Goodness-of-fit testing in the presence of cured data: IPCW approach.

Abstract

Here we revisit a goodness-of-fit testing problem for randomly right-censored data in the presence of cured subjects, i.e. the population consists of two parts: the cured or non-susceptible group, who will never experience the event of interest versus those who will undergo the event of interest when followed up sufficiently long. We consider the modifications of proposed characterization-based goodness-of-fit tests for the exponential distribution constructed via the inverse probability of censoring weighted U- or V-approach. We present their asymptotic properties and extend our discussion to encompass suitable generalizations applicable to a variety of tests formulated using the same methodology. A comparative power study of these proposed tests against a recent CvM-based competitor and the modifications of the most prominent competitors identified in prior studies that did not consider the presence of cured subjects, demonstrates good finite sample performance. Novel tests are illustrated on a real dataset related to leukemia relapse.