The overall aim of this study was to investigate the association between family structure in childhood and mental health problems in young adulthood. A prospective cohort study was conducted with 481,777 individuals with complete follow-up information, which was obtained from national registers in Sweden. Individuals who were living with only one biological parent at age 17 were compared with those who grew up with two parents with regard to retrieval of prescribed psychotropic drugs at age 35. The association was examined by Cox regression analyses with equal survival time for all individuals included in the analyses. The results demonstrated a higher risk for retrieval of psychotropic medicines among the individuals who grew up with only one parent, with hazard ratio of 1,21 (95%CI: 1,19-1,23). The multivariate analyses showed that a part of the association was explained by familial and individual factors, namely parents’ country of origin, area of residence, parents’ and the individual’s educational attainment, receipt of social benefits and parents’ history of psychiatric disorder. The results indicated that the increased risk of mental health problems among individuals who grew up with only one parent might be accounted for by various psychological, social and economic factors associated to parental separation.

Microbiome plays an important role in human health. The analysis of association between microbiome and clinical outcome has become an active direction in biostatistics research. Testing the microbiome effect on clinical phenotypes directly using operational taxonomic unit abundance data is a challenging problem due to the high dimensionality, non-normality and phylogenetic structure of the data. Most of the studies only focus on describing the change of microbe population that occur in patients who have the specific clinical condition. Instead, a statistical strategy utilizing distance-based or similarity-based non-parametric testing, in which a distance or similarity measure is defined between any two microbiome samples, is developed to assess association between microbiome composition and outcomes of interest. Despite the improvements, this test is still not easily interpretable and not able to adjust for potential covariates. A novel approach, kernel-based semi-parametric regression framework, is applied in evaluating the association while controlling the covariates. The framework utilizes a kernel function which is a measure of similarity between samples' microbiome compositions and characterizes the relationship between the microbiome and the outcome of interest. This kernel-based regression model, however, cannot be applied in longitudinal studies since it could not model the correlation between the repeated measurements. We proposed microbiome association exact tests (MAETs) in linear mixed model can deal with longitudinal microbiome data. MAETs can test not only the effect of overall microbiome but also the effect from specific cluster of the OTUs while controlling for others by introducing more random effects in the model. The current methods for multiple variance component testing are based on either asymptotic distribution or parametric bootstrap which require large sample size or high computational cost. The exact (R)LRT tests, an computational efficient and powerful testing methodology, was derived by Crainiceanu. Since the exact (R)LRT can only be used in testing one variance component, we proposed an approach that combines the recent development of exact (R)LRT and a strategy for simplifying linear mixed model with multiple variance components to a single case. The Monte Carlo simulation studies present correctly controlled type I error and provided superior power in testing association between microbiome and outcomes in longitudinal studies. Finally, the MAETs were applied to longitudinal pulmonary microbiome datasets to demonstrate that microbiome composition is associated with lung function and immunological outcomes. We also successfully found two interesting genera Prevotella and Veillonella which are associated with forced vital capacity.

