本研究旨在探討猝睡症患者生活品質受症狀變化及神經認知功能改變的影響情況，以及不同層次影響因素對患者生活品質的初始狀態及後續變化軌跡的影響效果。 本研究於北部一所醫學中心睡眠障礙科及兒童心智科募集確診為猝睡症之患者，經同意後進行為期五年的長期研究，募集總人數168人，完成五年資料收集人數85人。本研究使用睡眠多項檢驗（polysomnography, PSG）、多段入睡測試（multiple sleep latency test, MSLT）、人類白血球抗原檢驗（human leukocyte antigen, HLA）為基本檢驗工具，以電腦化第二版康氏持續注意力測驗（Continuous Performance Test- II）及威斯康辛卡片分類測驗（Wisconsin Card Sorting Test, WCST）為檢測神經認知功能之工具，以自填艾普渥斯嗜睡程度量表（Epworth sleepiness scale, ESS）、史丹佛睡眠問卷（Stanford sleep inventory, SSI）及簡式生活品質量表（short from-36 items of health related quality of life, SF-36）做為症狀嚴重度及生活品質的依據。資料分析以描述統計及階層線性模式（hierarchical linear models , HLM）統計方法進行。主要結果如下： 一、 猝睡症患者生活品質分為生理與心理兩個層面，患者生理層面在五年期間維持相對穩定沒有顯著變化；心理層面中之不同向度則有不同變化趨勢，心理健康與活力向度隨時間有逐漸提高的趨勢，患者此二向度生活品質接受治療後有穩定上升的趨勢，而社會功能及情緒角色限制則呈現二次方曲線變化，以及呈現先增後減的發展軌跡，患者此二向度接受治療後顯著上升，第三年後有逐年下降的趨勢。 二、 患者嗜睡程度及猝倒嚴重度變化隨時間有顯著成長軌跡，呈二次方曲線發展，轉折點在第三年，接受治療前三年症狀呈現穩定降低的軌跡，但自第三年起逐年增加，此結果與藥物治療初期症狀獲得顯著改善，後期改善幅度相對減少，及藥物效果具有關聯。 三、 個體間層次變項僅疾病持續時間、HLA對患者生活品質具顯著解釋力，其中疾病持續時間越長，患者可能發展因應症狀之策略，從而降低疾病對生活品質之衝擊。而HLA則對症狀有不同影響，HLA陽性患者初始嗜睡程度較陰性者為低，且接受治療後改善效果較陰性者顯著，猝倒嚴重度起始值較陰性者高，且接受治療後的趕善幅度較陰性者小。 四、 疾病嚴重度變化對生活品質具顯著影響，完整模式分析中，時間主效應未達顯著，但可由症狀變化及神經認知功能改變進行更佳的解釋。嗜睡程度變化僅對身體疼痛向度變化不具有解釋力外，對其餘七個向度均具顯著影響；猝倒影響層面不及嗜睡程度，但亦可解釋生理量表、生理角色限制、心理量表、心理健康、情緒角色限制、活力等向度上的變化。 五、 神經認知功能改變與否對患者生活品質具有加成效果，分析顯示患者神經認知功能改善時，其生活品質提升速率較未改善者高，影響較顯著的包括注意力、警覺度及概念反應，此結果與下視丘泌素參與的維持注意力及前額葉功能有關。 本研究依據分析結果提出猝睡症患者生活品質受嗜睡症狀及猝倒嚴重度改變直接影響，同時受人類白血球抗原屬性及神經認知功能改變調節之假設模型，作為未來研究參考依據。並根據研究結果與限制，提出對臨床實務的應用與心理介入的建議，並對未來提升猝睡症患者生活品質相關研究提供建議。 / The current study aims to: (1) examine the change of eight domains of quality of life in narcoleptics within five years, (2) investigate the impact of the change of symptom severity on different dimension of quality of life, as well as the influence associated with the change of neuro-cognitive function. There were 168 participants recruited from a medical center in northern Taiwan. 85 of them completed the 5-year annual follow-up data collection. During the follow-ups, polysomnography (PSG), multiple sleep latency test (MSLT) and human leukocyte antigen (HLA) test were conducted. Computerized neuropsychological tests of Conners’ Continuous Performance Test- II (CPT-II) and Wisconsin Card Sorting Test (WCST) were also administered to obtain attention and executive function data. The short from-36 items of health related quality of life (SF-36), Stanford sleep inventory (SSI) and Epworth sleepiness scale were applied to assess quality of life and symptom severity. Descriptive statistics and hierarchical linear models were applied for data analysis. The main results were: 1. The quality of life was divided into physical and psychological domains. The physical domain kept relatively stable during the 5-year follow up as opposed to the psychological domain. In psychological domain, the vitality and psychological health showed increasing tendency overtime. However, the social function and role functioning-emotion increased during the first 3 years then declined afterward. 