The diploma thesis is dealing with a description, emplacement and function of the causal adverbial complement in the contemporary French and Czech language. This paper is divided into a theoretical and a practical part. In the theoretical part, the topic of causality is elucidated and the notion of causal adverbial complement in the French and Czech linguistic terminology is defined. Further, it works with syntactic and semantic features of causal adverbial complement, observes the possible valence understanding of this sentence member, and deals with the question of its extra-predicative and intra-predicative emplacement, as well. Furthermore, the attention is drawn to the position of causal adverbial complement in a relationship to the functional sentence perspective. The practical part is dedicated to corpus analysis of the chosen causal utterances, which implement the causal adverbial complements. The main focus is given to their emplacement in the French and Czech sentence.

In this study of causal decision-making, a video game was adapted to explore factors affecting causal judgment in a dynamic setting. In the experiment, participants were presented with groups of potential targets. Causal delay and number of alternatives were varied. The participants were tasked with discriminating which one of the potential targets was producing a secondary event in the form of distal explosions on objects that the participant was instructed to preserve. Choice accuracies and latencies were recorded for each participant. For the analysis, choice accuracies were converted into discriminability metrics using signal detection theory. The experiment revealed a main effect of delay on discriminability but no effect of the number of alternatives. There were main effects of the number of alternatives, sex, as well as a Delay × Number of alternatives interaction on latency. The results suggest that discriminability is maintained across different numbers of targets by compensating with longer observation times.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / Apesar do crescente reconhecimento do potencial dos diagramas causais por epidemiologistas, essa técnica ainda é pouco utilizada na investigação epidemiológica. Uma das possíveis razões é que muitos temas de investigação exigem modelos causais complexos. Neste trabalho, a relação entre estresse ocupacional e obesidade é utilizada como um exemplo de aplicação de diagramas causais em questões relacionadas a confundimento. São apresentadas etapas da utilização dos diagramas causais, incluindo a construção do gráfico acíclico direcionado, seleção de variáveis para ajuste estatístico e a derivação das implicações estatísticas de um diagrama causal. A principal vantagem dos diagramas causais é tornar explícitas as hipóteses adjacentes ao modelo considerado, permitindo que suas implicações possam ser analisadas criticamente, facilitando, desta forma, a identificação de possíveis fontes de viés e incerteza nos resultados de um estudo epidemiológico. / Despite the increasing recognition of the potential of causal diagrams by epidemiologists, this technique has not been widely used in epidemiological research. One possible reason is that many research topics require complex causal models. In this article, the relationship between occupational stress and obesity is used as an example of application of causal diagrams on confounding. Some steps are presented, including the construction of the directed acyclic graph, the selection of variables for statistical control and the derivation of the statistical implications of a causal diagram. The main advantage of causal diagrams is to make the assumptions explicit, thus facilitating critical evaluations and the identification of possible sources of bias and uncertainty in the results of an epidemiological study.