Thesis (MCom)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: A multi-state model is a way of describing a process in which a subject moves through a series of states in continuous time. The series of states might be the measurement of a disease for example in state 1 we might have subjects that are free from disease, in state 2 we might have subjects that have a disease but the disease is mild, in state 3 we might have subjects having a severe disease and in last state 4 we have those that die because of the disease. So Markov models estimates the transition probabilities and transition intensity rates that describe the movement of subjects between these states. The transition might be for example a particular subject or patient might be slightly sick at age 30 but after 5 years he or she might be worse. So Markov model will estimate what probability will be for that patient for moving from state 2 to state 3. Markov multi-state models were studied in this thesis with the view of assessing the Markov models assumptions such as homogeneity of the transition rates through time, homogeneity of the transition rates across the subject population and Markov property or assumption. The assessments of these assumptions were based on simulated panel or longitudinal dataset which was simulated using the R package named msm package developed by Christopher Jackson (2014). The R code that was written using this package is attached as appendix. Longitudinal dataset consists of repeated measurements of the state of a subject and the time between observations. The period of time with observations in longitudinal dataset is being made on subject at regular or irregular time intervals until the subject dies then the study ends. / AFRIKAANSE OPSOMMING: ’n Meertoestandmodel is ’n manier om ’n proses te beskryf waarin ’n subjek in ’n ononderbroke tydperk deur verskeie toestande beweeg. Die verskillende toestande kan byvoorbeeld vir die meting van siekte gebruik word, waar toestand 1 uit gesonde subjekte bestaan, toestand 2 uit subjekte wat siek is, dog slegs matig, toestand 3 uit subjekte wat ernstig siek is, en toestand 4 uit subjekte wat aan die siekte sterf. ’n Markov-model raam die oorgangswaarskynlikhede en -intensiteit wat die subjekte se vordering deur hierdie toestande beskryf. Die oorgang is byvoorbeeld wanneer ’n bepaalde subjek of pasiënt op 30-jarige ouderdom net lig aangetas is, maar na vyf jaar veel ernstiger siek is. Die Markov-model raam dus die waarskynlikheid dat so ’n pasiënt van toestand 2 tot toestand 3 sal vorder. Hierdie tesis het ondersoek ingestel na Markov-meertoestandmodelle ten einde die aannames van die modelle, soos die homogeniteit van oorgangstempo’s oor tyd, die homogeniteit van oorgangstempo’s oor die subjekpopulasie en tipiese Markov-eienskappe, te beoordeel. Die beoordeling van hierdie aannames was gegrond op ’n gesimuleerde paneel of longitudinale datastel wat met behulp van Christopher Jackson (2014) se R-pakket genaamd msm gesimuleer is. Die R-kode wat met behulp van hierdie pakket geskryf is, word as bylae aangeheg. Die longitudinale datastel bestaan uit herhaalde metings van die toestand waarin ’n subjek verkeer en die tydsverloop tussen waarnemings. Waarnemings van die longitudinale datastel word met gereelde of ongereelde tussenposes onderneem totdat die subjek sterf, wanneer die studie dan ook ten einde loop.

A prospective, longitudinal and observational study, using a non-clinical population of mother-child dyads was conducted to evaluate the contribution of family-environmental factors in predicting child food avoidance and feeding problems across early childhood. The contribution of maternal feeding practices, mealtime structure and interactional behaviour during mealtimes, were explored in predicting child food avoidance between 2 and 5 years, whilst also evaluating the role of maternal psychopathology and child temperament. This thesis also assessed the validity of maternal reports of child eating behaviour and feeding practices by obtaining independent observations of these constructs, and explored the longitudinal stability and continuity of both independent observations and maternal reports of child eating behaviour and maternal feeding practices. Concurrently and prospectively, observations of mothers eating with their child, displaying high sensitivity, low control, and more positive emotion and verbalisation during mealtimes predicted less avoidant child eating behaviour. Reports of mothers providing a healthy food-related home environment, encouraging balanced food intake, and involving their child in food planning, in addition to a less emotional child temperament, were also significant longitudinal predictors of less avoidant child eating behaviour. Maternal descriptions of their child s eating behaviour were validated by independent observations; however maternal descriptions of their own feeding practices were not. Child eating behaviour and maternal feeding practices were predominantly stable and continuous across early childhood, with the exception of child difficulty to feed and maternal pressure to eat which decreased between the ages of 3 and 4. This thesis demonstrates many interesting and novel findings but primarily through the utilisation of observational and longitudinal data it demonstrates the important causal contribution of family-environmental factors in the development of food avoidant eating behaviours during early childhood.