2. The symptom severity also showed a tendency corresponded to quadratic curve. The daytime sleepiness together with cataplexy severity reduced immediately after treatment but rose after the third year. 3. The variables of individual characteristics that showed significant impact on quality of life were disease duration and HLA type. The longer the duration, the better quality of life one had. Positive HLA typing seemed to be a protective factor on severity of sleepiness. It also predicted better treatment outcomes, but worsen the severity of cataplexy and treatment effects. 4. The symptom severity could be a good explanation as a variable of quality of life. The daytime sleepiness altered all domain of SF-36 expect body pain. Cataplexy affected only psychological domain of SF-36. 5. The neuro-cognitive function was also found to affect quality of life. Those who improved in attention and executive function test got greater improvement on SF-36 as well. The vigilance on CPT-II and conceptualized response on WCST had most significant impact. I proposed a model of change of quality of life in patients with narcolepsy based on the results obtained. Several suggestions were also proposed for clinical and psychological intervention for narcolepsy to improve their quality of life.

No description available.

No description available.

本論文從教育社會學角度探討造成數學成就性別差異現象的成因，以班級的性別環境為研究脈絡，研究影響國中女生數學成就的可能原因。假設數學成就的性別差異是受到後天學習歷程影響，班級環境中隱含的性別刻板印象為其中一個重要社會文化影響因素。透過比較國一到國三階段女生班和一般男女合班女生數學成就的異同，嘗試找出造成數學成就性別差異現象的成因，是否與班級性別環境、師生的性別刻板印象等因素有關。利用階層線性模式（Hierarchical Linear Models，HLM）統計方法，分析資料取自由中央研究院、教育部和國科會共同規劃的全國性長期的調查計畫：「台灣教育長期追蹤資料庫」（Taiwan Education Panel Survey，簡稱TEPS）。研究結果發現女生班、數學老師性別及班級學業氣氛等因素對於國中女生數學成就有顯著影響力，但進一步考慮學校公私別變項之後，女生班的影響力即消失。 / The main purpose of this study is to assess the magnitude of individual and contextual influences to explain gender differences in math achievements. Adopting the hierarchical linear model analysis to determine whether or not statistically significant differences between the mathematical achievements of 7th grade students who attend all-girls classes compared with those who attend coeducational classes at the same time, and their academic performance after two years. The result shows that there are three factors that have significant influences on girls’ math achievement in junior high schools, which are the single-sex classes, female math teachers and the academic climate of each class. However, if private schools are taken into consideration, the significant influence of the gender composition of classes will disappear.

