Chronic diseases place a substantial economic burden on the health care system. Physical inactivity, poor diet and smoking are considered to be the main causes of high rates of chronic disease. Evidence clearly supports the positive influence of physical activity on health determinants, other health outcomes and quality of life. This implies that an increase in physical activity improves general health status and has the potential to reduce utilization of expensive healthcare services and disability days. Earlier studies show that physical activity programs would be an effective way of providing preventive care for people with chronic conditions. However studies that relate physical activity programs to health care utilization are limited in economics literature.<p>The aim of this paper is to examine the impact of physical activity programs on healthcare utilization. From 2002 to 2003, adults over the age of 50 years, in a mid-size Canadian city, presenting with excess weight, type 2 diabetes, hypertension, hyperlipidemia or osteoarthritis were recruited. Following a screening process, eligible participants were randomly assigned to one of two programs: a class-based structured program or a home-based unstructured program. Validated questionnaires related to health status and quality of life were completed and physical tests were carried out at baseline, 3, 6, 12 months and 24 months after the program initiation. In addition participants use of physician and hospital services and pharmaceutical expenditures were accessed through their administrative data files for three years, one year before and two years after the intervention. Using administrative data from Sask Health and individual level survey data the effects of physical activity programs on health care utilization were estimated. The results showed that structured physical activity program can reduce annual physician costs significantly. The exponential effect of aging was found to be significant on hospital utilization, and the number of comorbidities was found to be significant on prescription drug utilization.

This thesis consists of four papers that are related to commonly used propensity score-based estimators for average causal effects. The first paper starts with the observation that researchers often have access to data containing lots of covariates that are correlated. We therefore study the effect of correlation on the asymptotic variance of an inverse probability weighting and a matching estimator. Under the assumptions of normally distributed covariates, constant causal effect, and potential outcomes and a logit that are linear in the parameters we show that the correlation influences the asymptotic efficiency of the estimators differently, both with regard to direction and magnitude. Further, the strength of the confounding towards the outcome and the treatment plays an important role. The second paper extends the first paper in that the estimators are studied under the more realistic setting of using the estimated propensity score. We also relax several assumptions made in the first paper, and include the doubly robust estimator. Again, the results show that the correlation may increase or decrease the variances of the estimators, but we also observe that several aspects influence how correlation affects the variance of the estimators, such as the choice of estimator, the strength of the confounding towards the outcome and the treatment, and whether constant or non-constant causal effect is present. The third paper concerns estimation of the asymptotic variance of a propensity score matching estimator. Simulations show that large gains can be made for the mean squared error by properly selecting smoothing parameters of the variance estimator and that a residual-based local linear estimator may be a more efficient estimator for the asymptotic variance. The specification of the variance estimator is shown to be crucial when evaluating the effect of right heart catheterisation, i.e. we show either a negative effect on survival or no significant effect depending on the choice of smoothing parameters.   In the fourth paper, we provide an analytic expression for the covariance matrix of logistic regression with normally distributed regressors. This paper is related to the other papers in that logistic regression is commonly used to estimate the propensity score.

This thesis contributes to the research area of causal inference, where estimation of the effect of a treatment on an outcome of interest is the main objective. Some aspects of the estimation of average causal effects in observational studies in general, and case-control studies in particular, are explored. An important part of estimating causal effects in an observational study is to control for covariates. The first paper of this thesis concerns the selection of minimal covariate sets sufficient for unconfoundedness of the treatment assignment. A data-driven implementation of two covariate selection algorithms is proposed and evaluated. A common sampling scheme in epidemiology, and when investigating rare events, is the case-control design. In the second paper we study estimators of the marginal causal odds ratio in matched and independent case-control designs. Estimators that, under a logistic regression model, utilize information about the known prevalence of being a case is examined and compared through simulations. The third paper investigates the particular situation where case-control sampled data is reused to estimate the effect of the case-defining event on an outcome of interest. The consequence of ignoring the design when estimating the average causal effect is discussed and a design-weighted matching estimator is proposed. The performance of the estimator is evaluated with simulation experiments, when matching on the covariates directly and when matching on the propensity score. The last paper studies the effect of type 1 diabetes mellitus (T1DM) on school achievements using data from the Swedish Childhood Diabetes Register, a population-based incidence register. We apply theoretical results from the second and third papers in the estimation of the average causal effect within the T1DM population. A matching estimator that accounts for the matched case-control design is used.

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.

