arXiv subjects

Carmen Armero

Publications and source records attributed to Carmen Armero.

22 records · Page 2Linked to original sources

arXiv2020

Bayesian survival analysis with BUGS

Survival analysis is one of the most important fields of statistics in medicine and the biological sciences. In addition, the computational advances in the last decades have favoured the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this paper is to summarise some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R-packages are also discussed.

stat.AP↗

arXiv2020

Bayesian joint models for longitudinal and survival data

This paper takes a quick look at Bayesian joint models (BJM) for longitudinal and survival data. A general formulation for BJM is examined in terms of the sampling distribution of the longitudinal and survival processes, the conditional distribution of the random effects and the prior distribution. Next a basic BJM defined in terms of a mixed linear model and a Cox survival regression models is discussed and some extensions and other Bayesian topics are briefly outlined.

stat.ME↗

arXiv2020

Bayesian longitudinal models for exploring European sardine fishing in the Mediterranean Sea

In the Mediterranean Sea, catches are dominated by small pelagic fish, representing nearly the 49\% of the total harvest. Among them, the European sardine (Sardina pilchardus) is one of the most commercially important species showing high over-exploitation rates in recent last years. In this study we analysed the European sardine landings in the Mediterranean Sea from 1970 to 2014. We made use of Bayesian longitudinal linear mixed models in order to assess differences in the temporal evolution of fishing between and within countries. Furthermore, we modelled the subsequent joint evolution of artisanal and industrial fisheries. Overall results confirmed that Mediterranean fishery time series were highly diverse along their dynamics and this heterogeneity was persistent throughout the time. In addition, results highlighted a positive relationship between the two types of fishing.

stat.AP↗

arXiv2018

Approximate Bayesian inference for mixture cure models

Cure models in survival analysis deal with populations in which a part of the individuals cannot experience the event of interest. Mixture cure models consider the target population as a mixture of susceptible and non-susceptible individuals. The statistical analysis of these models focuses on examining the probability of cure (incidence model) and inferring on the time-to-event in the susceptible subpopulation (latency model). Bayesian inference on mixture cure models has typically relied upon Markov chain Monte Carlo (MCMC) methods. The integrated nested Laplace approximation (INLA) is a recent and attractive approach for doing Bayesian inference. INLA in its natural definition cannot fit mixture models but recent research has new proposals that combine INLA and MCMC methods to extend its applicability to them (Bivand et al., 2014, Gómez-Rubio et al., 2017, Gómez-Rubio and Rue, 2018}. This paper focuses on the implementation of INLA in mixture cure models. A general mixture cure survival model with covariate information for the latency and the incidence model within a general scenario with censored and non-censored information is discussed. The fact that non-censored individuals undoubtedly belong to the uncured population is a valuable information that was incorporated in the inferential process.

stat.CO↗