多年來，交通運輸與土地使用之交互影響關係受學界所廣泛討論，本研究主要探討高速鐵路與土地使用之交互影響關係。關於高鐵對土地使用影響之研究，分為兩大類別，分別為建立模式預測未來地區發展狀況，以及實證分析高鐵通車後對地區的影響效果。過去研究指出，高鐵營運後，可能對土地使用產生之影響包括：無顯著之土地使用改變、地區間互動改變、聚集效果（Cluster effect）、離散效果(Disparties)以及「隧道效果(Tunnel effect)」或「廊道效果(Corridor effect) 」。 　　本研究為以階層線性模型分析高鐵通車後對台灣土地使用影響之實證研究。根據實證，高鐵站之有無以及高鐵站所在區位對鄉鎮市區土地使用有顯著影響，且相較其他控制變數，為影響鄉鎮市區土地使用之重要變數。有高鐵站之鄉鎮市區與無高鐵站之鄉鎮市區相比，土地使用可能成長較多，而位於高鐵一定服務範圍內之鄉鎮市區之土地使用，亦受高鐵所影響。另外，不同區位之高鐵站對土地使用之效果有所不同，而該區位效果隨產業特性可能有所差異。人口、及業人口以及三級產業及業人口可能因市中心區位之高鐵站聚集，但二級產業及業人口未有因市中心區位高鐵站而聚集的現象；郊區區位之高鐵站鄉鎮市區或縣市，則有人口、及業人口或三級產業及業人口流失的現象。由上述結果可驗證，高鐵服務範圍內有聚集效果之發生，而不同區位之高鐵站，聚集之效果並不同。 / For many years, the interactive relationship between transportation and land use has been widely discussed by scholars. This study is trying to assess the short-term impact of high-speed rail (HSR) on land use. There are two types of studies on the impact of high-speed rail on land use. One is establishing models to predict future land use development; the other is evaluating the effect of HSR empirically. Past studies have shown that possible impacts on land use after the operation of HSR include: no significant land use change, inter-regional interaction change, cluster effect, disparities, and "tunnel effect" or "corridor effect." 　　In this empirical study, the results of hierarchical linear model show that the existence of the HSR station and the location of the HSR station have a significant effect on the land use in the city. Controlling for other control variables, the existence and location of the HSR station are important factors influencing the land use in the city. Land use development in cities with the HSR station may be more evident than those without the HSR station. Cities within the HSR service area are also effected by HSR. In addition, there may be different land use effects due to different locations of the HSR stations, and these location effects may be different due to different industrial characteristics of the area. Population, employment, and employment of tertiary industrial sectors in a city may cluster due to the HSR station in central area location, but employment of secondary industrial sectors doesn’t. Otherwise, population, employment, and employment of tertiary industrial sectors in a city or county may lose due to the HSR station in rural area location. In conclusion, there is a cluster effect within the HSR service area, and this effect varies according to the location of the HSR station.

Les logiciels utilisés sont Splus et R. / Résumé La technique connue comme l'imputation multiple semble être la technique la plus appropriée pour résoudre le problème de non-réponse. La littérature mentionne des méthodes qui modélisent la nature et la structure des valeurs manquantes. Une des méthodes les plus populaires est l'algorithme « Pan » de (Schafer & Yucel, 2002). Les imputations rapportées par cette méthode sont basées sur un modèle linéaire multivarié à effets mixtes pour la variable réponse. La méthode « BHLC » de (Murua et al, 2005) est une extension de « Pan » dont le modèle est bayésien hiérarchique avec groupes. Le but principal de ce travail est d'étudier le problème de sélection du modèle pour l'imputation multiple en termes d'efficacité et d'exactitude des prédictions des valeurs manquantes. Nous proposons une mesure de performance liée à la prédiction des valeurs manquantes. La mesure est une erreur quadratique moyenne reflétant la variance associée aux imputations multiples et le biais de prédiction. Nous montrons que cette mesure est plus objective que la mesure de variance de Rubin. Notre mesure est calculée en augmentant par une faible proportion le nombre de valeurs manquantes dans les données. La performance du modèle d'imputation est alors évaluée par l'erreur de prédiction associée aux valeurs manquantes. Pour étudier le problème objectivement, nous avons effectué plusieurs simulations. Les données ont été produites selon des modèles explicites différents avec des hypothèses particulières sur la structure des erreurs et la distribution a priori des valeurs manquantes. Notre étude examine si la vraie structure d'erreur des données a un effet sur la performance du choix des différentes hypothèses formulées pour le modèle d'imputation. Nous avons conclu que la réponse est oui. De plus, le choix de la distribution des valeurs manquantes semble être le facteur le plus important pour l'exactitude des prédictions. En général, les choix les plus efficaces pour de bonnes imputations sont une distribution de student avec inégalité des variances dans les groupes pour la structure des erreurs et une loi a