Goodness-of-fit test for a parametric survival function with cure fraction

Candida Geerdens (), Paul Janssen () and Ingrid Van Keilegom ()
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Candida Geerdens: Universiteit Hasselt
Paul Janssen: Universiteit Hasselt
Ingrid Van Keilegom: KU Leuven

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2020, vol. 29, issue 3, No 8, 768-792

Abstract: Abstract We consider the survival function for univariate right-censored event time data, when a cure fraction is present. This means that the population consists of two parts: the cured or non-susceptible group, who will never experience the event of interest versus the non-cured or susceptible group, who will undergo the event of interest when followed up sufficiently long. When modeling the data, a parametric form is often imposed on the survival function of the susceptible group. In this paper, we construct a simple novel test to verify the aptness of the assumed parametric form. To this end, we contrast the parametric fit with the nonparametric fit based on a rescaled Kaplan–Meier estimator. The asymptotic distribution of the two estimators and of the test statistic are established. The latter depends on unknown parameters, hence a bootstrap procedure is applied to approximate the critical values of the test. An extensive simulation study reveals the good finite sample performance of the developed test. To illustrate the practical use, the test is also applied on two real-life data sets.

Keywords: Bootstrap; Cramér-von Mises; Cure fraction; Kaplan–Meier; Parametric models; Weak convergence; 62N01; 62N02; 62N03 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-019-00680-4

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