Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

Spatial analysis and investigation of fire events occurrences in the Valencian Community, Spain

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Spatial point pattern analysis of gorilla nest sites in the Kagwene Sanctuary, Cameroon: Towards understanding the nesting behavior of a critically endangered subspecies

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Modelling HIV/AIDS length of stay in Portugal

Tese apresentada como requisito parcial para obtenção do grau de Doutor em Estatística e Gestão de Informação pelo Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa

Spatio-temporal analyses of the relationship between armed conflict and climate change in the eastern Africa

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Economic analysis in health care regulation : three essays

ABSTRACT - It is the purpose of the present thesis to emphasize, through a series of examples, the need and value of appropriate pre-analysis of the impact of health care regulation. Specifically, the thesis presents three papers on the theme of regulation in different aspects of health care provision and financing. The first two consist of economic analyses of the impact of health care regulation and the third comprises the creation of an instrument for supporting economic analysis of health care regulation, namely in the field of evaluation of health care programs. The first paper develops a model of health plan competition and pricing in order to understand the dynamics of health plan entry and exit in the presence of switching costs and alternative health premium payment systems. We build an explicit model of death spirals, in which profitmaximizing competing health plans find it optimal to adopt a pattern of increasing relative prices culminating in health plan exit. We find the steady-state numerical solution for the price sequence and the plan’s optimal length of life through simulation and do some comparative statics. This allows us to show that using risk adjusted premiums and imposing price floors are effective at reducing death spirals and switching costs, while having employees pay a fixed share of the premium enhances death spirals and increases switching costs. Price regulation of pharmaceuticals is one of the cost control measures adopted by the Portuguese government, as in many European countries. When such regulation decreases the products’ real price over time, it may create an incentive for product turnover. Using panel data for the period of 1997 through 2003 on drug packages sold in Portuguese pharmacies, the second paper addresses the question of whether price control policies create an incentive for product withdrawal. Our work builds the product survival literature by accounting for unobservable product characteristics and heterogeneity among consumers when constructing quality, price control and competition indexes. These indexes are then used as covariates in a Cox proportional hazard model. We find that, indeed, price control measures increase the probability of exit, and that such effect is not verified in OTC market where no such price regulation measures exist. We also find quality to have a significant positive impact on product survival. In the third paper, we develop a microsimulation discrete events model (MSDEM) for costeffectiveness analysis of Human Immunodeficiency Virus treatment, simulating individual paths from antiretroviral therapy (ART) initiation to death. Four driving forces determine the course of events: CD4+ cell count, viral load resistance and adherence. A novel feature of the model with respect to the previous MSDEMs is that distributions of time to event depend on individuals’ characteristics and past history. Time to event was modeled using parametric survival analysis. Events modeled include: viral suppression, regimen switch due virological failure, regimen switch due to other reasons, resistance development, hospitalization, AIDS events, and death. Disease progression is structured according to therapy lines and the model is parameterized with cohort Portuguese observational data. An application of the model is presented comparing the cost-effectiveness ART initiation with two nucleoside analogue reverse transcriptase inhibitors (NRTI) plus one non-nucleoside reverse transcriptase inhibitor(NNRTI) to two NRTI plus boosted protease inhibitor (PI/r) in HIV- 1 infected individuals. We find 2NRTI+NNRTI to be a dominant strategy. Results predicted by the model reproduce those of the data used for parameterization and are in line with those published in the literature.

Evolution of methodology for the study of adaptability and stability in cultivated species.

The GxE interaction only became widely discussed from evolutionary studies and evaluations of the causes of behavioral changes of species cultivated in environments. In the last 60 years, several methodologies for the study of adaptability and stability of genotypes in multiple environments trials were developed in order to assist the breeder's choice regarding which genotypes are more stable and which are the most suitable for the crops in the most diverse environments. The methods that use linear regression analysis were the first to be used in a general way by breeders, followed by multivariate analysis methods and mixed models. The need to identify the genetic and environmental causes that are behind the GxE interaction led to the development of new models that include the use of covariates and which can also include both multivariate methods and mixed modeling. However, further studies are needed to identify the causes of GxE interaction as well as for the more accurate measurement of its effects on phenotypic expression of varieties in competition trials carried out in genetic breeding programs.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Spatio-temporal modelling of environmental data

Programa Doutoral em Matemática e Aplicações.

Spatial-temporal modellization of the NO2 concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO2 levels, by using geostatisti- cal approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This method- ology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Aplicação da metodologia ROC na avaliação de desempenho de índices de gravidade clínica em unidades de neonatologia de Portugal

Tese de Doutoramento em Engenharia Industrial e de Sistemas

Longitudinal analysis of tumor marker CEA of breast cancer patients from Braga's hospital

Allied to an epidemiological study of population of the Senology Unit of Braga’s Hospital that have been diagnosed with malignant breast cancer, we describe the progression in time of repeated measurements of tumor marker Carcinoembryonic antigen (CEA). Our main purpose is to describe the progression of this tumor marker as a function of possible risk factors and, hence, to understand how these risk factors influences that progression. The response variable, values of CEA, was analyzed making use of longitudinal models, testing for different correlation structures. The same covariates used in a previous survival analysis were considered in the longitudinal model. The reference time used was time from diagnose until death from breast cancer. For diagnostic of the models fitted we have used empirical and theoretical variograms. To evaluate the fixed term of the longitudinal model we have tested for a changing point on the effect of time on the tumor marker progression. A longitudinal model was also fitted only to the subset of patients that died from breast cancer, using the reference time as time from date of death until blood test.