priori choisie pour les valeurs manquantes est la loi normale avec moyenne et variance empirique des données observées, ou celle régularisé avec grande variabilité. Finalement, nous avons appliqué nos idées à un cas réel traitant un problème de santé. Mots clés : valeurs manquantes, imputations multiples, modèle linéaire bayésien hiérarchique, modèle à effets mixtes. / Abstract The technique known as multiple imputation seems to be the most suitable technique for solving the problem of non-response. The literature mentions methods that models the nature and structure of missing values. One of the most popular methods is the PAN algorithm of Schafer and Yucel (2002). The imputations yielded by this method are based on a multivariate linear mixed-effects model for the response variable. A Bayesian hierarchical clustered and more flexible extension of PAN is given by the BHLC model of Murua et al. (2005). The main goal of this work is to study the problem of model selection for multiple imputation in terms of efficiency and accuracy of missing-value predictions. We propose a measure of performance linked to the prediction of missing values. The measure is a mean squared error, and hence in addition to the variance associated to the multiple imputations, it includes a measure of bias in the prediction. We show that this measure is more objective than the most common variance measure of Rubin. Our measure is computed by incrementing by a small proportion the number of missing values in the data and supposing that those values are also missing. The performance of the imputation model is then assessed through the prediction error associated to these pseudo missing values. In order to study the problem objectively, we have devised several simulations. Data were generated according to different explicit models that assumed particular error structures. Several missing-value prior distributions as well as error-term distributions are then hypothesized. Our study investigates if the true error structure of the data has an effect on the performance of the different hypothesized choices for the imputation model. We concluded that the answer is yes. Moreover, the choice of missing-value prior distribution seems to be the most important factor for accuracy of predictions. In general, the most effective choices for good imputations are a t-Student distribution with different cluster variances for the error-term, and a missing-value Normal prior with data-driven mean and variance, or a missing-value regularizing Normal prior with large variance (a ridge-regression-like prior). Finally, we have applied our ideas to a real problem dealing with health outcome observations associated to a large number of countries around the world. Keywords: Missing values, multiple imputation, Bayesian hierarchical linear model, mixed effects model.

Os modelos mistos são apropriados na análise de dados longitudinais, agrupados e hierárquicos, permitindo descrever e comparar os perfis médios de respostas, levando em conta a variabilidade e a correlação entre as unidades experimentais de um mesmo grupo e entre os valores observados na mesma unidade experimental ao longo do tempo, assim como a heterogeneidade das variâncias. Esses modelos possibilitam a análise de dados desbalanceados, incompletos ou irregulares com relação ao tempo. Neste trabalho, buscou-se mostrar a flexibilidade dos modelos lineares mistos e a sua importância na análise de dados toxicológicos longitudinais. Os modelos lineares mistos foram utilizados para analisar os efeitos de dose no ganho de peso de ratos adultos machos e fêmeas, em teste de toxicidade por doses repetidas e também os efeitos de fase de gestação e dose nos perfis de pesos de filhotes de ratas tratadas. Foram comparados os modelos lineares mistos de regressão polinomial de grau 3, spline e de regressão por partes, ambos com um único ponto de mudança na idade média de abertura dos olhos dos filhotes, buscando o mais apropriado para descrever o crescimento dos mesmos ao longo do período de amamentação. São apresentados os códigos escritos no SAS/STAT para a análise exploratória dos dados, ajuste, comparação e validação dos modelos. Espera-se que o detalhamento da teoria e das aplicações apresentado contribua para a compreensão, interesse e uso desta metodologia por estatísticos e pesquisadores da área. / Mixed models are appropriate in the analysis of longitudinal, grouped and hierarchical data, allowing describe and compare the average response profiles, taking into account the variability and correlation among the experimental