Marina Silva Paez

Multilevel analysis of ADHD, anxiety and depression symptoms aggregation in families

A strong genetic role in the etiology of attention-deficit hyperactivity disorder (ADHD) has been demonstrated by several studies using different methodologies. Shortcomings of genetic studies often include the lack of golden standard practices for diagnosis for ADHD, the use of categorical instead of a dimensional approach, and the disregard for assortative mating phenomenon in parents. The current study aimed to overcome these shortcomings and analyze data through a novel statistical approach, using multilevel analyses with Bayesian procedures and a specific mathematical model, which takes into account data with an elevated number of zero responses (expected in samples with few or no ADHD symptoms). Correlations of parental clinical variables (ADHD, anxiety and depression) to offspring psychopathology may vary according to gender and type of symptoms. We aimed to investigate how those variables interact within each other. One hundred families, comprising a proband child or adolescent with ADHD or a typically developing child or adolescent were included and all family members (both biological parents, the proband child or adolescent and their sibling) were examined through semi-structured interviews using DSM-IV criteria. Results indicated that: (a) maternal clinical variables (ADHD, anxiety and depression) were more correlated with offspring variables than paternal ones; (b) maternal inattention (but not hyperactivity) was correlated with both inattention and hyperactivity in the offspring; (c) maternal anxiety was correlated with offspring inattention; on the other hand, maternal inattention was correlated with anxiety in the offspring. Although a family study design limits the possibility of revealing causality and cannot disentangle genetic and environmental factors, our findings suggest that ADHD, anxiety and depression are variables that correlate in families and should be addressed together. Maternal variables significantly correlated with offspring variables, but the paternal variables did not.

Interpolation performance of a spatio-temporal model with spatially varying coefficients: application to PM10 concentrations in Rio de Janeiro

In this work we present a Bayesian analysis in linear regression models with spatially varying coefficients for modeling and inference in spatio-temporal processes. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial spatial heterogeneity. We describe for this model how to make inference about the regression coefficients and response processes under two scenarios: when the explanatory processes are known throughout the study region, and when they are known only at the sampling locations. Using a simulation experiment we investigate how parameter inference and interpolation performance are affected by some features of the data and prior distribution that is used. The proposed methodology is used to model the dataset on PM10 levels in the metropolitan region of Rio de Janeiro presented in Paez and Gamerman (2003).

Point pattern analysis with spatially varying covariate effects, applied to the study of cerebrovascular deaths

This article proposes a modeling approach for handling spatial heterogeneity present in the study of the geographical pattern of deaths due to cerebrovascular disease.The framework involvesa point pattern analysis with components exhibiting spatial variation. Preliminary studies indicate that mortality of this disease and the effect of relevant covariates do not exhibit uniform geographic distribution. Our model extends a previously proposed model in the literature that uses spatial and non-spatial variables by allowing for spatial variation of the effect of non-spatial covariates. A number of relative risk indicators are derived by comparing different covariate levels, different geographic locations, or both. The methodology is applied to the study of the geographical death pattern of cerebrovascular deaths in the city of Rio de Janeiro. The results compare well against existing alternatives, including fixed covariate effects. Our model is able to capture and highlight important data information that would not be noticed otherwise, providing information that is required for appropriate health decision-making.

State space models with spatial deformation

Space deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal stationarity. In real applications, however, the temporal stationarity assumption is inappropriate and unrealistic. In this work we propose a spatial-temporal model whose temporal trend is modeled through state space models and a spatially varying anisotropy is modeled through spatial deformation, under the Bayesian approach. A distinctive feature of our approach is the consideration of model uncertainty in an unified framework. Our model has a clear advantage over the ones proposed so far in the literature when the main objective of the study is to perform spatial interpolation for fixed points in time. Approximations of the posterior distributions of the model parameters are obtained via Markov chain Monte Carlo methods. This allows for prediction of the process values in space and time as well as handling of missing values. Two applications are presented: the first one to model concentrations of sulfur dioxide in the eastern United States and the second one to model monthly minimum temperatures in the State of Rio de Janeiro.

Bayesian dynamic models for space-time point processes

In this work we propose a model for the intensity of a space-time point process, specified by a sequence of spatial surfaces that evolve dynamically in time. This specification allows flexible structures for the components of the model, in order to handle temporal and spatial variations both separately and jointly. These structures make use of state-space and Gaussian process tools. They are combined to create a richer class of models for the intensity process. This structural approach allows for a decomposition of the intensity into purely temporal, purely spatial and spatio-temporal terms. Inference is performed under a fully Bayesian approach, with the description of simulation-based and analytic methods for approximating the posterior distributions. The proposed methodology is applied to model the incidence of impulses in the small intestine, illustrated by a data-set obtained through an experiment conducted in cats, in order to understand the interaction between the nervous and digestive systems. This application illustrates the usefulness of the proposed methodology and shows it compares favorably against existing alternatives. The paper is concluded with a few directions for further investigation.

Modeling with a large class of unimodal multivariate distributions

ABSTRACT In this paper we introduce a new class of multivariate unimodal distributions, motivated by Khintchines representation for unimodal densities on the real line. We start by introducing a new class of unimodal distributions which can then be naturally extended to higher dimensions, using the multivariate Gaussian copula. Under both univariate and multivariate settings, we provide MCMC algorithms to perform inference about the model parameters and predictive densities. The methodology is illustrated with univariate and bivariate examples, and with variables taken from a real data set.

Acantose nigricante: inter-relações metabólicas inerentes à síndrome dos ovários policísticos

OBJETIVO: Estabelecer a prevalencia da acantose nigricante (AN) no contexto da sindrome dos ovarios policisticos (SOP) e as respectivas associacoes com a obesidade, a resistencia insulinica (RI), a insulinemia e a sindrome metabolica (SM).METODOS: Em um estudo transversal e prospectivo, foram selecionadas cem pacientes acometidas pela SOP, diagnosticadas segundo o Consenso de Rotterdam (2003). O exame cutâneo incluiu, alem da verificacao da presenca da AN, a presenca do hirsutismo (escore ≥8) e da acne. Foram investigados os dados clinicos e bioquimicos, os fatores de risco cardiovascular que se fazem presentes na SM, como circunferencia abdominal (CA), obesidade, hipertensao e os indices de HDL e triglicerides. O modelo de afericao da resistencia insulinica foi realizado por meio do teste homeostatic model assessment of insulin resistance(HOMA-IR).RESULTADOS: A prevalencia da AN (53%) mostrou correspondencia significativa com o hirsutismo (p=0,02), o indice de massa corporea (IMC) (p<0,01), a insulinemia basal (p<0,01), o HOMA-IR (p<0,01) e a SM (p<0,05). A SM alcancou a prevalencia de 36% e associou-se significativamente apenas com a AN (p<0,01). Conquanto ausente o diabetes mellitus, sobressaem as conotacoes do HOMA-IR alterado (p=0,01) com a SM (p<5%) e a AN (p<0,01).CONCLUSOES: A AN integra o quadro fenotipico grave da SOP como mais um signo previsivel dos riscos da doenca cardiovascular.