Made available in DSpace on 2016-06-02T20:06:09Z (GMT). No. of bitstreams: 1 6004.pdf: 835734 bytes, checksum: b9cae4e00b44525ff06f6dfea7cfe687 (MD5) Previous issue date: 2014-03-28 / Universidade Federal de Sao Carlos / Correlated data sets with nonlinear structure are common in many areas such as biostatistics, pharmacokinetics and longitudinal studies. Nonlinear mixed-effects models are useful tools to analyse those type of problems. In this dissertation, a generalization to this models is proposed, namely by semiparametric partially nonlinear mixed-effects model (MMSPNL), with a nonparametric function to model the mean of the response variable. It assumes that the mean of the interest variable is explained by a nonlinear function, which depends on fixed effects parameters and explanatory variables, and by a nonparametric function. Such nonparametic function is quite flexible, allowing a better adequacy to the functional form that underlies the data. The random effects are included linearly to the model, which simplify the expression of the response variable distribution and enables the model to take into account the within-group correlation structure. It is assumed that the random errors and the random effects jointly follow a multivariate normal distribution. Relate to the nonparametric function, it is deal with the P-splines smoothing technique. The methodology to obtain the parameters estimates is penalized maximum likelihood method. The random effects may be obtained by using the Empirical Bayes method. The goodness of the model and identification of potencial influent observation is verified with the local influence method and a residual analysis. The pharmacokinetic data set, in which the anti-asthmatic drug theophylline was administered to 12 subjects and serum concentrations were taken at 11 time points over the 25 hours (after being administered), was re-analysed with the proposed model, exemplifying its uses and properties. / Dados correlacionados com estrutura não linear são comuns em bioestatística, estudos farmacocinéticos e longitudinais. Modelos mistos não lineares são ferramentas úteis para se analisar esses tipos de problemas. Nesta dissertação, propõe-se uma generalização desses modelos, chamada de modelo misto semiparamétrico parcialmente não linear (MMSPNL), com uma função não paramétrica para se modelar a média da variável resposta. Assume-se que a média da variável de interesse é explicada por uma função não linear, que depende de parâmetros de efeitos fixos e variáveis explicativas, e por uma função não paramétrica. Tal função não paramétrica possui grande flexibilidade, permitindo uma melhor adequação à forma funcional que subjaz aos dados. Os efeitos aleatórios são incluídos linearmente ao modelo, o que simplifica a expressão da distribuição da variável resposta e permite considerar a estrutura de correlação intra grupo. É assumido que os erros aleatórios e efeitos aleatórios conjuntamente seguem uma distribuição normal multivariada. Em relação a função não paramétrica, utiliza-se a técnica de suavização com P-splines. A metodologia para se obterem as estimativas dos parâmetros é o método de máxima verossimilhança penalizada. Os efeitos aleatórios podem ser obtidos usando-se o método de Bayes empírico. A qualidade do modelo e a identificação de observações aberrantes é verificada pelo método de influência local e por análise de resíduos. O conjunto de dados farmacocinéticos, em que o antiasmático theophylline foi administrado a 12 sujeitos e concentrações séricas foram tomadas em 11 instantes de tempo durante as 25 horas (após ser administrado), foi reanalisado com o modelo proposto, exemplificando seu uso e propriedades.