units of the same group and among the values observed over the time in the same experimental unit, as well as the heterogeneity of variances. These models allow the analysis of unbalanced, incomplete or irregular data with respect to time. This work aimed to show the flexibility of linear mixed models and its importance in the analysis of longitudinal toxicological data. Linear mixed models were used to evaluate the effects of doses in the body weight gain of adult male and female Wistar rats, in repeated doses toxicity test and also the effects of pregnancy period and dose in the pups growth of treated dams. It were compared the linear mixed models of third degree polynomial regression, spline and piecewise regression, both with a single point of change in the average time of pups eyes opening, searching for the most appropriate one to describe their growth along the lactation period. The SAS/STAT codes used for exploratory data analysis, comparison and validation of fitted models are presented. It is expected that the detailing of the theory and of the applications presented contribute with the understanding, interest and use of this methodology by statisticians and researchers in the area.

Résumé La technique connue comme l'imputation multiple semble être la technique la plus appropriée pour résoudre le problème de non-réponse. La littérature mentionne des méthodes qui modélisent la nature et la structure des valeurs manquantes. Une des méthodes les plus populaires est l'algorithme « Pan » de (Schafer & Yucel, 2002). Les imputations rapportées par cette méthode sont basées sur un modèle linéaire multivarié à effets mixtes pour la variable réponse. La méthode « BHLC » de (Murua et al, 2005) est une extension de « Pan » dont le modèle est bayésien hiérarchique avec groupes. Le but principal de ce travail est d'étudier le problème de sélection du modèle pour l'imputation multiple en termes d'efficacité et d'exactitude des prédictions des valeurs manquantes. Nous proposons une mesure de performance liée à la prédiction des valeurs manquantes. La mesure est une erreur quadratique moyenne reflétant la variance associée aux imputations multiples et le biais de prédiction. Nous montrons que cette mesure est plus objective que la mesure de variance de Rubin. Notre mesure est calculée en augmentant par une faible proportion le nombre de valeurs manquantes dans les données. La performance du modèle d'imputation est alors évaluée par l'erreur de prédiction associée aux valeurs manquantes. Pour étudier le problème objectivement, nous avons effectué plusieurs simulations. Les données ont été produites selon des modèles explicites différents avec des hypothèses particulières sur la structure des erreurs et la distribution a priori des valeurs manquantes. Notre étude examine si la vraie structure d'erreur des données a un effet sur la performance du choix des différentes hypothèses formulées pour le modèle d'imputation. Nous avons conclu que la réponse est oui. De plus, le choix de la distribution des valeurs manquantes semble être le facteur le plus important pour l'exactitude des prédictions. En général, les choix les plus efficaces pour de bonnes imputations sont une distribution de student avec inégalité des variances dans les groupes pour la structure des erreurs et une loi a priori choisie pour les valeurs manquantes est la loi normale avec moyenne et variance empirique des données observées, ou celle régularisé avec grande variabilité. Finalement, nous avons appliqué nos idées à un cas réel traitant un problème de santé. Mots clés : valeurs manquantes, imputations multiples, modèle linéaire bayésien hiérarchique, modèle à effets mixtes. / Abstract The technique known as multiple imputation seems to be the most suitable technique for solving the problem of non-response. The literature mentions methods that models the nature and structure of missing values. One of the most popular methods is the PAN algorithm of Schafer and Yucel (2002). The imputations yielded by this method are based on a multivariate linear mixed-effects model for the response variable. A Bayesian hierarchical clustered and more flexible extension of PAN is given by the BHLC model of Murua et al. (2005). The main goal of this work is to study the problem of model selection for multiple imputation in terms of efficiency and accuracy of missing-value predictions. We propose a measure of performance linked to the prediction of missing values. The measure is a mean squared error, and hence in addition to the variance associated to the multiple imputations, it includes