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Abstracts Session 2


Irène Gijbels (KULeuven): A hybrid hazard-based model using two-piece distributions


In the analysis of time-to-event or survival data, the Cox proportional hazard model and the accelerated failure time model, are well-known models. In both models the effect of covariates is standard of a parametric form. In this talk we consider a hybrid hazard-based regression model that englobes both mentioned models, among others, and in addition allows for more flexible (non pre-specified) effects of the covariates. The focus in on revealing the impact of the covariates, whereas the baseline hazard is modelled via a large class of two-piece asymmetric baseline distributions. We discuss estimation methods in various settings (parametric, semi-parametric -- partly linear, and non-parametric). We investigate the performance of the estimators, and illustrate the practical use of the developed methods in real data applications. 

This talk is based on joint work with Worku Biyadgie Ewnetu (UHasselt and KU Leuven) and Anneleen Verhasselt (UHasselt).

2) Morine Delhelle (UCLouvain): Copula based dependent censoring in cure models


Survival analysis examines and models the time it takes for events to occur. The typical event is death, from which the name ‘survival analysis’ and much of its terminology derives. Since the data can only be collected over a finite period of time, the ‘time to event’ may not be observed for all the individuals. This is the case for example when a patient leaves a clinical study prematurely or she/he is still alive by the end of the study. In such a case, the death time for this individual is unknown. Such a phenomenon, named censoring, creates some unusual difficulties in the analysis of survival data that cannot be handled properly by standard statistical methods. Most of the time, ’independent censoring’ is assumed but that can lead to bias when there is actually ’dependent censoring’, that is when the survival time and the censoring time are stochastically dependent of each other. This occurs, e.g., when the event of interest is the time to death due to a certain disease, and censoring occurs (among other causes) when a patient dies due to another disease that has the same risk factors.

In traditional survival analysis, all subjects in the population are assumed to be susceptible to the event of interest, that is, every subject has either already experienced the event or will experience it in the future. However it may happen that a fraction of individuals will never experience the event and are considered to be event free. For example, if we observe patients with breast cancer, fortunately some of them may never die of their cancer and thus can be regarded as cured individuals. Models which deal with such data are called cure models. One of them, the mixture cure model, assumes that the underlying population is a mixture of two sub-populations : the ‘susceptibles’ (who will experience the event) and the ‘non-susceptibles’ (who are event free).

In survival data analysis datasets with both a cure fraction and dependent censoring are not scarce and it is important to use an adequate model which deal with these two characteristics if we want to avoid bias in parameters estimations or false conclusions in clinical trials.

In this presentation I will propose a fully parametric survival mixture cure model that takes possible dependent censoring into account (inspired by the approach followed by Czado and Van Keilegom (2023 - to appear))), which is based on an unknown copula that describes the relation between the survival and censoring times. So the advantages of the model is that dependent censoring and the cure fraction are both taken into account and that the copula is not assumed to be known. Moreover it allows us to estimate the strength of dependence.