Submitted by Maike Costa (maiksebas@gmail.com) on 2017-06-09T14:31:16Z No. of bitstreams: 1 arquivototal.pdf: 4780800 bytes, checksum: 5e5e7789a9d23828acc66c2aa53f68ab (MD5) / Made available in DSpace on 2017-06-09T14:31:16Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 4780800 bytes, checksum: 5e5e7789a9d23828acc66c2aa53f68ab (MD5) Previous issue date: 2015-07-13 / The river Gramame’shydrographic basin – which supplies 70% of the population in the metropolitan area of João Pessoa-PB and several riverside communities – has a significant number of sources located in Pedras de Fogo-PB. This paper presents the results of a study conducted around three of the said sources: one located in the peri-urban area (Cacimba da Rosa) and another two located in the rural area (Nova Aurora and Fazendinha), due to the environmental and management problems that occur there. The study’s methodology is inspired on the application of the causal chain matrix as a step that followed the socioeconomic and environmental diagnosis of the areas around the sources. In order to do so, we identified and classified the main environmental and water management problems in that area: the conflicts for the use of water; the socioeconomic level and the sources of pollution, via exploratory walks and photographic records. These problems were analyzed according to the technical, managerial, political-social and cultural-economic causes – setting up the causal chain matrix for each of them. The analysis of each matrix revealed that the conflicts over the water use are presented as priority problem 1 with an increasing trend; the socioeconomic level is presented as a priority issue 2 and with stability tendency; and the sources of pollution are a priority issue 3 with an increasing trend in the areas around the three sources studied. We have also proved that the causal chain matrix is a useful tool to the formulation and implementation of public policies on conservation of natural resources. / A bacia hidrográfica do rio Gramame, que abastece cerca de 70% da população da área metropolitana da cidade de João Pessoa e várias comunidades ribeirinhas, tem um número significativo de nascentes localizadas no município de Pedras de Fogo. Este trabalho apresenta um estudo realizado na área de três dessas nascentes: uma periurbana (Cacimba da Rosa) e duas rurais (Nova Aurora e Fazendinha), face aos problemas ambientais e de gestão que lá ocorrem. A metodologia do estudo foi baseada na aplicação de matriz de cadeia causal, como etapa que se sucedeu ao diagnóstico socioeconômico e ambiental das áreas de abrangência ou em torno das nascentes. Para tanto, identificamos e classificamos os principais problemas ambientais e de gestão das águas nessas áreas. Entre eles foram destacados: os conflitos pelo uso da água; o nível socioeconômico e as fontes de poluição, a partir de caminhadas exploratórias e registros fotográficos. Esses problemas foram analisados segundo as causas técnicas, gerenciais, político-sociais e econômico-culturais, construindo-se para cada um deles uma matriz de cadeia causal. A análise dessas matrizes revelaram que os conflitos pelo uso da água se apresentam como problema de prioridade 1 e com tendência crescente; o nível socioeconômico aparece como problema de prioridade 2 e com tendência estável; e as fontes de poluição são um problema de prioridade 3 e com tendência crescente nas áreas em torno das três nascentes em estudo. Também constatou-se que a construção de matriz de cadeia causal é ferramenta útil à formulação e execução de políticas públicas de conservação dos recursos naturais.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / Apesar do crescente reconhecimento do potencial dos diagramas causais por epidemiologistas, essa técnica ainda é pouco utilizada na investigação epidemiológica. Uma das possíveis razões é que muitos temas de investigação exigem modelos causais complexos. Neste trabalho, a relação entre estresse ocupacional e obesidade é utilizada como um exemplo de aplicação de diagramas causais em questões relacionadas a confundimento. São apresentadas etapas da utilização dos diagramas causais, incluindo a construção do gráfico acíclico direcionado, seleção de variáveis para ajuste estatístico e a derivação das implicações estatísticas de um diagrama causal. A principal vantagem dos diagramas causais é tornar explícitas as hipóteses adjacentes ao modelo considerado, permitindo que suas implicações possam ser analisadas criticamente, facilitando, desta forma, a identificação de possíveis fontes de viés e incerteza nos resultados de um estudo epidemiológico. / Despite the increasing recognition of the potential of causal diagrams by epidemiologists, this technique has not been widely used in epidemiological research. One possible reason is that many research topics require complex causal models. In this article, the relationship between occupational stress and obesity is used as an example of application of causal diagrams on confounding. Some steps are presented, including the construction of the directed acyclic graph, the selection of variables for statistical control and the derivation of the statistical implications of a causal diagram. The main advantage of causal diagrams is to make the assumptions explicit, thus facilitating critical evaluations and the identification of possible sources of bias and uncertainty in the results of an epidemiological study.

