Single-index mixture cure models: vector, functional and image covariates

B. Piñeiro Lamas, R. Cao, A. López-Cheda

Standard survival models assume that, in the absence of censoring, all individuals would experience the event of interest. However, this is not always realistic. For instance, HER2-positive breast cancer patients usually receive trastuzumab. Although this therapy has antitumor efficacy it can cause a problem in the heart, known as cardiotoxicity. In this context, there will be a fraction of individuals that will never suffer the side effect, just because they are not susceptible to it. They are said to be cured. Mixture cure models allow to estimate the probability of being cured and the survival function of the uncured population, depending on some covariates. In the literature, nonparametric estimation of both functions is limited to continuous unidimensional covariates. We fill this important gap by considering vector, functional and image covariates, and proposing a single-index model for dimension reduction. The methodology is applied to a cardiotoxicity dataset.

Keywords: Censored data, Cardiotoxicity, Image data, Survival analysis.

Scheduled

Ramiro Melendreras Award II
November 7, 2023  3:30 PM
CC4: Room 2

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