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Nonparametric covariate hypothesis tests for the cure rate in mixture cure models
Author(s) -
LópezCheda Ana,
Jácome Maria Amalia,
Van Keilegom Ingrid,
Cao Ricardo
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8530
Subject(s) - covariate , nonparametric statistics , statistics , econometrics , cure rate , mathematics , computer science , medicine
In lifetime data, like cancer studies, there may be long term survivors, which lead to heavy censoring at the end of the follow‐up period. Since a standard survival model is not appropriate to handle these data, a cure model is needed. In the literature, covariate hypothesis tests for cure models are limited to parametric and semiparametric methods. We fill this important gap by proposing a nonparametric covariate hypothesis test for the probability of cure in mixture cure models. A bootstrap method is proposed to approximate the null distribution of the test statistic. The procedure can be applied to any type of covariate, and could be extended to the multivariate setting. Its efficiency is evaluated in a Monte Carlo simulation study. Finally, the method is applied to a colorectal cancer dataset.