Os modelos de transição de Markov constituem uma ferramenta de grande importância para diversas áreas do conhecimento quando são desenvolvidos estudos com medidas repetidas. Eles caracterizam-se por modelar a variável resposta ao longo do tempo condicionada a uma ou mais respostas anteriores, conhecidas como a história do processo. Além disso, é possível a inclusão de outras covariáveis. No caso das respostas binárias, pode-se construir uma matriz com as probabilidades de transição de um estado para outro. Neste trabalho, quatro abordagens diferentes de modelos de transição foram comparadas para avaliar qual estima melhor o efeito causal de tratamentos em um estudo experimental em que a variável resposta é um vetor binário medido ao longo do tempo. Estudos de simulação foram realizados levando em consideração experimentos balanceados com três tratamentos de natureza categórica. Para avaliar as estimativas foram utilizados o erro padrão, viés e percentual de cobertura dos intervalos de confiança. Os resultados mostraram que os modelos de transição marginalizados são mais indicados na situação em que um experimento é desenvolvido com um reduzido número de medidas repetidas. Como complementação, apresenta-se uma forma alternativa de realizar comparações múltiplas, uma vez que os pressupostos como normalidade, independência e homocedasticidade são violados impossibilitando o uso dos métodos tradicionais. Um experimento com dados reais no qual se registrou a presença de fungos (considerada como sucesso) em cultivos de citros e morango foi analisado por meio do modelo de transição apropriado. Para as comparações múltiplas, intervalos de confiança simultâneos foram construídos para o preditor linear e os resultados foram estendidos para a resposta média que neste caso são as probabilidades de sucesso. / The transition Markov models are a very important tool for several areas of knowledge when studies are developed with repeated measures. They are characterized by modeling the response variable over time conditional to the previous response which is known as the history. In addtion it is possible to include other covariates. In the case of binary responses, can be constructed a matrix of transition probabilities from one state to another. In this work, four different approaches to transition models were compared in order to assess which best estimates of the causal effect of treatments in an experimental studies where the outcome is a vector of binary response measured over time. Simulation study was held taking into account a balanced experiments with three treatments of categorical nature. To assess the best estimates standard error and bias, beyond the percentage of coverage were used. The results showed that the marginalized transition models are more appropriate in situation where an experiment is developed with a reduced number of repeated measurements. As complementation is presented an alternative way to perform multiple comparisons, since the assumptions as normality, independence and homoscedasticity are violated precluding the use of traditional methods. An experiment with real data where we recorded the presence of fungi (deemed successful) in citrus and strawberry crops was analyzed through the appropriate transition model. For multiple comparisons, simultaneous confidence intervals were constructed for the linear predictor and the results have been extended to the mean response in this case are the probabilities of success.

Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.

The Principal Stratification method estimates a causal intervention effect by taking account of subjects' differences in participation, adherence or compliance. The current Principal Stratification method has been mostly used in randomized intervention trials with randomization at a single (individual) level with subjects who were randomly assigned to either intervention or control condition. However, randomized intervention trials have been conducted at group level instead of individual level in many scientific fields. This is so called "two-level randomization", where randomization is conducted at a group (second) level, above an individual level but outcome is often observed at individual level within each group. The incorrect inferences may result from the causal modeling if one only considers the compliance from individual level, but ignores it or be determine it from group level for a two-level randomized trial. The Principal Stratification method thus needs to be further developed to address this issue. To extend application of the Principal Stratification method, this research developed a new methodology for causal inferences in two-level intervention trials which principal stratification can be formed by both group level and individual level compliance. Built on the original Principal Stratification method, the new method incorporates a range of alternative methods to assess causal effects on a population when data on exposure at the group level are incomplete or limited, and are data at individual level. We use the Gatekeeper Training Trial, as a motivating example as well as for illustration. This study is focused on how to examine the intervention causal effect for schools that varied by level of adoption of the intervention program (Early-adopter vs. Later-adopter). In our case, the traditional Exclusion Restriction Assumption for Principal Stratification method is no longer hold. The results show that the intervention had a stronger impact on Later-Adopter group than Early-Adopter group for all participated schools. These impacts were larger for later trained schools than earlier trained schools. The study also shows that the intervention has a different impact on middle and high schools.

Causal graphs are essential tools to find sufficient adjustment sets in observational studies. Subject matter experts can sometimes specify these graphs, but often the dependence structure of the variables, and thus the graph, is unknown even to them. In such cases, structure learning algorithms can be used to learn the graph. Early structure learning algorithms were implemented for either exclusively discrete or continuous variables. Recently, methods have been developed for structure learning on mixed data, including both continuous and discrete variables. In this thesis, three structure learning algorithms for mixed data are evaluated through a simulation study. The evaluation is based on graph recovery metrics and the ability to find a sufficient adjustment set for the average treatment effect (ATE). Depending on the intended purpose of the learned graph, the different evaluation metrics should be given varying attention. It is also concluded that the pcalg+micd algorithm learns graphs such that it is possible to find a sufficient adjustment set for the ATE in more than 99% of the cases. Moreover, the learned graphs from pcalg+micd are the most accurate compared to the true graph using the largest sample size.

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