arXiv subjects

Carolyne Soares de Brito

Publications and source records attributed to Carolyne Soares de Brito.

2 recordsLinked to original sources

arXiv2026

A parametric mixture cure rate model with competing risks

The well-known standard mixture cure model is widely used to model long-term survivors by distinguishing cured from non-cured individuals in the study population. Since its seminal introduction by Berkson and Gage, numerous extensions have been proposed to enhance its flexibility and applicability across diverse clinical settings. However, most existing extensions focus on a single cause of failure among non-cured individuals, often treating events from alternative causes as censored observations. This limitation may inadequately represent the complexity of survival data in which individuals are exposed to multiple competing causes of failure. In particular, treating competing events as non-informative censoring when estimating the probability of a specific cause of failure may lead to biased estimates of cumulative incidence and potentially misleading clinical interpretations. These limitations motivate the development of flexible survival models that jointly account for long-term survivors and multiple competing causes of failure. In this study, we propose an extension of the standard mixture cure model that incorporates a cure fraction and multiple competing risks within a unified framework. Model parameters are estimated by maximum likelihood using augmented data. The finite-sample performance of the proposed estimators is evaluated through extensive simulation studies, which yield satisfactory results across the scenarios considered. Finally, the proposed methodology is illustrated using melanoma patient data from the Surveillance, Epidemiology, and End Results (SEER) database, which include long-term survivors and multiple competing causes of death.

stat.ME↗

arXiv2026

Joint return levels of maximum temperature and minimum relative humidity by combining copulas with an extreme value framework for bimodal data

Climate change has become a growing concern, particularly in regions experiencing increasingly frequent extreme events. In Brasília, the capital of Brazil, significant shifts in climate patterns have drawn attention, including episodes of intense heat, unusually cold weather, and prolonged dry periods, often accompanied by wildfires. These phenomena directly affect the population and local ecosystems, requiring detailed analyses to understand their causes and effects. In this work, the return period for the joint distribution of maximum air temperature and minimum relative humidity in Brasília is determined using a copula-based approach. First, the dependence structure between the variables is modeled through a copula, while the marginal distributions are fitted using the novel methodology for modelling extreme values in complex systems. Different copula families, including rotated versions, were evaluated using information criteria, leading to the selection of a model that adequately captured the dependence structure, including asymmetry and tail behavior. The selected model also reproduced the multimodal pattern observed in the joint density, a feature consistent with the possible presence of multiple climate regimes. In the context of climate data modelling, the bivariate return level was determined using a conditional approach to assess the occurrence of extreme scenarios characterized by high temperatures combined with low relative humidity. This analysis makes it possible to quantify the expected frequency of such events, providing valuable information for environmental risk monitoring, the planning of preventive measures, and the development of climate change adaptation strategies in the region.

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