Nonparametric covariate hypothesis tests for the cure rate in mixture cure models
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Nonparametric covariate hypothesis tests for the cure rate in mixture cure modelsData
2020-06Cita bibliográfica
A. López-Cheda, M. A. Jácome, I. Van Keilegom, y R. Cao, «Nonparametric covariate hypothesis tests for the cure rate in mixture cure models», Statistics in Medicine, vol. 39, n.º 17, pp. 2291-2307, 2020, doi: 10.1002/sim.8530.
Resumo
[Abstract]: 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.
Palabras chave
Bootstrap
Censored data
Cure models
Hypothesis tests
Survival analysis
Censored data
Cure models
Hypothesis tests
Survival analysis
Descrición
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© 2020 John Wiley & Sons, Ltd.2291
ISSN
1097-0258