Time-to-event data is used in this thesis to analyze private cars’ longevity in Sweden. Thedataset is provided by Trafikanalys and contains all registered, deregistered or temporary deregisteredcars in Sweden during the time period 2000 - 2012.A Cox proportional hazards model is fitted, including variables such as car manufacturer andcar body. The results show that directly imported cars have a much shorter median survivalcompared to non-imported cars. The convertible cars have the longest median survival amongthe five different car bodies. Sedan and station wagon body types have the shortest mediansurvival. Volvo and Mercedes have the longest survival while Renault, Ford and Opel have theshortest survival. The model fits the data reasonably well, and the assumption of proportionalhazards holds for most of the variables.

No description available.

Background: The study examined the effects of the determinants of under-five mortality in Malawi. It therefore aimed to estimate the rate or prevalence of under-five mortality in Malawi and to examine differentials in infant and child mortality by socio-economic, demographic, environmental, health-seeking behaviour and nutritional value. Methods: This study involved a secondary data analysis of the 2010 Malawi Demographic and Health Survey (MDHS) data set of children under five years old and women who had given birth in the five years preceding the survey. The Kaplan-Meier survival analysis and multivariate hazard analysis were used to examine the relationship between under-five mortality and socio-economic. demographic, environmental, health-seeking behaviour and nutritional factors. Results: The results show that birth order, mother's education, place of residence. region and exclusive breastfeeding were significantly associated with under-five mortality. The results also show that there was no significant association between under-five mortality and other indicators of socio-economic. demographic. environmental, health-seeking behaviour. The results also show that more deaths of under-fives occurred during infancy than during childhood. Conclusion: The results show that more deaths occurred during the first months after birth than after 12 months of age. This showed that mother's education, birth order, place of residence, region and breastfeeding had a greater influence on the survival of the child. / Thesis (M.Soc.Sc. Population Studies) North-West University, Mafikeng Campus, 2014

In Sweden, all banks must report their regulatory capital in their reports to the market and their models for calculating this capital must be approved by the financial authority, Finansinspektionen. The regulatory capital is the capital that a bank has to hold as a security for credit risk and this capital should serve as a buffer if they would loose unexpected amounts of money in their lending business. Loss-Given-Default (LGD) is one of the main drivers of the regulatory capital and the minimum required capital is highly sensitive to the reported LGD. Workout LGD is based on the discounted future cash flows obtained from defaulted customers. The main issue with workout LGD is the incomplete workouts, which in turn results in two problems for banks when they calculate their workout LGD. A bank either has to wait for the workout period to end, in which some cases take several years, or to exclude or make rough assumptions about those incomplete workouts in their calculations. In this study the idea from Survival analysis (SA) methods has been used to solve these problems. The mostly used SA model, the Cox proportional hazards model (Cox model), has been applied to investigate the effect of covariates on the length of survival for a monetary unit. The considered covariates are Country of booking, Secured/Unsecured, Collateral code, Loan-To-Value, Industry code, Exposure-At- Default and Multi-collateral. The data sample was first split into 80 % training sample and 20 % test sample. The applied Cox model was based on the training sample and then validated with the test sample through interpretation of the Kaplan-Meier survival curves for risk groups created from the prognostic index (PI). The results show that the model correctly rank the expected LGD for new customers but is not always able to distinguish the difference between risk groups. With the results presented in the study, Nordea can get an expected LGD for newly defaulted customers, given the customers’ information on the considered covariates in this study. They can also get a clear picture of what factors that drive a low respectively high LGD. / I Sverige måste alla banker rapportera sitt lagstadgade kapital i deras rapporter till marknaden och modellerna för att beräkna detta kapital måste vara godkända av den finansiella myndigheten, Finansinspektionen. Det lagstadgade kapitalet är det kapital som en bank måste hålla som en säkerhet för kreditrisk och den agerar som en buffert om banken skulle förlora oväntade summor pengar i deras utlåningsverksamhet. Loss- Given-Default (LGD) är en av de främsta faktorerna i det lagstadgade kapitalet och kravet på det minimala kapitalet är mycket känsligt för det rapporterade LGD. Workout LGD är baserat på diskonteringen av framtida kassaflöden från kunder som gått i default. Det huvudsakliga problemet med workout LGD är ofullständiga workouts, vilket i sin tur resulterar i två problem för banker när de ska beräkna workout LGD. Banken måste antingen vänta på att workout-perioden ska ta slut, vilket i vissa fall kan ta upp till flera år, eller så får banken exkludera eller göra grova antaganden om dessa ofullständiga workouts i sina beräkningar. I den här studien har idén från Survival analysis (SA) metoder använts för att lösa dessa problem. Den mest använda SA modellen, Cox proportional hazards model (Cox model), har applicerats för att undersöka effekten av kovariat på livslängden hos en monetär enhet. De undersökta kovariaten var Land, Säkrat/Osäkrat, Kollateral-kod, Loan-To-Value, Industri-kod Exposure-At-Default och Multipla-kollateral. Dataurvalet uppdelades först i 80 % träningsurval och 20 % testurval. Den applicerade Cox modellen baserades på träningsurvalet och validerades på testurvalet genom tolkning av Kaplan-Meier överlevnadskurvor för riskgrupperna skapade från prognosindexet (PI). Med de presenterade resultaten kan Nordea beräkna ett förväntat LGD för nya kunder i default, givet informationen i den här studiens undersökta kovariat. Nordea kan också få en klar bild över vilka faktorer som driver ett lågt respektive högt LGD.

