Flexible estimation in cure survival models using Bayesian P-splines

Flexible estimation in cure survival models using Bayesian P-splines

In the analysis of survival data, it is usually assumed that any unit will experience the event of interest if it is observed for a sufficient long time. However, one can explicitly assume that an unknown proportion of the population under study will never experience the monitored event. The promotion time model, which has a biological motivation, is one of the survival models taking this featu...

Bayesian P-Splines

Exploring factors related to metastasis free survival in breast cancer patients using Bayesian cure models.

BACKGROUND Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis. MATERIALS AND METHODS Cure rate...

Archimedean copula estimation using Bayesian splines smoothing techniques

Copulas enable to specifymultivariate distributions with givenmarginals.Various parametric proposals weremade in the literature for these quantities, mainly in the bivariate case. They can be systematically derived from multivariate distributions with known marginals, yielding e.g. the normal and the Student copulas. Alternatively, one can restrict his/her interest to a sub-family of copulas na...

Spatially Adaptive Bayesian Penalized Regression Splines (P-splines)

In this paper we study penalized regression splines (P-splines), which are low–order basis splines with a penalty to avoid undersmoothing. Such P–splines are typically not spatially adaptive, and hence can have trouble when functions are varying rapidly. Our approach is to model the penalty parameter inherent in the P–spline method as a heteroscedastic regression function. We develop a full Bay...