A cardiotoxicity dataset for breast cancer patients.

This dataset is a result of the collaboration between the University of A Coruña and the University Hospital of A Coruña. It contains information about 531 women diagnosed with HER2+ breast cancer, treated with potentially cardiotoxic oncologic therapies. These treatments can cause cardiovascular adverse events, including cardiac systolic dysfunction, the development of which has important clinical and prognostic implications.

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.

Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data.

Cure models to estimate time until hospitalization due to COVID-19: A case study in Galicia (NW Spain).

A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the most important and recent approaches on parametric, semiparametric and nonparametric mixture cure models is also included.

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain).

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge.

How do early socioeconomic circumstances impact inflammatory trajectories? Findings from Generation XXI.

Background: The association between socioeconomic position and markers of inflammation in adults, including C-reactive protein (CRP), is well-established. We hypothesized that children from families of less-advantaged socioeconomic circumstances may be at higher inflammatory risk during childhood and, consequently, throughout their life course. Thus, we aimed to investigate whether early socioeconomic circumstances impact CRP trajectories using repeated measures of data from a population-based birth cohort.

Nonparametric covariate hypothesis tests for the cure rate in mixture cure models.

In lifetime data, like cancer studies, there may be long term survivors, which lead to heavy censoring at the end of the follow-up period. Since a standard survival model is not appropriate to handle these data, a cure model is needed. In the literature, covariate hypothesis tests for cure models are limited to parametric and semiparametric methods.