With the growth of Pentecostalism over the last few decades, conservative values and punitive sanctions related to the sexual behavior of adolescents and unmarried youth began to play an important and systematic role in Pentecostal and renewed Protestant churches as well as in charismatic Catholic communities. Simultaneously, religion has become an important and highly present factor in the lives of many adolescents and youth in Brazil. In terms of attempting to attract this age group, these churches and communities, stand out, as they have used their resources to create a space for this segment of the population to participate in a religious environment. Youth groups, dating groups, trade courses, lectures, aid work in poor communities, confirmation and other activities such as retreats and religious trips, have been frequently observed in these churches and charismatic communities. In this dissertation, I examine the associations between religious involvement and sexual initiation in Brazil. More specifically, I investigate (1) whether religious denomination and religiosity are associated with age at premarital first sexual intercourse, (2) whether these associations have changed over the last three decades, (3) how different churches and religious leaders address sexual behavior issues, and (4) the mechanisms through which religion can influence adolescents’ sexual behavior in Brazil. These research questions are assessed by employing multiple data sources and methodologies including three Demographic and Health Surveys carried out in Brazil in 1986, 1996, and 2006 and event history analysis, as well as in-depth interview data and participant observation among different religious groups and affiliations by attending several Catholic masses, Protestant religious services, youth groups, Sunday schools, and religious talks/lectures. Quantitative and qualitative findings of this dissertation show that adolescents and youth from Pentecostal churches and communities seem more likely to delay or abstain from premarital sexual initiation when compared to traditional Catholics. I conclude by suggesting that the dissemination of conservative norms and sanctions as well as the availability of greater space for youth to maintain close relationships with these churches have helped create mechanisms through which religion can directly and indirectly influence the lives and sexual behavior of young people in Brazil. / text