Submitted by Rafael Burjack (rburjack@fgvmail.br) on 2012-10-11T20:27:44Z No. of bitstreams: 1 file_0_Tese.pdf: 550931 bytes, checksum: 38cf0263f5b9d60f39a3ea5ce971d077 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2012-10-24T18:12:32Z (GMT) No. of bitstreams: 1 file_0_Tese.pdf: 550931 bytes, checksum: 38cf0263f5b9d60f39a3ea5ce971d077 (MD5) / Made available in DSpace on 2012-10-30T10:37:14Z (GMT). No. of bitstreams: 1 file_0_Tese.pdf: 550931 bytes, checksum: 38cf0263f5b9d60f39a3ea5ce971d077 (MD5) Previous issue date: 2012-03-01 / In this paper, we analyze the impact of hosting the Summer Olympics on macroeconomic aggregates such as GDP, consumption, government consumption and investments per capita. The data is in panel structure and includes the period of ten years before and ten years after the event containing the Olympic Summer Games between 1960 and 1996. The sample countries comprise only candidates to host the games. This sampling strategy allows us to estimate the average treatment effect consistently, because it is assumed that these countries are comparable to each other, including those that ultimately hosted the games. The impact of hosting the Olympic games is measured by Fixed Effect and First Difference regressions. Moreover, we do a structural break test developed by Andrews (1993) to identify if hosting the Olympic Games creates anticipation effects for demand changes that stimulate current GDP, consumption, government consumption and investments. The results indicate a positive effect of the Summer Olympics in all variables of interest. However, the distribution in time and anticipation of these effects is unclear in the tests, changing significantly depending on the model and the significance level used. / Neste artigo e analisado o impacto de sediar as Olimp adas de Ver~ao em agregados macroecon^omicos como PIB, consumo, gastos do governo e investimentos per capitas. Os dados utilizados est~ao em painel e cont^em o per odo entre dez anos antes e dez anos depois do evento contendo os Jogos Ol mpicos de Ver~ao entre 1960 e 1996. Os pa ses da amostra s~ao apenas os candidatos a sediar os jogos, essa estrat egia amostral permite estimar os efeitos m edios de tratamento de forma consistente, pois assume-se que esses pa ses s~ao compar aveis entre si. Para avaliar o impacto dos jogos e lan cado m~ao de t ecnicas em painel como o Efeito Fixo e a Primeira Diferen ca e, al em disso, faz-se um teste de quebra estrutural desenvolvido por Andrews (1993) entre os pa ses sede. Os resultados indicam um efeito positivo e robusto dos Jogos Ol mpicos de Ver~ao em todas as vari aveis de interesse. No entanto, a distribui c~ao no tempo e antecipa c~ao desses efeitos e amb gua nos testes mudando de forma signi cativa dependendo do modelo e n vel de signi c^ancia utilizados.

Doctor of Philosophy / Department of Statistics / Dallas E. Johnson / Over the years sampling and experimental design have developed independently with little mutual compatibility. However, many studies do (or should) involve both a sampling design and an experimental design. For example, a polluted site may be exhaustively partitioned into area plots, a random sample of plots selected, and the selected plots randomly assigned to three clean-up regimens. In this research the relationship between sampling design and experimental design is discussed and a basic review of each is given. An estimator that combines sampling and experimental design is presented and it's development explained. Properties of this estimator will be derived and some applications of the estimator will be examined. Finally, a simulation study comparing this estimator with the traditional estimator will be presented.

<div>Dynamic treatment regimes (DTRs) are often considered for the medical care of chronic diseases and complex conditions. They consist of multistage treatment decisions, each based on the individual's health information and their treatment and response history. In this dissertation, we consider this setting with binary responses (i.e., either respond favorably or unfavorably to a treatment) and highlight one type of heterogeneity, specifically the existence of subgroups of patients who respond favorably to only a distinct subset of study treatments. </div><div>Currently, most works employ model-free approaches to find the optimal DTR. In contrast, we propose a model-based approach, which focuses more on describing heterogeneity in treatment responses. We first consider the scenario when baseline covariates are not included. A mixture of mixed logit models is proposed along with an EM alogorithm to estimate these subgroup proportions and the probabilities of a favorable response. We describe how an optimal dynamic treatment regime can be determined given the model information. We also discuss the necessary identifiability conditions (i.e., what sets of parameters are necessary for DTR determination). </div><div><div>Then, we extend the proposed model to incorporate baseline covariates. Specifically, we include certain baseline covariates in the logistic model for the probabilities of a favorable response and develop a multivariate Bernoulli model to incorporate the remaining covariates in the determination of subgroup proportions. Furthermore, time effects are considered in the model to allow for a potential overall decline in response effectiveness over time. </div><div>In each setting, simulation studies are performed to demonstrate the effectiveness of the proposed method in both parameter and DTR estimation. We also compare our approach with another competing method, Q-learning, and provide the scenarios when our mixture model outperforms Q-learning in terms of finding the optimal DTR.</div></div>

Abstract Purpose Aim of this exploratory study was to investigate whether a retrospective assessment of oral health-related quality of life (OHRQoL) using the Oral Health Impact Profile (OHIP) is susceptible to bias such as implicit theory of change and cognitive dissonance. Methods In this prospective clinical study, a sample of 126 adult patients (age 17–83 years, 49% women) requiring prosthodontic treatment was consecutively recruited. The OHRQoL was assessed using the 49-item OHIP at baseline and at follow-up. Additionally, patients were asked at followup to retrospectively rate their oral health status at baseline (retrospective pretest or then-test) and the change in oral health status using a global transition question. Furthermore, patients’ ratings of overall oral health and general health were used as validity criteria for the OHRQoL assessments. Response shift was calculated as the difference between the initial and retrospective baseline assessments. Results Baseline and retrospective pretest did not differ substantially in terms of internal consistency and convergent validity. Response shift was more pronounced when patients perceived a large change in OHRQoL during treatment. Retrospective pretests were more highly correlated with the baseline than with the follow-up assessment. Conclusion Findings suggest that retrospective assessments of OHRQoL using the OHIP-49 are susceptible to bias. Cognitive dissonance is more likely to appear as a source of bias than implicit theory of change.:Inhaltsverzeichnis 1. Einführung in die Thematik…………………………………………………2 2. Formatierte Publikation……………………………………………………..11 3. Zusammenfassung…………………………………………………………. 19 4. Literaturverzeichnis……………………………………………………….... 24 5. Anlagen 5.1. Darstellung des eigenen Beitrags……………………………………. 27 5.2. Selbstständigkeitserklärung…………………………………………... 28 5.3. Lebenslauf……………………………………………………………… 29 5.4. Publikationen…………………………………………………………… 30 5.5. Danksagung……………………………………………………………. 31

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第20870号 / 経博第565号 / 新制||経||283(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慶彦, 准教授 山田 憲, 准教授 高野 久紀 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

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