a measure of bias in the prediction. We show that this measure is more objective than the most common variance measure of Rubin. Our measure is computed by incrementing by a small proportion the number of missing values in the data and supposing that those values are also missing. The performance of the imputation model is then assessed through the prediction error associated to these pseudo missing values. In order to study the problem objectively, we have devised several simulations. Data were generated according to different explicit models that assumed particular error structures. Several missing-value prior distributions as well as error-term distributions are then hypothesized. Our study investigates if the true error structure of the data has an effect on the performance of the different hypothesized choices for the imputation model. We concluded that the answer is yes. Moreover, the choice of missing-value prior distribution seems to be the most important factor for accuracy of predictions. In general, the most effective choices for good imputations are a t-Student distribution with different cluster variances for the error-term, and a missing-value Normal prior with data-driven mean and variance, or a missing-value regularizing Normal prior with large variance (a ridge-regression-like prior). Finally, we have applied our ideas to a real problem dealing with health outcome observations associated to a large number of countries around the world. Keywords: Missing values, multiple imputation, Bayesian hierarchical linear model, mixed effects model. / Les logiciels utilisés sont Splus et R.

Os modelos mistos são apropriados na análise de dados longitudinais, agrupados e hierárquicos, permitindo descrever e comparar os perfis médios de respostas, levando em conta a variabilidade e a correlação entre as unidades experimentais de um mesmo grupo e entre os valores observados na mesma unidade experimental ao longo do tempo, assim como a heterogeneidade das variâncias. Esses modelos possibilitam a análise de dados desbalanceados, incompletos ou irregulares com relação ao tempo. Neste trabalho, buscou-se mostrar a flexibilidade dos modelos lineares mistos e a sua importância na análise de dados toxicológicos longitudinais. Os modelos lineares mistos foram utilizados para analisar os efeitos de dose no ganho de peso de ratos adultos machos e fêmeas, em teste de toxicidade por doses repetidas e também os efeitos de fase de gestação e dose nos perfis de pesos de filhotes de ratas tratadas. Foram comparados os modelos lineares mistos de regressão polinomial de grau 3, spline e de regressão por partes, ambos com um único ponto de mudança na idade média de abertura dos olhos dos filhotes, buscando o mais apropriado para descrever o crescimento dos mesmos ao longo do período de amamentação. São apresentados os códigos escritos no SAS/STAT para a análise exploratória dos dados, ajuste, comparação e validação dos modelos. Espera-se que o detalhamento da teoria e das aplicações apresentado contribua para a compreensão, interesse e uso desta metodologia por estatísticos e pesquisadores da área. / Mixed models are appropriate in the analysis of longitudinal, grouped and hierarchical data, allowing describe and compare the average response profiles, taking into account the variability and correlation among the experimental units of the same group and among the values observed over the time in the same experimental unit, as well as the heterogeneity of variances. These models allow the analysis of unbalanced, incomplete or irregular data with respect to time. This work aimed to show the flexibility of linear mixed models and its importance in the analysis of longitudinal toxicological data. Linear mixed models were used to evaluate the effects of doses in the body weight gain of adult male and female Wistar rats, in repeated doses toxicity test and also the effects of pregnancy period and dose in the pups growth of treated dams. It were compared the linear mixed models of third degree polynomial regression, spline and piecewise regression, both with a single point of change in the average time of pups eyes opening, searching for the most appropriate one to describe their growth along the lactation period. The SAS/STAT codes used for exploratory data analysis, comparison and validation of fitted models are presented. It is expected that the detailing of the theory and of the applications presented contribute with the understanding, interest and use of this methodology by statisticians and researchers in the area.

No description available.