O objetivo deste trabalho foi comparar o desempenho do modelo de riscos proporcionais de Cox convencional, modelo de Cox modificado quando os riscos não são proporcionais e o modelo de análise de sobrevida baseado na teoria de processos de contagem. Para tanto utilizou-se uma coorte de 648 pacientes portadores de câncer de pênis, atendidos no Departamento de Cirurgia Pélvica do Hospital A. C. Camargo, no período de 1953 a 1985. Dessa coorte foram selecionadas três amostras com o objetivo de validar internamente os resultados da análise de sobrevida do banco de dados original. Os resultados do modelo de riscos proporcionais de Cox, no banco de dados original, foram confirmados por uma das amostras desse conjunto de dados. Apenas o estadiamento N foi confirmado como fator prognóstico também nas outras duas amostras. O modelo de riscos proporcionais de Cox e o modelo de análise de sobrevida baseado na teoria de processos de contagem apresentaram resultados semelhantes, na definição dos fatores prognósticos dessa coorte de pacientes com câncer de pênis. O modelo utilizando processos de contagem é mais sofisticado, do ponto de vista matemático. Porém o modelo de Cox está disponível em grande número de pacotes estatísticos e a interpretação de seus coeficientes se faz com maior facilidade. Por isso, talvez, continue a ser a técnica estatística mais utilizada quando o objetivo do estudo é definir fatores prognósticos e grupos de risco. Os fatores prognósticos para a sobrevida de pacientes com câncer de pênis foram os estadiamentos T e N e o grau de diferenciação do tumor. Esses resultados foram ajustados pelo ano de início de tratamento no Hospital A.C. Camargo. Os pacientes com prognóstico favorável foram os que apresentaram tumor pequeno, sem presença de linfonodos clinicamente positivos, e tumor bem diferenciado. / The aim of this study was to compare the performance of the Cox proportional hazards model, the Cox model with time-dependent covariates and the survival model using the counting process theory. These methods were applied in a cohort of 648 patients with penile cancer treated at the Department of Pelvic Surgery, Hospital A.C. Camargo (São Paulo-Brazil), between 1953 and 1985. Three samples were selected from the total database in order to check the internal validity. The prognostic factors selected using the Cox proportional hazards model were the same in one sample. The only prognostic factor selected in all samples was the N stage. The T and N stages, and the grade of differentiation were independent prognostic factors of survival using both the Cox proportional hazards model and the survival,model using the counting process theory. The statistical significance was the same and even the values of estimation of the coefficients were very close. The survival model using the counting process is more sophisticated from the mathematical point of view, but the Cox model is more available in statistical software, and, probably because of this, is more applied in survival analysis than the model using the counting processo Patients with small tumors, clinically negatives nodes and well differentiated tumors showed a favorable prognosis. These results were adjusted by year of the beginning in the study.

In this paper we propose two simple algorithms to estimate parameters £] and baseline survival function in Cox proportional hazard model with application to Massachusetts Health Care Panel Study (MHCPS) (Chappell, 1991) data which is a left truncated and interval censored data. We find that, in the estimation of £] and baseline survival function, Kaplan and Meier algorithm is uniformly better than the Empirical algorithm. Also, Kaplan and Meier algorithm is uniformly more powerful than the Empirical algorithm in testing whether two groups of survival functions are the same. We also define a distance measure D and compare the performance of these two algorithms through £] and D.

O objetivo deste trabalho foi comparar o desempenho do modelo de riscos proporcionais de Cox convencional, modelo de Cox modificado quando os riscos não são proporcionais e o modelo de análise de sobrevida baseado na teoria de processos de contagem. Para tanto utilizou-se uma coorte de 648 pacientes portadores de câncer de pênis, atendidos no Departamento de Cirurgia Pélvica do Hospital A. C. Camargo, no período de 1953 a 1985. Dessa coorte foram selecionadas três amostras com o objetivo de validar internamente os resultados da análise de sobrevida do banco de dados original. Os resultados do modelo de riscos proporcionais de Cox, no banco de dados original, foram confirmados por uma das amostras desse conjunto de dados. Apenas o estadiamento N foi confirmado como fator prognóstico também nas outras duas amostras. O modelo de riscos proporcionais de Cox e o modelo de análise de sobrevida baseado na teoria de processos de contagem apresentaram resultados semelhantes, na definição dos fatores prognósticos dessa coorte de pacientes com câncer de pênis. O modelo utilizando processos de contagem é mais sofisticado, do ponto de vista matemático. Porém o modelo de Cox está disponível em grande número de pacotes estatísticos e a interpretação de seus coeficientes se faz com maior facilidade. Por isso, talvez, continue a ser a técnica estatística mais utilizada quando o objetivo do estudo é definir fatores prognósticos e grupos de risco. Os fatores prognósticos para a sobrevida de pacientes com câncer de pênis foram os estadiamentos T e N e o grau de diferenciação do tumor. Esses resultados foram ajustados pelo ano de início de tratamento no Hospital A.C. Camargo. Os pacientes com prognóstico favorável foram os que apresentaram tumor pequeno, sem presença de linfonodos clinicamente positivos, e tumor bem diferenciado. / The aim of this study was to compare the performance of the Cox proportional hazards model, the Cox model with time-dependent covariates and the survival model using the counting process theory. These methods were applied in a cohort of 648 patients with penile cancer treated at the Department of Pelvic Surgery, Hospital A.C. Camargo (São Paulo-Brazil), between 1953 and 1985. Three samples were selected from the total database in order to check the internal validity. The prognostic factors selected using the Cox proportional hazards model were the same in one sample. The only prognostic factor selected in all samples was the N stage. The T and N stages, and the grade of differentiation were independent prognostic factors of survival using both the Cox proportional hazards model and the survival,model using the counting process theory. The statistical significance was the same and even the values of estimation of the coefficients were very close. The survival model using the counting process is more sophisticated from the mathematical point of view, but the Cox model is more available in statistical software, and, probably because of this, is more applied in survival analysis than the model using the counting processo Patients with small tumors, clinically negatives nodes and well differentiated tumors showed a favorable prognosis. These results were adjusted by year of the beginning in the study.

One of the most common measures of clinical outcomes is the survival time. Accurately linking cancer molecular profiling with survival outcome advances clinical management of cancer. However, existing survival analysis relies intensively on statistical evidence from a single level of data, without paying much attention to the integration of interacting multi-level data and the underlying biology. Advances in genomic techniques provide unprecedented power of characterizing the cancer tissue in a more complete manner than before, opening the opportunity of designing biologically informed and integrative approaches for survival analysis. Many cancer tissues have been profiled for gene expression levels and genomic variants (such as copy number alterations, sequence mutations, DNA methylation, and histone modification). However, it is not clear how to integrate the gene expression and genetic variants to achieve a better prediction and understanding of the cancer survival. To address this challenge, we propose two approaches for data integration in order to both biologically and statistically boost the features selection process for proper detection of the true predictive players of survival. The first approach is data-driven yet biologically informed. Consistent with the biological hierarchy from DNA to RNA, we prioritize each survival-relevant feature with two separate scores, predictive and mechanistic. With mRNA expression levels in concern, predictive features are those mRNAs whose variation in expression levels are associated with the survival outcome, and mechanistic features are those mRNAs whose variation in expression levels are associated with genomic variants (copy number alterations (CNAs) in this study). Further, we propose simultaneously integrating information from both the predictive model and the mechanistic model through our new approach GEMPS (Gene Expression as a Mediator for Predicting Survival). Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power than peer methods. Gene set enrichment analysis confirms that the genes utilized for the final survival analysis are biologically important and relevant. The second approach is a generic mathematical framework to biologically regularize the Cox's proportional hazards model that is widely used in survival analysis. We propose a penalty function that both links the mechanistic model to the clinical model and reflects the biological downstream regulatory effect of the genomic variants on the mRNA expression levels of the target genes. Fast and efficient optimization principles like the coordinate descent and majorization-minimization are adopted in the inference process of the coefficients of the Cox model predictors. Through this model, we develop the regulator-target gene relationship to a new one: regulator-target-outcome relationship of a disease. Assessed via a simulation study and analysis of two real cancer data sets, the proposed method showed better performance in terms of selecting the true predictors and achieving better survival prediction. The proposed method gives insightful and meaningful interpretability to the selected model due to the biological linking of the mechanistic model and the clinical model. Other important forms of clinical outcomes are monitoring angiogenesis (formation of new blood vessels necessary for tumor to nourish itself and sustain its existence) and assessing therapeutic response. This can be done through dynamic imaging, in which a series of images at different time instances are acquired for a specific tumor site after injection of a contrast agent. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive tool to examine tumor vasculature patterns based on accumulation and washout of the contrast agent. DCE-MRI gives indication about tumor vasculature permeability, which in turn indicates the tumor angiogenic activity. Observing this activity over time can reflect the tumor drug responsiveness and efficacy of the treatment plan. However, due to the limited resolution of the imaging scanners, a partial-volume effect (PVE) problem occurs, which is the result of signals from two or more tissues combining together to produce a single image concentration value within a pixel, with the effect of inaccurate estimation to the values of the pharmacokinetic parameters. A multi-tissue compartmental modeling (CM) technique supported by convex analysis of mixtures is used to mitigate the PVE by clustering pixels and constructing a simplex whose vertices are of a single compartment type. CAM uses the identified pure-volume pixels to estimate the kinetics of the tissues under investigation. We propose an enhanced version of CAM-CM to identify pure-volume pixels more accurately. This includes the consideration of the neighborhood effect on each pixel and the use of a barycentric coordinate system to identify more pure-volume pixels and to test those identified by CAM-CM. Tested on simulated DCE-MRI data, the enhanced CAM-CM achieved better performance in terms of accuracy and reproducibility. / Ph. D. / Disease outcome can refer to an event, state, condition, or behavior for some aspect of a patient’s health status. Event can express survival, while behavior can assess drug efficacy and treatment responsiveness. To gain deeper and, hence, better understanding about diseases, symptoms inspection has been shifted from the physical symptoms appearing externally on the human body to internal symptoms that require invasive and noninvasive techniques to find out and quantify them. These internal symptoms can be further divided into phenotypic and genotypic symptoms. Examples of phenotypes can include shape, structure, and volume of a specific human body organ or tissue. Examples of genotypes can be the dosage of the genetic information and the activity of genes, where genes are responsible for identifying the function of the cells constituting tissues. Linking disease phenotypes and genotypes to disease outcomes is of great importance to widen the understanding of disease mechanisms and progression. In this dissertation, we propose novel computational techniques to integrate data generated from different platforms, where each data type addresses one aspect of the disease internal symptoms, to provide wider picture and deeper understanding about a disease. We use imaging and genomic data with applications in ovarian, glioblastoma multiforme, and breast cancers to test the proposed techniques. These techniques aim to provide outcomes that are statistically significant, as what current peer methods do, beside biological insights, which current peer methods lack.

