Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

El análisis estadístico de la información generada por el seguimiento médico de una enfermedad es un reto muy importante en el ámbito de la medicina personalizada. A medida que avanza el curso evolutivo de la enfermedad en un paciente, su seguimiento genera cada vez más información que debe ser procesada inmediatamente para revisar y actualizar su pronóstico y tratamiento. Nuestro objetivo en esta tesis se centra en dicho proceso de actualización a través de métodos de inferencia secuencial en modelos conjuntos de datos longitudinales y de supervivencia desde una perspectiva Bayesiana. En concreto, proponemos la utilización de métodos secuenciales de Monte Carlo adaptados a modelos conjuntos con parámetros estáticos (independientes del tiempo) para actualizar la distribución a posteriori de los parámetros, hiperparámetros y efectos aleatorios con la intención de reducir el tiempo de computación en cada actualización del proceso inferencial. Nuestra propuesta es muy general y puede aplicarse de forma muy sencilla a las modelizaciones longitudinales y de supervivencia conjuntas más populares en la literatura científica del tema. Utilizamos dos estudios diferentes para ilustrar nuestra propuesta: (i) un modelo conjunto para datos longitudinales con pérdida de seguimiento informativa simulados a través de un mecanismo novedoso propio y (ii) un modelo conjunto para eventos con riesgo competitivos para un problema real sobre pacientes que reciben ventilación mecánica en unidades de cuidados intensivos.The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalised medicine. As the evolutionary course of a patient's disease progresses, its medical follow-up generates more and more information that should be processed immediately in order to review and update its prognosis and treatment. Our objective in this thesis focuses on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo methods for static parameter joint models in order to update the posterior distribution of the parameters, hyperparameters, and random effects with the intention of reducing computation time in each update of the inferential process. Our proposal is very general and can be easily applied to most popular joint models approaches. We illustrate our research with two different studies: (i) a joint model for longitudinal data with informative dropout simulated through an own novel mechanism, and (ii) a joint model with competing risk events for a real problem about patients receiving mechanical ventilation in intensive care units

Distribuciones previas objetivas para el modelo Dirichlet-multinomial: una aplicación en la agricultura

La chufa, Cyperus sculentus, es un tubérculo que se utiliza principalmente para la elaboración de horchata. Se cultiva en la comarca de l'Horta Nord, en Valencia, y tiene una gran importancia socioeconómica en la zona. La mancha negra en los tubérculos de chufa es una enfermedad de origen desconocido, que produce un ennegrecimiento de la piel que conlleva a su depreciación comercial, pues un cierto porcentaje de tubérculos deben ser desechados para su venta. El objetivo de este trabajo es analizar si la selección de tubérculos sin mancha negra, para su utilización como simiente, supone una mejora en la cosecha en cuanto a una menor incidencia de la enfermedad. Los datos analizados proceden de un experimento en invernadero en el que se utilizaron simientes de chufas asintomáticas y con manchas negras. El análisis estadístico se ha realizado utilizando la metodología Bayesiana. Utilizamos modelos de regresión multinomial, estudiando su sensibilidad a la elección de la distribución previa, en concreto a la elección de los parámetros en una distribución Dirichlet. A la combinación del modelo multinomial con la distribución previa Dirichlet se le conoce como modelo Dirichlet-multinomial. En conclusión, se identifica que la selección de simientes asintomáticas produce menos tubérculos con síntomas de mancha negra. Además, estos resultados son robustos en relación a elección de los parámetros de las distribuciones previas Dirichlet propuestas en la literatura como opciones objetivas o mínimo informativas

Percepção dos alunos de uma instituição privada de ensino superior sobre a construção de um sistema de irrigação automatizado em uma casa de vegetação / Perception of students from a private higher education institution about the construction of an automated irrigation system in a greenhouse

No Ensino de Botânica, algumas dificuldades são relatadas por professores e alunos no tocante ao processo de ensino-aprendizagem, destacando-se falta de articulação entre teoria e prática, conteúdos colocados em segundo plano por disponibilidade temporal, memorização de conceitos. Dessa forma, o objetivo do presente estudo foi articular a teoria e a prática de Botânica por meio da construção de um sistema de irrigação automatizado para uma casa de vegetação. Por meio de uma problemática surgida pelo estrese hídrico e térmico sofrido pelas plantas, foi elaborada a construção de tal sistema, de modo a beneficiar diferentes espécies de plantas. Além disso, foi analisa a percepção dos alunos acerca do material produzido e aquisição de conhecimentos por meio de um questionário. Os alunos apontaram as vantagens e desvantagens do material produzido, assim como descreveram a experiência como oportunidade de aprendizagem e de socialização, aplicação cotidiana e profissional, trazendo perspectivas de propostas interdisciplinares

Stochastic model used in planning the operation of hydrothermal

Algumas abordagens para o problema de Planejamento Ótimo da Operação de Sistemas Hidrotérmicos (POOSH) utilizam modelos estocásticos para representar as vazões afluentes dos reservatórios do sistema. Essas abordagens utilizam, em geral, técnicas de Programação Dinâmica Estocástica (PDE) para resolver o POOSH. Por outro lado, muitos autores têm defendido o uso dos modelos determinísticos ou, particularmente, a Programação Dinâmica Determinística (PDD) por representar de forma individualizada a interação entre as usinas hidroelétricas do sistema. Nesse contexto, esta dissertação tem por objetivo comparar o desempenho da solução do POOSH obtida via PDD com a solução obtida pela PDE, que emprega um modelo Markoviano periódico, com distribuição condicional Log-Normal Truncada para representar as vazões. Além disso, é realizada a análise com abordagem bayesiana, no modelo de vazões, para estimação dos parâmetros e previsões das vazões afluentes. Comparamos as performances simulando a operação das usinas hidroelétricas de Furnas e Sobradinho, considerando séries de vazões geradas artificialmenteSome approaches for problem of Optimal Operation Planning of Hydrothermal Systems (OOPHS) use stochastic models to represent the inflows in the reservoirs that compose the system. These approaches typically use the Stochastic Dynamic Programming (SDP) to solve the OOPHS. On the other hand, many authors defend the use of deterministic models and, particularly, the Deterministic Dynamic Programming (DDP) since it individually represents the interaction between the hydroelectric plants. In this context, this dissertation aims to compare the performance of the OOPHS solution obtained via DDP with the one given by SDP, which employs a periodic Markovian model with conditional Truncated Log-Normal distribution to represent the inflows. Furthermore, it is performed a bayesian approach analysis, in the inflow model, for estimating the parameters and forecasting the inflows. We have compared the performances of the DDP and SDP solutions by simulating the hydroelectric plants of Furnas and Sobradinho, employing artificially generated serie

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data