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.

published_or_final_version / abstract / Medical Sciences / Master / Master of Medical Sciences

Recent large-N studies conclude that inequality and ethnic distribution have no significant impact on the risk of civil conflict. This study argues that such conclusions are erroneous and premature due to incorrect specification of independent variables and functional forms. Case studies suggest that measures of inter-group inequality (horizontal inequality) and polarization (ethnic distribution distance from a bipolar equilibrium) are more accurate predictors of civil conflict, as they better capture the group-motivation aspect of conflict. This study explores whether indicators of inequality and ethnic distribution impact the probability of civil conflict across 38 developing countries in the period 1986 to 2004. Analysis reveals that horizontal inequality and polarization have significant, robust relationships with civil conflict. Furthermore, vertical, or individual, inequality is a robust, significant predictor of civil conflict when specified as a nonlinear function.

Census data indicates people with higher levels of education are leaving Appalachian Kentucky as they do in other rural areas. Aside from anecdotal information and primarily qualitative community studies, there is little quantitative evidence of the factors which may influence these migration decisions. State policies and regional efforts to increase educational attainment of people in the region have focused on producing more college degrees however may be contributing to the out-migration of those with higher levels of education. The study incorporates community level data with demographic, academic, and employment data from a cohort of 2005-06 college graduates from Appalachian Kentucky. The study includes an analysis of migration rates for a variety of different types of graduates and a set of three complimentary logistic regression models developed to understand the impact of individual demographic and academic factors, factors about the communities where these graduates came from, and the factors related to the communities where they went after completing their degrees and credentials to predict likelihood of migrating. This study builds upon previous efforts by providing extensive, externally validated data about a large population of individuals. It leverages sociological, demographic, and neoclassical microeconomic research methods and leverages data from Kentucky's statewide longitudinal data system to serve as an illustration for how these systems can be used for complex statistical analyses.

Dementia is increasingly recognized as a major and growing threat to public health worldwide, and there is a critical need for prevention and treatment strategies. However, it is necessary that appropriate methodologies are used in the identification of risk factors. The purpose of this dissertation research was to develop further the body of literature featuring Markov chains as an analytic tool for data derived from longitudinal studies of aging and dementia. Data drawn from 649 participants in the University of Kentucky’s Alzheimer’s Disease Center’s (UK ADC) Biologically Resilient Adults in Neurological Studies (BRAiNS) cohort, which was established in 1989 and follows adults age 60 years and older who are cognitively normal at baseline to death, were used to conduct three studies. The first study, “Mild cognitive impairment: Statistical models of transition using longitudinal clinical data,” shows that mild cognitive impairment is a stable clinical entity when a rigorous definition is applied. The second study, “Self-reported head injury and risk of cognitive impairment and Alzheimer’s-type pathology in a longitudinal study of aging and dementia,” shows that when the competing risk of death is properly accounted for, self-reported head injury is a clear risk factor for late-life dementia and is associated with increased beta-amyloid deposition in the brain. The third study, “Incorporating prior-state dependence among random effects and beta coefficients improves multistate Markov chain model fit,” shows that the effect of risk factors, like age, may not be constant over time and may be altered based on the subject’s cognitive state and that model fit is significantly improved when this is taken into account.

Le processus de socialisation peut s'envisager sous l'angle des mouvements des relations interpersonnelles nouées par les individus. S'appuyant sur une enquête longitudinale articulant recueils de données structurelles et qualitatives, cette thèse étudie l'évolution sur neuf ans des réseaux de sociabilité de soixante jeunes entrant dans vie adulte. Les dispositifs de communication en forment l'analyseur central. Leurs usages sont explorés à partir de l'analyse des intrications entre la dynamique des réseaux sociaux et celle des parcours biographiques.<br />Le premier axe problématique porte sur les logiques d'équipement. Une typologie montre que la diversité des modes d'accès aux TIC est en rapport avec des modèles transitionnels, définis comme l'intersection des trajectoires matrimoniales et professionnelles. <br />Ensuite, des portraits détaillés, catégorie par catégorie, mettent en évidence les liens entre le cheminement des acteurs, les différenciations sociales dans l'élaboration des sociabilités et les dynamiques d'appropriation des TIC. Les trajectoires d'usage se révèlent fondamentalement associées à des rythmes biographiques typiques portant la marque des héritages sociaux et scolaires comme des rapports sociaux de sexe.