In Sweden, there has been an long term effort to allow as heavy traffic as possible, provided thatthe road network can handle it. This is because heavy traffic offers a competitive advantage withsocio-economic gains. In July 2018, the Swedish Transport Administration made 12 percent ofthe Swedish road network avaliable for the new maximum vehicle weight of 74 tonnes, basedon a legislative change from 2017. It is known that heavy traffic has a negative effect on thedegradation of the road, but it prevails divided opinions on whether 74 tonnes have a greaterimpact on the degradation rate compared to previous maximum gross weights of 64 tonnes.The 74 tonne vehicles have the same allowed axle load, which means more axles per vehicle. Some argue that an increased total load and more axles affect the degradation associated withtime-dependent material properties, while others argue that 74 tonnes mean fewer heavy vehiclesoverall, and thus should have a positive impact on the road’s lifespan. The construction companySkanska therefore requests a statistical analysis that enables to nuance the effects that heavytraffic has on the Swedish state road network. Since there is very limited data on the effect of 74 tonne traffic, this Master thesis instead focuseson modeling heavy traffic in general in order to be able to draw conclusions on which variablesare significant for a road’s lifetime. The method used is survival analysis where the lifetimeof the road is defined as the time between two maintenance treatments. The model selectedis the semi-parametric ’Cox Proportional Hazard Model’. The model is fitted with data froman open source database called LTPP (Long Term Pavement Performance) which is providedby the National Road and Transport Research Institute (VTI). The result of the modeling ispresented with hazard ratios, which is the relative risk that a road will require maintance atthe next time stamp compared to a reference category. The covariates that turned out to besignificant for a road’s lifetime and thus are included in the model are; lane width, undergroundtype, speed limit, asphalt layer thickness, bearing layer thickness and proportion of heavy traffic. Survival curves estimated by the model are also presented. In addition, a sensitivity analysis ismade by exploring survival curves estimated for different scenarios, with different combinationsof covariate levels.The results is then compared with previous studies on the subject. The most interesting finding isa case study from Finland since Finland allow 76 tonne vehicles since 2013. In the comparison,the model’s significant variables are confirmed, but the significance of precipitation and thenumber of axes for a roads lifetime is also highlighted