Anabel Forte

The geography of Spanish bank branches

This article analyzes the determinants of bank branch location in Spain taking the role of geography explicitly into account. After a long period of intense territorial expansion, especially by savings banks, many of these firms are now involved in merger processes triggered off by the financial crisis, most of which entail the closing of many branches. However, given the contributions of this type of banks to limit financial exclusion, this process might exacerbate the consequences of the crisis for some disadvantaged social groups. Related problems such as new banking regulation initiatives (Basel III), or the current excess capacity in the sector add further relevance to this problem. We address this issue from a Bayesian perspective, using a Poisson regression model within the framework of generalized linear mixed models. This proposal allows us to assess whether over-branching or under-branching has taken place. Our results suggest, among other findings, that both phenomena are present in the Spanish banking sector, although the implications for the three types of banks in the industry, namely commercial banks, savings banks or credit unions, vary a great deal.

Bayesian joint modeling for assessing the progression of chronic kidney disease in children

Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.

Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.

Bayesian joint modeling of bivariate longitudinal and competing risks data: An application to study patient-ventilator asynchronies in critical care patients

Mechanical ventilation is a common procedure of life support in intensive care. Patient-ventilator asynchronies (PVAs) occur when the timing of the ventilator cycle is not simultaneous with the timing of the patient respiratory cycle. The association between severity markers and the events death or alive discharge has been acknowledged before, however, little is known about the addition of PVAs data to the analyses. We used an index of asynchronies (AI) to measure PVAs and the SOFA (sequential organ failure assessment) score to assess overall severity. To investigate the added value of including the AI, we propose a Bayesian joint model of bivariate longitudinal and competing risks data. The longitudinal process includes a mixed effects model for the SOFA score and a mixed effects beta regression model for the AI. The survival process is defined in terms of a cause-specific hazards model for the competing risks death or alive discharge. Our model indicates that the SOFA score is strongly related to vital status. PVAs are positively associated with alive discharge but there is not enough evidence that PVAs provide a more accurate indication of death prognosis than the SOFA score alone.

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional‐odds cumulative logit model. Time‐to‐event is modeled through a left‐truncated proportional‐hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event‐free probabilities and predict future longitudinal marker values. The model is applied to the assessment of breast cancer risk in women attending a population‐based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI‐RADS) scale in biennial screening exams.

Practical Issues on Energy-Growth Nexus Data and Variable Selection With Bayesian Analysis

Abstract Given that the energy-growth nexus (EGN) is short of a complete theoretical base, the production function used therein is typically complemented with numerous variables that characterize an economy. Researchers are often puzzled not only with the selection of variables per se, but also with the variable sources and the various data handlings which become apparent and available only after years of experience in this research field. Thus, this chapter is divided into two distinctive parts: The first part contains an overview of the available data sources for the EGN as well as a succinct selection of advice on data handlings, transformations, and interpretations that could come handy to students and practitioners. The second part is more technical and deals with variable selection with Bayesian analysis, which appears as a reasonable solution to the overwhelming problem of variable selection in the EGN. Besides a worked example, the chapter provides with an introduction to Bayesian analysis and the essentials to Bayesian estimation and prediction.

An Ordinal Joint Model for Breast Cancer

We propose a Bayesian joint model to analyze the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model and the time-to-event process through a left-truncated Cox proportional hazards model with information of the longitudinal marker and baseline covariates. Both longitudinal and survival processes are connected by a common vector of random effects.

Incidence and control of black spot syndrome of tiger nut

Tiger nut (Cyperus esculentum) is a very profitable crop in Valencia, Spain, but in the last years, part of the harvested tubers presents black spots in the skin making them unmarketable. Surveys performed in two consecutive years showed that about 10% of the tubers were severely affected by the black spot syndrome whose aetiology is unknown. Disease control procedures based on selection of tubers used as seed (seed tubers) or treatment with hot-water and/or chemicals were assayed in greenhouse. These assays showed that that this syndrome had a negative impact on the germination rate, tuber size and yield. Selection of asymptomatic seed tubers reduced drastically the incidence of the black spot syndrome with respect to using seed tubers with severe symptoms (selection of healthy seed tubers was not possible because the causal agent is undetermined). Thermal treatment of seed tubers with severe symptoms reduced the number of unmarketable harvested tubers by half but was detrimental for the germination. Chemical treatments of seed tubers with severe symptoms decreased the incidence of the black spot syndrome about 40% for sodium hypochlorite and about 10% for hydrochloric acid, trisodium phosphate and the fungicide trioxystrobin.

Sequential Monte Carlo Methods in Random Intercept Models for Longitudinal Data

Longitudinal modelling is common in the field of Biostatistical research. In some studies, it becomes mandatory to update posterior distributions based on new data in order to perform inferential process on-line. In such situations, the use of posterior distribution as the prior distribution in the new application of the Bayes’ theorem is sensible. However, the analytic form of the posterior distribution is not always available and we only have an approximated sample of it, thus making the process “not-so-easy”. Equivalent inferences could be obtained through a Bayesian inferential process based on the set that integrates the old and new data. Nevertheless, this is not always a real alternative, because it may be computationally very costly in terms of both time and resources. This work uses the dynamic characteristics of sequential Monte Carlo methods for “static” setups in the framework of longitudinal modelling scenarios. We used this methodology in real data through a random intercept model.

Bayesian longitudinal models for paediatric kidney transplant recipients

Chronic kidney disease is a progressive loss of renal function which results in the inability of the kidneys to properly filter waste from the blood. Renal function is usually estimated by the glomerular filtration rate (eGFR), which decreases with the worsening of the disease. Bayesian longitudinal models with covariates, random effects, serial correlation and measurement error are discussed to analyse the progression of eGFR in first transplanted children taken from a study in València, Spain.