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.


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

Other papers in the same session

An approach to non-homogenous phase-type distributions through multiple cut-points

C. J. Acal González, J. E. Ruiz Castro, J. B. Roldán Aranda

Cookie policy

We use cookies in order to be able to identify and authenticate you on the website. They are necessary for the correct functioning of it, and therefore they can not be disabled. If you continue browsing the website, you are agreeing with their acceptance, as well as our Privacy Policy.

Additionally, we use Google Analytics in order to analyze the website traffic. They also use cookies and you can accept or refuse them with the buttons below.

You can read more details about our Cookie Policy and our Privacy Policy.