The main purpose of panel studies is to analyze changes in values of studied variables over time. In micro panel research, a large number of elements are periodically observed within the relatively short time period of just a few years. Moreover, the number of repeated measurements is small. This dissertation deals with contemporary approaches to the regression and the clustering analysis of micro panel data. One of the approaches to the micro panel analysis is to use multivariate statistical models originally designed for crosssectional data and modify them in order to take into account the within-subject correlation. The thesis summarizes available tools for the regression analysis of micro panel data. The known and currently used linear mixed effects models for a normally distributed dependent variable are recapitulated. Besides that, new approaches for analysis of a response variable with other than normal distribution are presented. These approaches include the generalized marginal linear model, the generalized linear mixed effects model and the Bayesian modelling approach. In addition to describing the aforementioned models, the paper also includes a brief overview of their implementation in the R software. The difficulty with the regression models adjusted for micro panel data is the ambiguity of their parameters estimation. This thesis proposes a way to improve the estimations through the cluster analysis. For this reason, the thesis also contains a description of methods of the cluster analysis of micro panel data. Because supply of the methods is limited, the main goal of this paper is to devise its own two-step approach for clustering micro panel data. In the first step, the panel data are transformed into a static form using a set of proposed characteristics of dynamics. These characteristics represent different features of time course of the observed variables. In the second step, the elements are clustered by conventional spatial clustering techniques (agglomerative clustering and the C-means partitioning). The clustering is based on a dissimilarity matrix of the values of clustering variables calculated in the first step. Another goal of this paper is to find out whether the suggested procedure leads to an improvement in quality of the regression models for this type of data. By means of a simulation study, the procedure drafted herein is compared to the procedure applied in the kml package of the R software, as well as to the clustering characteristics proposed by Urso (2004). The simulation study demonstrated better results of the proposed combination of clustering variables as compared to the other combinations currently used. A corresponding script written in the R-language represents another benefit of this paper. It is available on the attached CD and it can be used for analyses of readers own micro panel data.

Par delà les études transversales, étudier l'évolution temporelle de phénomènes connait un intérêt croissant. En effet, pour comprendre un phénomène, il semble plus adapté de comparer l'évolution des marqueurs de celui-ci au cours du temps plutôt que ceux-ci à un stade donné. Le suivi de maladies neuro-dégénératives s'effectue par exemple par le suivi de scores cognitifs au cours du temps. C'est également le cas pour le suivi de chimiothérapie : plus que par l'aspect ou le volume des tumeurs, les oncologues jugent que le traitement engagé est efficace dès lors qu'il induit une diminution du volume tumoral.L'étude de données longitudinales n'est pas cantonnée aux applications médicales et s'avère fructueuse dans des cadres d'applications variés tels que la vision par ordinateur, la détection automatique d'émotions sur un visage, les sciences sociales, etc.Les modèles à effets mixtes ont prouvé leur efficacité dans l'étude des données longitudinales, notamment dans le cadre d'applications médicales. Des travaux récent (Schiratti et al., 2015, 2017) ont permis l'étude de données complexes, telles que des données anatomiques. L'idée sous-jacente est de modéliser la progression temporelle d'un phénomène par des trajectoires continues dans un espace de mesures, que l'on suppose être une variété riemannienne. Sont alors estimées conjointement une trajectoire moyenne représentative de l'évolution globale de la population, à l'échelle macroscopique, et la variabilité inter-individuelle. Cependant, ces travaux supposent une progression unidirectionnelle et échouent à décrire des situations telles que la sclérose en plaques ou le suivi de chimiothérapie. En effet, pour ces pathologies, vont se succéder des phases de progression, de stabilisation et de remision de la maladie, induisant un changement de la dynamique d'évolution globale.Le but de cette thèse est de développer des outils méthodologiques et algorithmiques pour l’analyse de données longitudinales, dans le cas de phénomènes dont la dynamique d'évolution est multiple et d'appliquer ces nouveaux outils pour le suivi de chimiothérapie. Nous proposons un modèle non-linéaire à effets mixtes dans lequel les trajectoires d'évolution individuelles sont vues comme des déformations spatio-temporelles d'une trajectoire géodésique par morceaux et représentative de l'évolution de la population. Nous présentons ce modèle sous des hypothèses très génériques afin d'englober une grande classe de modèles plus spécifiques.L'estimation des paramètres du modèle géométrique est réalisée par un estimateur du maximum a posteriori dont nous démontrons l'existence et la consistance sous des hypothèses standards. Numériquement, du fait de la non-linéarité de notre modèle, l'estimation est réalisée par une approximation stochastique de l'algorithme EM, couplée à une méthode de Monte-Carlo par chaînes de Markov (MCMC-SAEM). La convergence du SAEM vers les maxima locaux de la vraisemblance observée ainsi que son efficacité numérique ont été démontrées. En dépit de cette performance, l'algorithme SAEM est très sensible à ses conditions initiales. Afin de palier ce problème, nous proposons une nouvelle classe d'algorithmes SAEM dont nous démontrons la convergence vers des minima locaux. Cette classe repose sur la simulation par une loi approchée de la vraie loi conditionnelle dans l'étape de simulation. Enfin, en se basant sur des techniques de recuit simulé, nous proposons une version tempérée de l'algorithme SAEM afin de favoriser sa convergence vers des minima globaux. / Beyond transversal studies, temporal evolution of phenomena is a field of growing interest. For the purpose of understanding a phenomenon, it appears more suitable to compare the evolution of its markers over time than to do so at a given stage. The follow-up of neurodegenerative disorders is carried out via the monitoring of cognitive scores over time. The same applies for chemotherapy monitoring: rather than tumors aspect or size, oncologists asses that a given treatment is efficient from the moment it results in a decrease of tumor volume. The study of longitudinal data is not restricted to medical applications and proves successful in various fields of application such as computer vision, automatic detection of facial emotions, social sciences, etc.Mixed effects models have proved their efficiency in the study of longitudinal data sets, especially for medical purposes. Recent works (Schiratti et al., 2015, 2017) allowed the study of complex data, such as anatomical data. The underlying idea is to model the temporal progression of a given phenomenon by continuous trajectories in a space of measurements, which is assumed to be a Riemannian manifold. Then, both a group-representative trajectory and inter-individual variability are estimated. However, these works assume an unidirectional dynamic and fail to encompass situations like multiple sclerosis or chemotherapy monitoring. Indeed, such diseases follow a chronic course, with phases of worsening, stabilization and improvement, inducing changes in the global dynamic.The thesis is devoted to the development of methodological tools and algorithms suited for the analysis of longitudinal data arising from phenomena that undergo multiple dynamics and to apply them to chemotherapy monitoring. We propose a nonlinear mixed effects model which allows to estimate a representative piecewise-geodesic trajectory of the global progression and together with spacial and temporal inter-individual variability. Particular attention is paid to estimation of the correlation between the different phases of the evolution. This model provides a generic and coherent framework for studying longitudinal manifold-valued data.Estimation is formulated as a well-defined maximum a posteriori problem which we prove to be consistent under mild assumptions. Numerically, due to the non-linearity of the proposed model, the estimation of the parameters is performed through a stochastic version of the EM algorithm, namely the Markov chain Monte-Carlo stochastic approximation EM (MCMC-SAEM). The convergence of the SAEM algorithm toward local maxima of the observed likelihood has been proved and its numerical efficiency has been demonstrated. However, despite appealing features, the limit position of this algorithm can strongly depend on its starting position. To cope with this issue, we propose a new version of the SAEM in which we do not sample from the exact distribution in the expectation phase of the procedure. We first prove the convergence of this algorithm toward local maxima of the observed likelihood. Then, with the thought of the simulated annealing, we propose an instantiation of this general procedure to favor convergence toward global maxima: the tempering-SAEM.

La résolution des pronoms est le processus par lequel un pronom anaphorique est mis en relation avec son antécédent. Les humains en sont capables sans efforts notables en situation normale. En revanche, les systèmes automatiques ont une performance qui reste loin derrière, malgré des algorithmes de plus en plus sophistiqués, développés par la communauté du Traitement Automatique des Langues. La recherche en psycholinguistique a montré à travers des expériences qu'au cours de la résolution de nombreux facteurs sont pris en compte par les locuteurs. Une question importante se pose : comment les facteurs interagissent et quel poids faut-il attribuer à chacun d'entre eux ? Une deuxième question qui se pose alors est comment les théories linguistiques de la résolution des pronoms incorporent tous les facteurs. Nous proposons une nouvelle approche à ces problématiques : la simulation computationnelle de la charge cognitive de la résolution des pronoms. La motivation pour notre approche est double : d'une part, l'implémentation d'hypothèses par un système computationnel permet de mieux spécifier les théories, d’autre part, les systèmes automatiques peuvent faire des prédictions sur des données naturelles comme les corpus de mouvement oculaires. De cette façon, les modèles computationnels représentent une alternative aux expériences classiques avec des items expérimentaux construits manuellement. Nous avons fait plusieurs expériences afin d'explorer les modèles cognitifs computationnels de la résolution des pronoms. D'abord, nous avons simulé la charge cognitive des pronoms en utilisant des poids de facteurs de résolution appris sur corpus. Ensuite, nous avons testé si les concepts de la Théorie de l’Information sont pertinents pour prédire la charge cognitive des pronoms. Finalement, nous avons procédé à l’évaluation d’un modèle psycholinguistique sur des données issues d’un corpus enrichi de mouvements oculaires. Les résultats de nos expériences montrent que la résolution des pronoms est en effet multi-factorielle et que l’influence des facteurs peut être estimée sur corpus. Nos résultats montrent aussi que des concepts de la Théorie de l’Information sont pertinents pour la modélisation des pronoms. Nous concluons que l’évaluation des théories sur des données de corpus peut jouer un rôle important dans le développement de ces théories et ainsi amener dans le futur à une meilleure prise en compte du contexte discursif. / Pronoun resolution is the process in which an anaphoric pronoun is linked to its antecedent. In a normal situation, humans do not experience much cognitive effort due to this process. However, automatic systems perform far from human accuracy, despite the efforts made by the Natural Language Processing community. Experimental research in the field of psycholinguistics has shown that during pronoun resolution many linguistic factors are taken into account by speakers. An important question is thus how much influence each of these factors has and how the factors interact with each-other. A second question is how linguistic theories about pronoun resolution can incorporate all relevant factors. In this thesis, we propose a new approach to answer these questions: computational simulation of the cognitive load of pronoun resolution. The motivation for this approach is two-fold. On the one hand, implementing hypotheses about pronoun resolution in a computational system leads to a more precise formulation of theories. On the other hand, robust computational systems can be run on uncontrolled data such as eye movement corpora and thus provide an alternative to hand-constructed experimental material. In this thesis, we conducted various experiments. First, we simulated the cognitive load of pronouns by learning the magnitude of impact of various factors on corpus data. Second, we tested whether concepts from Information Theory were relevant to predict the cognitive load of pronoun resolution. Finally, we evaluated a theoretical model of pronoun resolution on a corpus enriched with eye movement data. Our research shows that multiple factors play a role in pronoun resolution and that their influence can be estimated on corpus data. We also demonstrate that the concepts of Information Theory play a role in pronoun resolution. We conclude that the evaluation of hypotheses on corpus data enriched with cognitive data ---- such as eye movement data --- play an important role in the development and evaluation of theories. We expect that corpus based methods will lead to a better modelling of the influence of discourse structure on pronoun resolution in future work.

This thesis employs the globalization and integration-demarcation cleavage theory formulated by Huttar [2014] and Kriesi [2012], conception of populism formulated by Mudde [2017] and draws on Gramscian conception of civil society and hegemony. Aim of this thesis is to build a model of causal influence of globalization on cleavage and on populism, as was suggested by Hutter [2014]. After building this model, the aim is to explore how this theoretical relationship hold's over the 30 years since 90's, when the connection between globalization and new sociocultural cleavage had been theoretically suggested. For this model I am using KOF Globalization Index, European Values Survey datasets and Authoritarian Populism Index constructed and published by Timbro in years 1990, 1999, 2008 and 2017. This model is built on a dataset containing 38 countries on European continent or being a candidate country for EU. I am elaborating Hutter's theoretical suggestion and framing it in Gramscian conception of civil society. This allows me to suggest that populists are using organic crisis in a society to attract people who feel disjointed from current hegemonical elite and to create counterhegemony. The theory is, that globalization increases the tension between winners and losers of globalization sides of cleavage...

La vancomycine est l'un des antibiotiques les plus prescrits, principalement utilisé pour les infections suspectées et confirmées à Staphylococcus aureus résistant à la méthicilline (SARM). Les infections par des souches de SARM font peser une charge importante sur le système de santé, à laquelle s'ajoute l'incertitude qui demeure quant à la posologie optimale de la vancomycine. Les récentes lignes directrices révisées sur le suivi thérapeutique de la vancomycine, publiées en 2020, avalisent principalement l'estimation directe de l'aire sous la courbe de concentration en fonction du temps (AUC) par l'utilisation d'équations bayésiennes ou pharmacocinétiques (PK) de premier ordre pour le suivi thérapeutique. Pour mieux informer la posologie de la vancomycine, nous avons d'abord mis à jour une revue précédente des analyses pharmacocinétiques de population (PopPK) de la vancomycine publiées chez les adultes et les enfants. Pour ce faire, nous avons déterminé les caractéristiques des modèles pharmacocinétiques rapportés et identifié les diverses sources potentielles de variabilité observées dans différentes sous-populations particulières. Motivés par la controverse existante autour des nouvelles directives de surveillance thérapeutique de la vancomycine et par l'absence d'une étude approfondie des méthodes recommandées, nous avons recueilli des données hospitalières et construit un cadre de modélisation qui nous a permis d'évaluer les recommandations des directives sur les méthodes de surveillance, tout en considérant une variété de scénarios et d'hypothèses cliniques réalistes. Notre analyse a confirmé que la surveillance bayésienne est la méthode la plus rapide et la plus fiable, à condition qu'elle soit correctement mise en œuvre, la plus importante condition pour cela étant l'utilisation de modèles bayésiens a priori appropriés. De plus, nous avons montré que le suivi bayésien ne nécessite pas nécessairement des niveaux de concentration de types creux ou pic et peut en fait être réalisé en utilisant un niveau aléatoire. Aussi, nous avons démontré que l'utilisation correcte des équations pharmacocinétiques de premier ordre exigerait au moins deux mesures de concentration à l'état d'équilibre. L’utilisation de la méthode creux-seulement de la vancomycine à l'état d'équilibre peut être tout aussi efficace dans certaines situations que nous avons explorées ici. En considérant la larges étendue et la grande variabilité des populations traitées à la vancomycine en termes d'âge, de gravité de l'infection et de scénarios cliniques, cette thèse adopte un regard objectif pour évaluer quantitativement le gain potentiel de chaque méthode de surveillance de la vancomycine, en explorant leur adéquation en termes d'effort nécessaire, de disponibilité des ressources et de gain potentiel. Compte tenu des lignes directrices sur la vancomycine récemment publiées et de la controverse qui persiste, nous pensons que cette thèse a permis de démêler la variété et la complexité de l'utilisation de la vancomycine et a apporté un éclairage supplémentaire plus objectifvement informé vers un suivi thérapeutique optimal de la vancomycine. / Vancomycin is among the most prescribed antibiotics, mainly used for suspected and confirmed methicillin-resistant Staphylococcus aureus (MRSA) infections. Infections by MRSA strains carry a substantial burden on the health care system, supplemented by the uncertainty that remains regarding vancomycin optimal dosing. The recent revised vancomycin therapeutic monitoring guidelines published in 2020, endorsed primarily the direct estimation of area under the concentration-time curve (AUC) through the use of Bayesian or first-order pharmacokinetic (PK) equations monitoring. To better inform vancomycin dosing, we first updated a previous review of published vancomycin population pharmacokinetic (PopPK) analysis in both adults and children. This was accomplished by determining the characteristics of the reported pharmacokinetic models and identifying the potential various sources of variability observed in different special subpopulations. Motivated by the existing controversy around the new vancomycin therapeutic monitoring guidelines and the lack of a thorough investigation of the recommended methods, we collected hospital data and built a modeling framework that allowed us to assess the guideline recommendations of monitoring methods while considering a variety of realistic clinical scenarios and assumptions. Our analysis affirmed that Bayesian monitoring is the fastest and most reliable method, conditional on its proper implementation, the most important being the use of proper Bayesian priors. Moreover, we showed that Bayesian monitoring does not necessarily require trough or peak concentration levels and can in fact be performed using a random level. Proper use of first-order PK equations required at least two steady-state concentration measurements. Alternatively, simpler trough-only vancomycin monitoring near steady-state can be as effective in certain cases that we explored here. By considering the wide ranges and the high variability in populations treated with vancomycin in terms of age, the severity of infection, and clinical scenarios, this thesis takes an objective look to quantitatively assess the potential gain of each vancomycin drug monitoring method, by investigating their suitability in terms of the effort needed, the availability of resources and the resulting gain. Considering the recently released vancomycin guidelines and the ensuing controversies between well-established clinical teams, we believe that this dissertation helped untangle the variety and complexity of vancomycin use and brought additional insights towards a more objective and optimal vancomycin therapeutic monitoring.

No description available.

Climate change affects lakes, seas and running water globally, but the long-term effects on aquatic ecosystems, including fish communities, are complex and difficult to predict. Previous research has shown that changes in temperature, for example, can lead to shifts in fish species distribution and reductions in body size within fish communities. This study aims to investigate the impact of temperature on size distribution of individuals in fish communities by examining variations in so-called size spectrum and mean body size in ten Swedish lakes over the period 1994-2023. Data were collected from Swedish monitoring programs and analyzed using linear mixed-effects models. The result indicates a significant negative effect of temperature on the size spectrum and mean body size, meaning the number of small individuals increases while the number of large ones decreases. The size spectrum also shows a declining trend over time in several of the lakes, highlighting a shift in the size distribution of fish. These findings support the use of the size spectrum as an indicator of the impacts of climate change on freshwater ecosystems. The results of this study found a clearer link between temperature changes and size distribution compared to biomass, which supports the usefulness of size distribution as an indicator. Understanding these relationships is crucial for informing management and conservation strategies aimed at preserving lake ecosystems and the functions and ecosystem services that fish provide, including food and recreational opportunities. The study further contributes to the existing evidence that climate change is reshaping fish communities and aquatic ecosystems, underscoring the need for adaptive management to mitigate these effects and ensure sustainability of fish stocks for future generations. / Klimatförändringarna påverkar sjöar, hav och rinnande vatten globalt, men långsiktiga effekter på akvatiska ekosystem, inklusive fisksamhällens, är komplexa och svåra att förutse. Tidigare studier har visat att förändringar i temperatur till exempel kan leda till skiften i fiskarters utbredning och minskningar i kroppsstorlek i fisksamhällen. Denna studie syftar till att undersöka temperaturens inverkan på storleksfördelningen av individer i fisksamhällen genom att undersöka variation i det så kallade storleksspektrumets lutning och medelstorlek i tio svenska sjöar under perioden 1994–2023. Data samlades in från svenska övervakningsprogram för sjöar och analyserades med hjälp av linjära mixade effektmodeller. Resultaten indikerar en signifikant negativ effekt av temperatur på storleksspektrumets lutning och medelkroppsstorlek, dvs. antalet små individer ökar relativt antalet stora. Storlekspektrumets lutning visar också en nedåtgående trend över tid i flera av sjöarna, vilket belyser att fiskarnas storleksfördelning har skiftat mot relativt fler små individer i vissa sjöar. Dessa fynd styrker användningen av storleksspektrum som en indikator på klimatförändringarnas effekter på sötvattensekosystem. Resultat från denna studie visar också en tydligare koppling av förändrad temperatur på storleksfördelning än biomassor av fisk och växtplankton, vilket styrker nyttan av storleksfördelning som indikator. Att förstå dessa samband är avgörande för att vägleda förvaltnings- och bevarande strategier som syftar till att bevara sjöekosystem, de funktioner samt ekosystemtjänster som fiskar tillhandahåller, såsom mat och rekreationsmöjligheter. Studien bidrar till redan existerande bevis på att klimatförändringarna omformar fisksamhällen och akvatiska ekosystem, vilket tydliggör behovet av adaptiv förvaltning för att mildra dessa effekter och säkerställa starka fiskbestånd för framtida generationer.

