 Goodness-of-fit tests for the cure rate in a mixture cure modelU U Müller and Ingrid Van Keilegom () Biometrika, 2019, vol. 106, issue 1, 211-227 Abstract: SUMMARY We consider models for time-to-event data that allow that an event, e.g., a relapse of a disease, never occurs for a certain percentage $p$ of the population, called the cure rate. We suppose that these data are subject to random right censoring and we model the data using a mixture cure model, in which the survival function of the uncured subjects is left unspecified. The aim is to test whether the cure rate $p$, as a function of the covariates, satisfies a certain parametric model. To do so, we propose a test statistic that is inspired by a goodness-of-fit test for a regression function due to Härdle & Mammen (1993). We show that the statistic is asymptotically normally distributed under the null hypothesis, that the model is correctly specified, and under local alternatives. A bootstrap procedure is proposed to implement the test. The good performance of the approach is confirmed with simulations. For illustration we apply the test to data on the times between first and second births. Keywords: Beran estimator; Bootstrap; Censoring; Cure fraction; Kaplan–Meier estimator; Kernel estimator; Logistic model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:106:y:2019:i:1:p:211-227. Ordering information: This journal article can be ordered from Access Statistics for this article Biometrika is currently edited by Paul Fearnhead More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. |
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