A Bayesian nonparametric model for classification of longitudinal profiles.

Across several medical fields, developing an approach for disease classification is an important challenge. The usual procedure is to fit a model for the longitudinal response in the healthy population, a different model for the longitudinal response in the diseased population, and then apply Bayes' theorem to obtain disease probabilities given the responses. Unfortunately, when substantial heterogeneity exists within each population, this type of Bayes classification may perform poorly. In this article, we develop a new approach by fitting a Bayesian nonparametric model for the joint outcome of disease status and longitudinal response, and then we perform classification through the clustering induced by the Dirichlet process. This approach is highly flexible and allows for multiple subpopulations of healthy, diseased, and possibly mixed membership. In addition, we introduce an Markov chain Monte Carlo sampling scheme that facilitates the assessment of the inference and prediction capabilities of our model. Finally, we demonstrate the method by predicting pregnancy outcomes using longitudinal profiles on the human chorionic gonadotropin beta subunit hormone levels in a sample of Chilean women being treated with assisted reproductive therapy.

Classification of longitudinal profiles using semi-parametric nonlinear mixed models with P-Splines and the SAEM algorithm.

In this work, we propose an extension of a semiparametric nonlinear mixed-effects model for longitudinal data that incorporates more flexibility with penalized splines (P-splines) as smooth terms. The novelty of the proposed approach consists of the formulation of the model within the stochastic approximation version of the EM algorithm for maximum likelihood, the so-called SAEM algorithm. The proposed approach takes advantage of the formulation of a P-spline as a mixed-effects model and the use of the computational advantages of the existing software for the SAEM algorithm for the estimation of the random effects and the variance components. Additionally, we developed a supervised classification method for these non-linear mixed models using an adaptive importance sampling scheme. To illustrate our proposal, we consider two studies on pregnant women where two biomarkers are used as indicators of changes during pregnancy. In both studies, information about the women's pregnancy outcomes is known. Our proposal provides a unified framework for the classification of longitudinal profiles that may have important implications for the early detection and monitoring of pregnancy-related changes and contribute to improved maternal and fetal health outcomes. We show that the proposed models improve the analysis of this type of data compared to previous studies. These improvements are reflected both in the fit of the models and in the classification of the groups.

Glaciological Mass balance of Znosko Glacier, King George Island.

The present article objective is to determine the net mass balance of the glacier Znosko for periods 2018-2019 and 2019-2020. It is situated on King George Island which belongs to the groups Shetland of the South, Antarctic Peninsula region. For this objective, during February 2018 a net of 19 stakes (which were controlled once during February 2019 and 2020) were installed on the glacier ablation zone, drilling in the accumulation zone and about flights using unmanned aerial vehicles (UAV) to control the glacier zone and geomorphological changes. For the year 2020, it was determined a glacier area of 1.71 ± 0.02 km2, moreover, using five different methods of interpolation, it was obtained on average, as a result, a specific net balance of -590.7 ± 46.6 mm w.e (in water equivalent) for 2018-2019 and -686.7 ± 28.2 mm w.e for 2019-2020, being the ELA altitude 146.5 ± 18.2 m and 144.2 ± 8.3 m respectively. The two consecutive years represent negative net mass balances which are in accordance with other similar studies on this region, also glacier data were obtained on a zone that is characterized by its difficult access.

A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism.

Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism.

Predicting pregnancy outcomes using longitudinal information: a penalized splines mixed-effects model approach.

We propose a semiparametric nonlinear mixed-effects model (SNMM) using penalized splines to classify longitudinal data and improve the prediction of a binary outcome. The work is motivated by a study in which different hormone levels were measured during the early stages of pregnancy, and the challenge is using this information to predict normal versus abnormal pregnancy outcomes. The aim of this paper is to compare models and estimation strategies on the basis of alternative formulations of SNMMs depending on the characteristics of the data set under consideration. For our motivating example, we address the classification problem using a particular case of the SNMM in which the parameter space has a finite dimensional component (fixed effects and variance components) and an infinite dimensional component (unknown function) that need to be estimated. The nonparametric component of the model is estimated using penalized splines. For the parametric component, we compare the advantages of using random effects versus direct modeling of the correlation structure of the errors. Numerical studies show that our approach improves over other existing methods for the analysis of this type of data. Furthermore, the results obtained using our method support the idea that explicit modeling of the serial correlation of the error term improves the prediction accuracy with respect to a model with random effects, but independent errors. Copyright © 2017 John Wiley & Sons, Ltd.

Study protocol for the Maule Cohort (MAUCO) of chronic diseases, Chile 2014-2024.

