Rolando De la Cruz
Pontifical Catholic University of Chile
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Publication
Featured researches published by Rolando De la Cruz.
Ultrasound in Obstetrics & Gynecology | 2009
Jacques Jani; Kypros H. Nicolaides; Eduard Gratacós; Catalina Valencia; E. Done; J-M Martinez; Léonardo Gucciardo; Rolando De la Cruz; Jan Deprest
To examine operative and perinatal aspects of fetal endoscopic tracheal occlusion (FETO) in congenital diaphragmatic hernia (CDH).
The Journal of Clinical Endocrinology and Metabolism | 2010
Juan P. Valderas; Verónica Irribarra; Camilo Boza; Rolando De la Cruz; Yessica Liberona; Acosta Am; Macarena Yolito; Alberto Maiz
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. SETTING The study was conducted at the Departments of Nutrition and Digestive Surgery at a university hospital. PARTICIPANTS PARTICIPANTS included three groups of eight patients with similar body mass indexes (RYGB 37.8 +/- 0.8, SG 35.3 +/- 0.7, and MED 39.1 +/- 1.7 kg/m(2), P = NS) and eight lean controls (body mass index 21.7 +/- 0.7 kg/m(2)). MAIN OUTCOME MEASURES Total plasma PYY, hunger, and satiety visual analog scales in fasting and after ingestion of a standard test meal were measured. RESULTS At baseline there were no differences in the area under the curve (AUC) of PYY, hunger, or satiety in obese groups. Two months after the interventions, RYGB, SG, and MED groups achieved similar weight loss (17.7 +/- 3, 14.9 +/- 2.4, 16.6 +/- 4%, respectively, P = NS). PYY AUC increased in RYGB (P < 0.001) and SG (P < 0.05) and did not change in MED. PYY levels decreased at fasting, 30 min, and 180 min after a standard test meal in MED (P < 0.05). Hunger AUC decreased in RYGB (P < 0.05). Satiety AUC increased in RYGB (P < 0.05) and SG (P < 0.05). Appetite did not change in MED. PYY AUC correlated with satiety AUC (r = 0.35, P < 0.05). CONCLUSION RYGB and SG increased PYY and reduced appetite. MED failed to produce changes. Different effects occur despite similar weight loss. This suggests that the weight-loss effects of these procedures are enhanced by an increase in PYY and satiety.
Statistics and Computing | 2012
Cristian Meza; Felipe Osorio; Rolando De la Cruz
Nonlinear mixed-effects models are very useful to analyze repeated measures data and are used in a variety of applications. Normal distributions for random effects and residual errors are usually assumed, but such assumptions make inferences vulnerable to the presence of outliers. In this work, we introduce an extension of a normal nonlinear mixed-effects model considering a subclass of elliptical contoured distributions for both random effects and residual errors. This elliptical subclass, the scale mixtures of normal (SMN) distributions, includes heavy-tailed multivariate distributions, such as Student-t, the contaminated normal and slash, among others, and represents an interesting alternative to outliers accommodation maintaining the elegance and simplicity of the maximum likelihood theory. We propose an exact estimation procedure to obtain the maximum likelihood estimates of the fixed-effects and variance components, using a stochastic approximation of the EM algorithm. We compare the performance of the normal and the SMN models with two real data sets.
PLOS ONE | 2011
José María de los Santos; Rolando De la Cruz; Claus Holst; Katrine Grau; Carolina Naranjo; Alberto Maiz; Arne Astrup; Wim H. M. Saris; Ian A. Macdonald; Jean-Michel Oppert; Torben Hansen; Oluf Pedersen; Thorkild I. A. Sørensen; J. Alfredo Martínez
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. The aim of this study is to assess the association in adults between allelic variants of MC3R with weight loss induced by energy-restricted diets. Subjects and Methods This research is based on the NUGENOB study, a trial conducted to assess weight loss during a 10-week dietary intervention involving two different hypo-energetic (high-fat and low-fat) diets. A total of 760 obese patients were genotyped for 10 single nucleotide polymorphisms covering the single exon of MC3R gene and its flanking regions, including the missense variants Thr6Lys and Val81Ile. Linear mixed models and haplotype-based analysis were carried out to assess the potential association between genetic polymorphisms and differential weight loss, fat mass loss, waist change and resting energy expenditure changes. Results No differences in drop-out rate were found by MC3R genotypes. The rs6014646 polymorphism was significantly associated with weight loss using co-dominant (p = 0.04) and dominant models (p = 0.03). These p-values were not statistically significant after strict control for multiple testing. Haplotype-based multivariate analysis using permutations showed that rs3827103–rs1543873 (p = 0.06), rs6014646–rs6024730 (p = 0.05) and rs3746619–rs3827103 (p = 0.10) displayed near-statistical significant results in relation to weight loss. No other significant associations or gene*diet interactions were detected for weight loss, fat mass loss, waist change and resting energy expenditure changes. Conclusion The study provided overall sufficient evidence to support that there is no major effect of genetic variants of MC3R and differential weight loss after a 10-week dietary intervention with hypo-energetic diets in obese Europeans.
Computational Statistics & Data Analysis | 2008
Rolando De la Cruz
Typically, the fundamental assumption in non-linear regression models is the normality of the errors. Even though this model offers great flexibility for modeling these effects, it suffers from the same lack of robustness against departures from distributional assumptions as other statistical models based on the Gaussian distribution. It is of practical interest, therefore, to study non-linear models which are less sensitive to departures from normality, as well as related assumptions. Thus the current methods proposed for linear regression models need to be extended to non-linear regression models. This paper discusses non-linear regression models for longitudinal data with errors that follow a skew-elliptical distribution. Additionally, we discuss Bayesian statistical methods for the classification of observations into two or more groups based on skew-models for non-linear longitudinal profiles. Parameter estimation for a discriminant model that classifies individuals into distinct predefined groups or populations uses appropriate posterior simulation schemes. The methods are illustrated with data from a study involving 173 pregnant women. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from beta human chorionic gonadotropin data available at early stages of pregnancy.
Biometrical Journal | 2009
Rolando De la Cruz; Márcia D. Branco
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.
Biometrics | 2015
Ana Arribas-Gil; Rolando De la Cruz; Emilie Lebarbier; Cristian Meza
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 SNMMs 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.
Biometrical Journal | 2011
Rolando De la Cruz; Guillermo Marshall; Fernando A. Quintana
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.
Pharmaceutical Statistics | 2014
Rolando De la Cruz
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 Students 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.
Journal of Multivariate Analysis | 2016
Rolando De la Cruz; Cristian Meza; Ana Arribas-Gil; Raymond J. Carroll
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., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification.