Renewable energy applications in electric power systems have undergone rapid development and increased use due to global environmental concerns associated with conventional energy sources. Photovoltaics and wind energy sources are considered to be very promising alternatives for power generation because of their tremendous environmental, social and economic benefits, together with public support. </p> <p>Electrical power generation from wind and solar energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of these facilities therefore affect power system reliability in a different manner than those of conventional systems. The research work presented in this thesis is focused on the development of appropriate models and techniques for wind energy conversion and photovoltaic conversion systems to assess the adequacy of composite power systems containing wind or solar energy.</p> <p>This research shows that a five-state wind energy conversion system or photovoltaic conversion system model can be used to provide a reasonable assessment in practical power system adequacy studies using an analytical method or a state sampling simulation approach. The reliability benefits of adding single or multiple wind/solar sites in a composite generation and transmission system are examined in this research. The models, methodologies, results and discussion presented in this thesis provide valuable information for system planners assessing the adequacy of composite electric power systems incorporating wind or solar energy conversion systems.

Renewable energy applications in electric power systems have undergone rapid development and increased use due to global environmental concerns associated with conventional energy sources. Photovoltaics and wind energy sources are considered to be very promising alternatives for power generation because of their tremendous environmental, social and economic benefits, together with public support. </p> <p>Electrical power generation from wind and solar energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of these facilities therefore affect power system reliability in a different manner than those of conventional systems. The research work presented in this thesis is focused on the development of appropriate models and techniques for wind energy conversion and photovoltaic conversion systems to assess the adequacy of composite power systems containing wind or solar energy.</p> <p>This research shows that a five-state wind energy conversion system or photovoltaic conversion system model can be used to provide a reasonable assessment in practical power system adequacy studies using an analytical method or a state sampling simulation approach. The reliability benefits of adding single or multiple wind/solar sites in a composite generation and transmission system are examined in this research. The models, methodologies, results and discussion presented in this thesis provide valuable information for system planners assessing the adequacy of composite electric power systems incorporating wind or solar energy conversion systems.

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Pelo fato da maioria das mortes e morbidades associadas à gravidez ocorrerem em torno do parto, a qualidade do cuidado nesse período é crucial para as mães e seus bebês. Para acompanhar as mulheres nessa etapa, o partograma tem sido a ferramenta mais utilizada nas últimas décadas e, devido à sua simplicidade, é frequentemente usado em países com baixa e média renda. No entanto, sua utilização é altamente questionada devido à ausência de evidências que justifiquem uma contribuição ao parto. Para melhorar a qualidade do parto nessas circunstâncias, o projeto BOLD tem sido desenvolvido com o intuito de reduzir a ocorrência de problemas indesejados e com a finalidade desenvolver uma ferramenta moderna, chamada de SELMA, que projetase como uma alternativa ao partograma. Com a finalidade de associar características fixas e dinâmicas avaliadas no parto e identificar quais elementos intra parto podem ser utilizados como gatilhos para realização de uma intervenção e, assim, prevenir um desfecho indesejado, propomos nesta tese a utilização de modelos de sobrevivência com covariáveis dependentes do tempo. Inicialmente, consideramos a modelagem de dados longitudinais e de sobrevivência utilizando funções de risco paramétricas flexíveis. Nesse caso, propomos a utilização de cinco generalizações da distribuição Weibull, da distribuição Nagakami e utilizamos um procedimento geral de seleção de modelos paramétricos usuais via distribuição Gamma generalizada, inédito na modelagem conjunta. Realizamos um extenso estudo de simulação para avaliar as estimativas de máxima verossimilhança e os critérios de discriminação. Além disso, a própria natureza do parto nos leva a um contexto de eventos múltiplos, nos remetendo à utilização dos modelos multiestados. Eles são definidos como modelos para um processo estocástico que a qualquer momento ocupa um conjunto discreto de estados. De uma forma geral, são os modelos mais comuns para descrever o desenvolvimento de dados de tempo de falha longitudinais e são frequentemente utilizados em aplicações médicas. Considerando o contexto de eventos múltiplos, propomos a inclusão de uma covariável dependente do tempo no modelo multiestados a partir de uma modificação dos dados, o que nos trouxe resultados satisfatórios e similares ao esperado na prática clínica. / As most pregnancy-related deaths and morbidities are clustered around the time of child birth, the quality of care during this period is crucial for mothers and their babies. To monitor the women at this stage, the partograph has been the central tool used in recent decades and, motivated by its simplicity, is frequently used in low-and middle-income countries. However, its use is highly questioned due to lack of evidence to justify a contribution to labor. To improve the quality of labor in these circumstances, the BOLD project has been developed in order to reduce the occurrence of pregnancy-related problems and in order to develop a modern tool, called SELMA, which is projected as an alternative to partograph. Aiming to associate fixed and dynamic characteristics evaluated in the delivery and to identify which elements can be used as triggers for performing an intervention, and thus preventing a bad outcome, this thesis proposes the use of survival models with time dependent covariates. Initially, we consider the joint modeling of survival and longitudinal data using flexible parametric hazard functions. In this sense, we propose the use of five generalizations of Weibull distribution, the Nagakami model and an inedited framework to discriminate usual parametric models via the generalized Gamma distribution, performing an extensive simulation study to evaluate the maximum likelihood estimations and the proposed discrimination criteria. Indeed, by its own nature, the birth leads us to a context of multiple events, referring to the use of multi-state models. These are models for a stochastic process which at any time occupies one of a few possible states. In general, they are the most common models to describe the development of longitudinal failure time data and are often used in medical applications. Considering this context, we proposed the inclusion of a time dependent covariate in the multi-state model using a modified version of the input data, which gave us satisfactory results similar to those expected in clinical practice.

Migrationsverket är en myndighet som prövar ansökningar från personer som vill söka skydd, ha medborgarskap, studera eller vill jobba i Sverige. Då det på senare tid varit en stor ökning i dessa ansökningar har tiden för vilket ett beslut tar ökat. Varje typ av ansökning (exempelvis medborgarskap) är en process som består av flera steg. Hur beslutet går igenom dessa steg kallas för flöde. Migrationsverket vill därför öka sin flödeseffektivitet. När beslutet är klart och personen tagit del av det men inte är nöjd kan denne överklaga. Detta är en av de mest komplexa processerna på Migrationsverket. Syftet är analysera hur lång tid denna process tar och vilka steg i processen som påverkar tiden. Ett steg (som senare visar sig ha en stor effekt på tiden) är yttranden. Det är när domstolen begär information om vad personen som överklagar har att säga om varför denne överklagar. För att analysera detta var två metoder relevanta, accelerated failure time (AFT) och \multi-state models (MSM). Den ena kan predicera tid till händelse (AFT) medan den andra kan analysera effekten av tidspåverkan (MSM) i stegen. Yttranden tidigt i processen har stor betydelse för hur snabbt en överklagan får en dom samtidigt som att antal yttranden ökar tiden enormt. Det finns andra faktorer som påverkar tiden men inte i så stor grad som yttranden. Då yttranden tidigt i processen samtidigt som antal yttranden har betydelse kan flödeseffektiviteten ökas med att ta tid på sig att skriva ett informativt yttrande som gör att domstolen inte behöver begära flera yttranden. / The Swedish Migration Board is an agency that review applications from individuals who wish to seek shelter, have citizenship, study or want to work in Sweden. In recent time there has been a large increase in applications and the time for which a decision is made has increased. Each type of application (such as citizenship) is a process consisting of several stages. How the decision is going through these steps is called flow. The Swedish Migration Board would therefore like to increase their flow efficiency. When the decision is made and the person has take part of it but is not satisfied, he can appeal. This is one of the most complex processes at the Board. The aim is to analyze how long this process will take and what steps in the process affects the time. One step (which was later found to have a significant effect on time) is opinions. This is when the court requests information on what the person is appealing has to say about why he is appealing. To analyze this, two methods were relevant, accelerated failure time (AFT) and the multi-state models (MSM). One can predict time to event (AFT), the other to analyze the effect of time-manipulation (MSM) in the flow. Opinions early in the process is crucial to how quickly an appeal get judgment while the number of opinions increases the time enormously. There are other factors that affect the time but not so much as opinions. The flow efficiency can be increased by taking time to write an informative opinion which allows the court need not to ask for more opinions.

Continuous-time multi-state models are widely used in modeling longitudinal data of disease processes with multiple transient states, yet the analysis is complex when subjects are observed periodically, resulting in interval censored data. Recently, most studies focused on modeling the true disease progression as a discrete time stationary Markov chain, and only a few studies have been carried out regarding non-homogenous multi-state models in the presence of interval-censored data. In this dissertation, several likelihood-based methodologies were proposed to deal with interval censored data in multi-state models. Firstly, a continuous time version of a homogenous Markov multi-state model with backward transitions was proposed to handle uneven follow-up assessments or skipped visits, resulting in the interval censored data. Simulations were used to compare the performance of the proposed model with the traditional discrete time stationary Markov chain under different types of observation schemes. We applied these two methods to the well-known Nun study, a longitudinal study of 672 participants aged ≥ 75 years at baseline and followed longitudinally with up to ten cognitive assessments per participant. Secondly, we constructed a non-homogenous Markov model for this type of panel data. The baseline intensity was assumed to be Weibull distributed to accommodate the non-homogenous property. The proportional hazards method was used to incorporate risk factors into the transition intensities. Simulation studies showed that the Weibull assumption does not affect the accuracy of the parameter estimates for the risk factors. We applied our model to data from the BRAiNS study, a longitudinal cohort of 531 subjects each cognitively intact at baseline. Last, we presented a parametric method of fitting semi-Markov models based on Weibull transition intensities with interval censored cognitive data with death as a competing risk. We relaxed the Markov assumption and took interval censoring into account by integrating out all possible unobserved transitions. The proposed model also allowed for incorporating time-dependent covariates. We provided a goodness-of-fit assessment for the proposed model by the means of prevalence counts. To illustrate the methods, we applied our model to the BRAiNS study.

This thesis develops novel statistical methodology for estimating the incidence and the prevalence of Human Immunodeficiency Virus (HIV) using routinely collected surveillance data. The robust estimation of HIV incidence and prevalence is crucial to correctly evaluate the effectiveness of targeted public health interventions and to accurately predict the HIV- related burden imposed on healthcare services. Bayesian CD4-based multi-state back-calculation methods are a key tool for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their competing contribution to the observed surveillance data. Improving the effectiveness of public health interventions, requires targeting specific age-groups at high risk of infection; however, existing methods are limited in that they do not allow for such subgroups to be identified. Therefore the methodological focus of this thesis lies in developing a rigorous statistical framework for age-dependent back-calculation in order to achieve the joint estimation of age-and-time dependent HIV incidence and diagnosis rates. Key challenges we specifically addressed include ensuring the computational feasibility of proposed methods, an issue that has previously hindered extensions of back-calculation, and achieving the joint modelling of time-and-age specific incidence. The suitability of non-parametric bivariate smoothing methods for modelling the age-and-time specific incidence has been investigated in detail within comprehensive simulation studies. Furthermore, in order to enhance the generalisability of the proposed model, we developed back-calculation that can admit surveillance data less rich in detail; these handle surveillance data collected from an intermediate point of the epidemic, or only available on a coarse scale, and concern both age-dependent and age-independent back-calculation. The applicability of the proposed methods is illustrated using routinely collected surveillance data from England and Wales, for the HIV epidemic among men who have sex with men (MSM).

The typical research of Alzheimer's disease includes a series of cognitive states. Multi-state models are often used to describe the history of disease evolvement. Competing risks models are a sub-category of multi-state models with one starting state and several absorbing states. Analyses for competing risks data in medical papers frequently assume independent risks and evaluate covariate effects on these events by modeling distinct proportional hazards regression models for each event. Jeong and Fine (2007) proposed a parametric proportional sub-distribution hazard (SH) model for cumulative incidence functions (CIF) without assumptions about the dependence among the risks. We modified their model to assure that the sum of the underlying CIFs never exceeds one, by assuming a proportional SH model for dementia only in the Nun study. To accommodate left censored data, we computed non-parametric MLE of CIF based on Expectation-Maximization algorithm. Our proposed parametric model was applied to the Nun Study to investigate the effect of genetics and education on the occurrence of dementia. After including left censored dementia subjects, the incidence rate of dementia becomes larger than that of death for age < 90, education becomes significant factor for incidence of dementia and standard errors for estimates are smaller. Multi-state Markov model is often used to analyze the evolution of cognitive states by assuming time independent transition intensities. We consider both constant and duration time dependent transition intensities in BRAiNS data, leading to a mixture of Markov and semi-Markov processes. The joint probability of observing a sequence of same state until transition in a semi-Markov process was expressed as a product of the overall transition probability and survival probability, which were simultaneously modeled. Such modeling leads to different interpretations in BRAiNS study, i.e., family history, APOE4, and sex by head injury interaction are significant factors for transition intensities in traditional Markov model. While in our semi-Markov model, these factors are significant in predicting the overall transition probabilities, but none of these factors are significant for duration time distribution.

Un des points cruciaux en pharmacoépidémiologie concerne le suivi longitudinal de l’exposition médicamenteuse dans les bases de données médico-administratives. Le parcours médicamenteux dans la « vraie » vie étant rarement linéaire, les trajectoires d’exposition médicamenteuses sont particulièrement complexes (notamment pour les médicaments psychoactifs) et difficilement mesurable par les méthodologies habituelles. Dans un premier temps nous avons ainsi étudié les trajectoires d’exposition à la méthadone notamment entre ses 2 formes galéniques à partir du SNIIRAM en région PACA-Corse. Cependant, la complexité des trajectoires d’exposition à la méthadone rendait leur description difficile. Pour modéliser précisément ces processus complexes un modèle multi-états a été construit. Dans ces travaux, nous avons pu identifier dans les bases de données des périodes ou l’exposition médicamenteuse ne peut être observée et pouvant engendrer un biais de classification des patients entre exposés et non exposés. Dans un second temps, nous avons donc étudié l’impact de ces périodes induites par les séjours hospitaliers a été évalué sur l’estimation du niveau d’exposition aux antipsychotiques de patients atteints de démence. En faisant l’hypothèse des « extrêmes » nous avons mis en évidence la variabilité importante induite par ces périodes. Enfin nous les avons modélisées et étudié leur impact sur la relation entre exposition aux benzodiazépines et mortalité toutes causes à 1 an à partir de l’EGB. L’ensemble de ce travail de thèse a permis de développer des méthodologies permettant une analyse plus précise de la dynamique d’exposition médicamenteuse. / In pharmacoepidemiology, one of the main concerns is analysis of drug exposure time in claim databases. In real-life settings, trajectories of patients ‘exposure are complex especially with psychoactive drugs and difficult to measure with traditional methodologies. In a first stage, we have highlighted the methadone exposure paths including between its two dosages formulations. This work underlined the multiplicity of exposure trajectories to methadone and the difficulty of making an accurate description. Consequently, we developed a multi-state model on a large claims database (SNIIR-AM) in order to investigate variations of methadone exposure with time. In this work, we identified the presence of periods or drug exposure cannot be observed in these databases. These periods lead to an unobservable or immeasurable exposure time bias in which patients are misclassified as unexposed. In a second stage, we assessed their impact on the prevalence of long-term antipsychotic use in community-dwelling patients with dementia considering hospitalization periods during which drugs administered are not available within almost all health insurance databases. Under extreme bias hypothesis the prevalence of long-term antipsychotic users almost doubled. Finally, we sought to model unobservable periods due to hospitalization and to apply several methods for addressing this bias and assess their impact on risk estimates. This approach was applied to the study of the association between benzodiazepines and mortality and was performed on the EGB database. In this thesis work we have developed methodologies for a more accurate analysis of the dynamics of drug exposure.

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models. Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the observed data likelihood function is based on the model of a joint distribution of the response longitudinal event history data and the discrete covariate with missing values. Secondly, we proposed a maximum simulated likelihood method to deal with the missing continuous covariate when applying multi-state models. The observed data likelihood function was approximated by using the Monte Carlo simulation method. At last, an EM algorithm was used to deal with multiple missing covariates when estimating the parameters of multi-state model. The EM algorithm would be able to handle multiple missing discrete covariates in general missing pattern efficiently. All the proposed methods are justified by simulation studies and applications to the datasets from the SMART project, a consortium of 11 different high-quality longitudinal studies of aging and cognition.