BACKGROUND: Maule Cohort (MAUCO), a Chilean cohort study, seeks to analyze the natural history of chronic diseases in the agricultural county of Molina (40,000 inhabitants) in the Maule Region, Chile. Molina´s population is of particular interest because in the last few decades it changed from being undernourished to suffering excess caloric intake, and it currently has the highest national rates of cardiovascular diseases, stomach cancer and gallbladder cancer. Between 2009 and 2011 Molina´s poverty rate dropped from 24.1 % to 13.5 % (national average 20.4 %); in this period the county went from insufficient to almost complete basic sanitation. Despite these advances, chemical pollutants in the food and air are increasing. Thus, in Molina risk factors typical of both under-developed and developed countries coexist, generating a unique profile associated with inflammation, oxidative stress and chronic diseases. METHODS/DESIGN: MAUCO is the core project of the recently established Advanced Center for Chronic Diseases (ACCDiS), Universidad de Chile & Pontificia Universidad Católica de Chile. In this study, we are enrolling and following 10,000 adults aged 38 to 74 years over 10 years. All eligible Molina residents will be enrolled. Participants were identified through a household census. Consenting individuals answer an epidemiological survey exploring risk factors (psycho-social, pesticides, diet, alcohol, and physical activity), medical history and physical and cognitive conditions; provide fasting blood, urine, and saliva samples; receive an electrocardiogram, abdominal ultrasound and bio-impedance test; and take a hand-grip strength test. These subjects will be re-interviewed after 2, 5 and 7 years. Active surveillance of health events is in place throughout the regional healthcare system. The MAUCO Bio-Bank will store 30 to 50 aliquots per subject using an NIH/NCI biorepository system for secure and anonymous linkage of samples with data. DISCUSSION: MAUCO´s results will help design public health interventions tailored to agricultural populations in Latin America.

[Early child development inequalities and associated factors between public and private providers at metropolitan region in Chile]. / Desigualdades en desarrollo infantil temprano entre prestadores públicos y privados de salud y factores asociados en la Región Metropolitana de Chile.

Early child development is a population determinant of physical, mental and social health. To know the base line situation prior to the implementation of "Chile grows with you" (Chile Crece Contigo) is key to its evaluation. OBJECTIVE: To compare early child development and associated factors at baseline in pre-school children from public and private health sectors. PATIENTS AND METHOD: The sample consisted of 1045 children aged 30-58 months, 52% male, and 671 from the public and 380 from the private sector of the metropolitan region in Chile were evaluated using Battelle Developmental Inventory-1 and a household interview of primary carer. RESULTS: Abnormal child development was found in 14.4% of children in the private sector compared to 30.4% in the public sector. There were no differences in adaptive area (26.3% vs 29.2%), but there were statistically significant differents in cognitive (8.8% vs 12.1%), social-personal (13.2% vs 32.5%), motor (19.2% vs 35.3%), and communication (19% vs 36.8%) development. The logistic regression showed that, independent of socioeconomic level, the risk factors are: Apgar<7 (OR: 5.4; 95% CI: 1.24-23.84); having childhood chronic diseases (OR: 1.3; 95% CI: 1.11-1.42). Protective factor is: home with resources to learn and play (OR: 0.8; 95% CI: 0.76-0.89). CONCLUSION: These results are another input about early child development situation and its importance for paediatric social policy.

Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators.

We propose a classification method for longitudinal data. The Bayes classifier is classically used to determine a classification rule where the underlying density in each class needs to be well modeled and estimated. This work is motivated by a real dataset of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. The proposed model, which is a semiparametric linear mixed-effects model (SLMM), is a particular case of the semiparametric nonlinear mixed-effects class of models (SNMM) in which finite dimensional (fixed effects and variance components) and infinite dimensional (an unknown function) parameters have to be estimated. In SNMM's maximum likelihood estimation is performed iteratively alternating parametric and nonparametric procedures. However, if one can make the assumption that the random effects and the unknown function interact in a linear way, more efficient estimation methods can be used. Our contribution is the proposal of a unified estimation procedure based on a penalized EM-type algorithm. The Expectation and Maximization steps are explicit. In this latter step, the unknown function is estimated in a nonparametric fashion using a lasso-type procedure. A simulation study and an application on real data are performed.

Pyrosequencing-Based Assessment of the Microbial Community Structure of Pastoruri Glacier Area (Huascarán National Park, Perú), a Natural Extreme Acidic Environment.

