias. Few statistical methods exist to evaluate sufficient follow‐up, and they can exhibit poor performance and lead users to falsely conclude sufficient follow‐up, leading to bias, or to falsely claim insufficient follow‐up, possibly leading to additional, costly data collection. We propose a new quantitative statistic (RECeUS) to evaluate whether cure models may be appropriate to apply to censored data. Specifically, we propose that the estimated proportion of censored uncured subjects in a study can be used to evaluate cure model appropriateness. We evaluated the performance of RECeUS against existing methods via simulation and with two data examples, and we observe that RECeUS displays superior performance. In simulated and real‐world settings, RECeUS correctly identifies both situations in which data appear appropriate for cure modeling and when data seem inappropriate for fitting cure models.