Generalized parametric cure models for relative survival. Issue 4 (20th January 2020)

• Record Type: Journal Article
• Authors: Jakobsen, Lasse Hjort; Bøgsted, Martin; Clements, Mark
• Is Part Of: Biometrical journal. Volume 62:Issue 4(2020:Jul.)
• Page Start: 989
• Page End: 1011
• Publication Date: 2020-01-20
• Subjects: cure models -- parametric models -- relative survival -- splines; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201900056
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: Cure models are used in time‐to‐event analysis when not all individuals are expected to experience the event of interest, or when the survival of the considered individuals reaches the same level as the general population. These scenarios correspond to a plateau in the survival and relative survival function, respectively. The main parameters of interest in cure models are the proportion of individuals who are cured, termed the cure proportion, and the survival function of the uncured individuals. Although numerous cure models have been proposed in the statistical literature, there is no consensus on how to formulate these. We introduce a general parametric formulation of mixture cure models and a new class of cure models, termed latent cure models, together with a general estimation framework and software, which enable fitting of a wide range of different models. Through simulations, we assess the statistical properties of the models with respect to the cure proportion and the survival of the uncured individuals. Finally, we illustrate the models using survival data on colon cancer, which typically display a plateau in the relative survival. As demonstrated in the simulations, mixture cure models which are not guaranteed to be constant after a finite time point, tend to produce accurate estimates of the cure proportion and the survival of the uncured. However, these models are very unstable in certain cases due to identifiability issues, whereas LC models generally provide stable results at the price of more biased estimates.