The exposure of fresh sulfide-rich lithologies by the retracement of the Nevado Pastoruri glacier (Central Andes, Perú) is increasing the presence of heavy metals in the water as well as decreasing the pH, producing an acid rock drainage (ARD) process in the area. We describe the microbial communities of an extreme ARD site in Huascarán National Park as well as their correlation with the water physicochemistry. Microbial biodiversity was analyzed by FLX 454 sequencing of the 16S rRNA gene. The suggested geomicrobiological model of the area distinguishes three different zones. The proglacial zone is located in the upper part of the valley, where the ARD process is not evident yet. Most of the OTUs detected in this area were related to sequences associated with cold environments (i.e., psychrotolerant species of Cyanobacteria or Bacteroidetes). After the proglacial area, an ARD-influenced zone appeared, characterized by the presence of phylotypes related to acidophiles (Acidiphilium) as well as other species related to acidic and cold environments (i.e., acidophilic species of Chloroflexi, Clostridium and Verrumicrobia). Sulfur- and iron-oxidizing acidophilic bacteria (Acidithiobacillus) were also identified. The post-ARD area was characterized by the presence of OTUs related to microorganisms detected in soils, permafrost, high mountain environments, and deglaciation areas (Sphingomonadales, Caulobacter or Comamonadaceae).

Bayesian analysis for nonlinear mixed-effects models under heavy-tailed distributions.

A common assumption in nonlinear mixed-effects models is the normality of both random effects and within-subject errors. However, such assumptions make inferences vulnerable to the presence of outliers. More flexible distributions are therefore necessary for modeling both sources of variability in this class of models. In the present paper, I consider an extension of the nonlinear mixed-effects models in which random effects and within-subject errors are assumed to be distributed according to a rich class of parametric models that are often used for robust inference. The class of distributions I consider is the scale mixture of multivariate normal distributions that consist of a wide range of symmetric and continuous distributions. This class includes heavy-tailed multivariate distributions, such as the Student's t and slash and contaminated normal. With the scale mixture of multivariate normal distributions, robustification is achieved from the tail behavior of the different distributions. A Bayesian framework is adopted, and MCMC is used to carry out posterior analysis. Model comparison using different criteria was considered. The procedures are illustrated using a real dataset from a pharmacokinetic study. I contrast results from the normal and robust models and show how the implementation can be used to detect outliers.

[Epidemiology of febrile neutropenia in adult patients with acute leukemia and lymphoma: Cohort study of public and private hospital of Santiago, Chile]. / Epidemiología de neutropenia febril en pacientes adultos con leucemia aguda y linfoma: Estudio de cohorte en hospitales público y privado de Santiago, Chile.

INTRODUCTION: Febrile neutropenia (FN) is a common complication of patients undergoing chemotherapy (QMT). Clinical presentation is varied, from mild fever to severe sepsis with invasive bacterial infection (IBI) or invasive fungal infection (IFI), with great impact on prognosis and patient mortality. PATIENTS AND METHODS: Prospective cohort study of FN episodes in adult patients with acute leukemia (AL) or lymphoma (L), diagnosed and treated at the Hospital Clínico Universidad Católica and Hospital Dr. Sótero del Río in Santiago from April 2010 to January 2012. RESULTS: 130 patients were included with 105 episodes of NF, with an incidence of 0.65 per 100 days of observation, higher in AL than L (1.31 vs 0.25, p = 0.001). Etiology or clinical focus was documented in 67 (63.8%) episodes, with IBI in 33 (31.4%) and IFI in 21 (20%) cases. Mortality related to infection occurred in 4 (6.2%) patients. CONCLUSIONS: This study reports that the FN incidence and frequency of IBI and IFI during episodes are higher in AL vs. L. It is necessary to evaluate the impact of interventions to reduce its incidence, including the benefit and risk of using antibacterial and antifungal prophylaxis in high-risk subgroups.

A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage.

Pregnancy in-vitro fertilization (IVF) cases are associated with adverse first-trimester outcomes in comparison to spontaneously achieved pregnancies. Human chorionic gonadotrophin ß subunit (ß-HCG) is a well-known biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of ß-HCG during this period are related to miscarriage, ectopic pregnancy, and IVF procedure failures. Longitudinal profiles of ß-HCG can be used to distinguish between normal and abnormal pregnancies and to assist and guide the clinician in better management and monitoring of post-IVF pregnancies. Therefore, assessing the association between longitudinally measured ß-HCG serum concentration and time to early miscarriage is of crucial interest to clinicians. A common joint modeling approach is to use the longitudinal ß-HCG trajectory to determine the risk of miscarriage. This work was motivated by a follow-up study with normal and abnormal pregnancies where ß-HCG serum concentrations were measured in 173 young women during a gestational age of 9-86 days in Santiago, Chile. Some women experienced a miscarriage event, and their exact event times were unknown, so we have interval-censored data, with the event occurring between the last time of the observed measurement and ten days later. However, for those women belonging to the normal pregnancy group; that is, carrying a pregnancy to a full-term event, right censoring data are observed. Estimation procedures are based on the Stochastic Approximation of the Expectation-Maximization (SAEM) algorithm.

