Vertical modeling: analysis of competing risks data with a cure fraction

Mioara Alina Nicolaie (), Jeremy M. G. Taylor and Catherine Legrand
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Mioara Alina Nicolaie: Catholic University of Louvain
Jeremy M. G. Taylor: University of Michigan
Catherine Legrand: Catholic University of Louvain

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 1, No 1, 25 pages

Abstract: Abstract In this paper, we extend the vertical modeling approach for the analysis of survival data with competing risks to incorporate a cure fraction in the population, that is, a proportion of the population for which none of the competing events can occur. The proposed method has three components: the proportion of cure, the risk of failure, irrespective of the cause, and the relative risk of a certain cause of failure, given a failure occurred. Covariates may affect each of these components. An appealing aspect of the method is that it is a natural extension to competing risks of the semi-parametric mixture cure model in ordinary survival analysis; thus, causes of failure are assigned only if a failure occurs. This contrasts with the existing mixture cure model for competing risks of Larson and Dinse, which conditions at the onset on the future status presumably attained. Regression parameter estimates are obtained using an EM-algorithm. The performance of the estimators is evaluated in a simulation study. The method is illustrated using a melanoma cancer data set.

Keywords: Mixture cure model; Competing risks; Cumulative incidences (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9417-8

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