Randomised controlled trials (RCT) are considered the gold standard for assessing the efficacy and safety of medical interventions. However, RCTs face unique challenges when applied to medical technologies, such as issues related to timing of assessment, eligible population, acceptability, blinding, choice of comparator group, and consideration for learning curves. To address these challenges, this thesis explores the adaptation of the model-informed drug development (MIDD) approach to the field of medical technology, using a case study on transurethral microwave thermotherapy (TUMT). The research employs non-linear mixed- effects (NLME) modelling and D-optimal design to optimise study designs and improve the reliability and efficiency of clinical trials. The impact of different sampling times, sample sizes, and learning curves on effect size estimates is analysed. The results show that optimising sampling points and sizes significantly improves the precision and reliability of effect size estimates and describes how MIDD can be a useful tool for this purpose. The study also highlights the limitations of the TUMT study design, suggesting ways in which the model-based approach could offer more robust and reliable clinical evidence generation. This research highlights the potential of the MIDD approach to streamline the medical technology clinical development process, enhance the quality of evidence, and address its inherent complexities. Future work should expand on these findings by exploring more complex error models and additional study designs and its related aspects. / Randomiserade kontrollerade studier (RCT) anses vara standard för att bedöma effekt och säkerhet i kliniska interventionsstudier. RCT:er står dock inför unika utmaningar när de tillämpas på medicinteknik såsom utmaningar relaterade till tidpunkt för bedömning, rekrytering av lämpliga studiedeltagare, acceptans, blindning, val av jämförelsegrupp och hänsyn till inlärningskurvor. För att hantera dessa utmaningar undersöker denna avhandling anpassningen av modellinformerad läkemedelsutveckling (MIDD) till området medicinteknik, med hjälp av en fallstudie om transuretral mikrovågstermoterapi (TUMT). I arbetet tillämpas icke-linjär, hierarkisk (NLME) modellering och D-optimal design för att optimera studiedesigner och förbättra tillförlitligheten i kliniska prövningar. Effekten av olika observationstider, antal studiedeltagare och inlärningskurvor på estimeringen av effektstorlek analyseras. Resultaten visar att optimering av observationstidpunkter och studiestorlek avsevärt förbättrar precisionen och tillförlitligheten av den estimerade effektstorleken och visar på hur MIDD kan vara ett användbart verktyg för detta ändamål inom medicinteknisk utveckling. Studien belyser också begränsningarna i studiedesignen för fallstudien och föreslår hur en modellbaserad metod skulle kunna erbjuda mer robust och tillförlitlig generering av klinisk evidens. Denna forskning belyser potentialen hos MIDD-metoder för att effektivisera den medicintekniska kliniska utvecklingsprocessen, förbättra kvaliteten av evidens, och hantera dess inneboende komplexitet. Framtida arbete bör utvidga dessa resultat genom att utforska mer komplexa modeller, ytterligare studiedesigner, och relaterade aspekter.

長期性資料(longitudinal data)的最主要特徵是為對多個被觀測個體在不同的時間點上重複測量一個或多個反應變數。而在分析長期性資料的方法中，Laird & Ware(1982)建議以線性混合效果模型(linear mixed effects model，LME)來進行估計分析，此模型方法中，資料可以允許遺失值，並可將受測個體間與個體內的變異分開說明。 另在配適最小平方法(OLS)的迴歸模型中，係數估計經常會受到異常值的影響，而Rousseeuw & Leroy(1987)提出最小消去平方法(least trimmed squares，LTS)的穩健迴歸模型，即是解決最小平方法中對於異常值敏感的問題。 本研究主要針對台灣股票預期報酬之三種模型：資本資產定價模型、特徵模型、因子模型分別以OLS、LTS、LME三種估計方法做配適，並比較配適模型之適當與否，樣本資料為民國七十年七月至九十年六月共252個月516家上市公司股票報酬。實證結果顯示，不論是採用OLS、LTS、LME的估計方法，股票報酬解釋變數：系統風險、公司規模、帳面權益對市值比、SMB、HML皆為股票報酬的顯著解釋因子；而在模型比較方面，不論是配適資本資產定價模型、特徵模型或因子模型，LME都較OLS為較適當配適模型。這顯示了在分析長期性資料時，LME的確是一個較佳的統計分析模型。