Evaluation of Sexual Dimorphism in the Efficacy and Safety of Simvastatin/Atorvastatin Therapy in a Southern Brazilian Cohort

Background: Dyslipidemia is the primary risk factor for cardiovascular disease, and statins have been effective in controlling lipid levels. Sex differences in the pharmacokinetics and pharmacodynamics of statins contribute to interindividual variations in drug efficacy and toxicity. Objective: To evaluate the presence of sexual dimorphism in the efficacy and safety of simvastatin/atorvastatin treatment. Methods: Lipid levels of 495 patients (331 women and 164 men) were measured at baseline and after 6 ± 3 months of simvastatin/atorvastatin treatment to assess the efficacy and safety profiles of both drugs. Results: Women had higher baseline levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) compared with men (p < 0.0001). After treatment, women exhibited a greater decrease in plasma TC and LDL-C levels compared with men. After adjustment for covariates, baseline levels of TC and LDL-C influenced more than 30% of the efficacy of lipid-lowering therapy (p < 0.001), regardless of sex. Myalgia [with or without changes in creatine phosphokinase (CPK) levels] occurred more frequently in women (25.9%; p = 0.002), whereas an increase in CPK and/or abnormal liver function was more frequent in in men (17.9%; p = 0.017). Conclusions: Our results show that baseline TC and LDL-C levels are the main predictors of simvastatin/atorvastatin therapy efficacy, regardless of sex. In addition, they suggest the presence of sexual dimorphism in the safety of simvastatin/atorvastatin. The effect of sex differences on receptors, transporter proteins, and gene expression pathways needs to be better evaluated and characterized to confirm these observations.

Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study.

AIMS/HYPOTHESIS: Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors. METHODS: We constructed an additive genetic score using the most replicated single-nucleotide polymorphisms (SNPs) within 15 type 2 diabetes-susceptibility genes, weighting each SNP with its reported effect. We tested this score in the extensively phenotyped population-based cross-sectional CoLaus Study in Lausanne, Switzerland (n = 5,360), involving 356 diabetic individuals. RESULTS: The clinical predictors of prevalent diabetes were age, BMI, family history of diabetes, WHR, and triacylglycerol/HDL-cholesterol ratio. After adjustment for these variables, the risk of diabetes was 2.7 (95% CI 1.8-4.0, p = 0.000006) for individuals with a genetic score within the top quintile, compared with the bottom quintile. Adding the genetic score to the clinical covariates improved the area under the receiver operating characteristic curve slightly (from 0.86 to 0.87), yet significantly (p = 0.002). BMI was similar in these two extreme quintiles. CONCLUSIONS/INTERPRETATION: In this population, a simple weighted 15 SNP-based genetic score provides additional information over clinical predictors of prevalent diabetes. At this stage, however, the clinical benefit of this genetic information is limited.

Coffee and tea intake and risk of head and neck cancer: pooled analysis in the International Head and Neck Cancer Epidemiology Consortium.

BACKGROUND: Only a few studies have explored the relation between coffee and tea intake and head and neck cancers, with inconsistent results. METHODS: We pooled individual-level data from nine case-control studies of head and neck cancers, including 5,139 cases and 9,028 controls. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI), adjusting for potential confounders. RESULTS: Caffeinated coffee intake was inversely related with the risk of cancer of the oral cavity and pharynx: the ORs were 0.96 (95% CI, 0.94-0.98) for an increment of 1 cup per day and 0.61 (95% CI, 0.47-0.80) in drinkers of >4 cups per day versus nondrinkers. This latter estimate was consistent for different anatomic sites (OR, 0.46; 95% CI, 0.30-0.71 for oral cavity; OR, 0.58; 95% CI, 0.41-0.82 for oropharynx/hypopharynx; and OR, 0.61; 95% CI, 0.37-1.01 for oral cavity/pharynx not otherwise specified) and across strata of selected covariates. No association of caffeinated coffee drinking was found with laryngeal cancer (OR, 0.96; 95% CI, 0.64-1.45 in drinkers of >4 cups per day versus nondrinkers). Data on decaffeinated coffee were too sparse for detailed analysis, but indicated no increased risk. Tea intake was not associated with head and neck cancer risk (OR, 0.99; 95% CI, 0.89-1.11 for drinkers versus nondrinkers). CONCLUSIONS: This pooled analysis of case-control studies supports the hypothesis of an inverse association between caffeinated coffee drinking and risk of cancer of the oral cavity and pharynx. IMPACT: Given widespread use of coffee and the relatively high incidence and low survival of head and neck cancers, the observed inverse association may have appreciable public health relevance.