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 β subunit (β-HCG) is a well-known biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of β-HCG during this period are related to miscarriage, ectopic pregnancy, and IVF procedure failures.

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.

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).

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.

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.

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.

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.

"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.

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.

[Early child development inequalities and associated factors between public and private providers at metropolitan region in Chile].

Unlabelled: 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.

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.

Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements.

Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al.

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.

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.

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.

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.

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.

Allelic variants of melanocortin 3 receptor gene (MC3R) and weight loss in obesity: a randomised trial of hypo-energetic high- versus low-fat diets.

Introduction: The melanocortin system plays an important role in energy homeostasis. Mice genetically deficient in the melanocortin-3 receptor gene have a normal body weight with increased body fat, mild hypophagia compared to wild-type mice. In humans, Thr6Lys and Val81Ile variants of the melanocortin-3 receptor gene (MC3R) have been associated with childhood obesity, higher BMI Z-score and elevated body fat percentage compared to non-carriers.

Medical and surgical treatments for obesity have opposite effects on peptide YY and appetite: a prospective study controlled for weight loss.

Context: The effects of medical and surgical treatments for obesity on peptide YY (PYY) levels, in patients with similar weight loss, remain unclear.

Objective: The objective of the study was to assess PYY and appetite before and after Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG), and medical treatment (MED).

Design: This was a prospective, controlled, nonrandomized study.

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.