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease.

Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.

Spatial and temporal distribution of reported dengue cases and hot spot identification in Quezon City, Philippines, 2010-2017.

BACKGROUND: Dengue remains a major public health problem in the Philippines, particularly in urban areas of the National Capital Region. Thematic mapping using geographic information systems complemented by spatial analysis such as cluster analysis and hot spot detection can provide useful information to guide preventive measures and control strategies against dengue. Hence, this study was aimed to describe the spatiotemporal distribution of dengue incidence and identify dengue hot spots by barangay using reported cases from Quezon City, the Philippines from 2010 to 2017. METHODS: Reported dengue case data at barangay level from January 1, 2010 to December 31, 2017 were obtained from the Quezon City Epidemiology and Surveillance Unit. The annual incidence rate of dengue from 2010 to 2017, expressed as the total number of dengue cases per 10,000 population in each year, was calculated for each barangay. Thematic mapping, global cluster analysis, and hot spot analysis were performed using ArcGIS 10.3.1. RESULTS: The number of reported dengue cases and their spatial distribution varied highly between years. Local clusters were evident during the study period. Eighteen barangays were identified as hot spots. CONCLUSIONS: Considering the spatial heterogeneity and instability of hot spots in Quezon City across years, efforts towards the containment of dengue can be made more targeted, and efficient with the application of hot spot analysis in routine surveillance. This may be useful not only for the control of dengue but also for other diseases, and for public health planning, monitoring, and evaluation.

Logistic regression when covariates are random effects from a non-linear mixed model.

In many studies, the association of longitudinal measurements of a continuous response and a binary outcome are often of interest. A convenient framework for this type of problems is the joint model, which is formulated to investigate the association between a binary outcome and features of longitudinal measurements through a common set of latent random effects. The joint model, which is the focus of this article, is a logistic regression model with covariates defined as the individual-specific random effects in a non-linear mixed-effects model (NLMEM) for the longitudinal measurements. We discuss different estimation procedures, which include two-stage, best linear unbiased predictors, and various numerical integration techniques. The proposed methods are illustrated using a real data set where the objective is to study the association between longitudinal hormone levels and the pregnancy outcome in a group of young women. The numerical performance of the estimating methods is also evaluated by means of simulation.

Bayesian analysis for nonlinear regression model under skewed errors, with application in growth curves.

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Error-rate estimation in discriminant analysis of non-linear longitudinal data: A comparison of resampling methods.

Consider longitudinal observations across different subjects such that the underlying distribution is determined by a non-linear mixed-effects model. In this context, we look at the misclassification error rate for allocating future subjects using cross-validation, bootstrap algorithms (parametric bootstrap, leave-one-out, .632 and [Formula: see text]), and bootstrap cross-validation (which combines the first two approaches), and conduct a numerical study to compare the performance of the different methods. The simulation and comparisons in this study are motivated by real observations from a pregnancy study in which one of the main objectives is to predict normal versus abnormal pregnancy outcomes based on information gathered at early stages. Since in this type of studies it is not uncommon to have insufficient data to simultaneously solve the classification problem and estimate the misclassification error rate, we put special attention to situations when only a small sample size is available. We discuss how the misclassification error rate estimates may be affected by the sample size in terms of variability and bias, and examine conditions under which the misclassification error rate estimates perform reasonably well.

"I am not promiscuous enough!": Exploring the low uptake of HIV testing by gay men and other men who have sex with men in Metro Manila, Philippines.

The Philippines faces a severe HIV epidemic among gay and other men who have sex with men (MSM). HIV testing uptake remains low. A case series of 12 men from Metro Manila were interviewed to explore barriers to uptake of HIV testing services. Most did not see the need to get tested for HIV despite significant risk, based on the misconception they were feeling well or showed no symptoms. Being of a higher socioeconomic class, feeling morally superior to other gay men, distance of the testing facility, fear of what will happen once infected, fear of HIV- and sexual stigma, fear of side effects of antiretroviral drugs and fear of high health care expenses after testing positive for HIV were key reasons why MSM kept postponing their test. Misconceptions about HIV risk, disease, and treatment and care need to be addressed in order to increase uptake of HIV services in this population.