Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.

Background: Childhood excess weight and family food insecurity are food-system related public health problems that exist in Canada. Since both relate to issues of food accessibility and availability, which have elements of “place”, they may share common risk factors in the local environment that are amenable to intervention. In this area of research, the literature derives mostly from a US context, and there is a dearth of high quality evidence, specifically from longitudinal studies. Objectives: The main objectives of this thesis were to examine the adjusted associations between the place factors: material deprivation, social deprivation, social cohesion, disorder, and living location, with change in child BMI Z-score and with change in family food insecurity status in a Canadian cohort of children. Methods: The Québec Longitudinal Study of Child Development was used to meet the main objectives of this thesis. Response data from six collection cycles (4 – 10 years of age) were used in three main analyses. The first analysis examined change in child BMI Z-score as a function of the place factors using mixed models regression. The second analysis examined change in child BMI Z-score as a function of place factors using group-based trajectory modeling. The third and final analysis examined change in family food insecurity status as a function of the place factors using generalized estimating equations. Results: Social deprivation, social cohesion and disorder were strongly and positively associated with family food insecurity, increasing the odds by 45-76%. These place factors, on the other hand, were not consistently associated with child weight status. Material deprivation was not important for either outcome, except for a slight positive association in the mixed models analysis of child weight status. Living location was not important in explaining family food insecurity. On the other hand, it was associated with child weight status in both analyses, but the nature of the relationship is still unclear. Conclusions: Results do not suggest that addressing similar place factors may alleviate both child excess weight and family food insecurity. More high quality longitudinal and experimental studies are needed to clarify relationships between the local environment and child weight status and family food insecurity.

Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.

This research develops a framework to estimate the effective sample size of Global Positioning System (GPS) based panel surveys in urban travel behavior studies for a variety of planning purposes. Recent advances in GPS monitoring technologies have made it possible to implement panel surveys with lengths of weeks, months or even years. The many advantageous features of GPS-based panel surveys make such surveys attractive for travel behavior studies, but the higher cost of such surveys compared to conventional one-day or two-day paper diary surveys requires scrutiny at the sample size planning stage to ensure cost-effectiveness. The sample size analysis in this dissertation focuses on three major aspects in travel behavior studies: 1) to obtain reliable means for key travel behavior variables, 2) to conduct regression analysis on key travel behavior variables against explanatory variables such as demographic characteristics and seasonal factors, and 3) to examine impacts of a policy measure on travel behavior through before-and-after studies. The sample size analyses in this dissertation are based on the GPS data collected in the multi-year Commute Atlanta study. The sample size analysis with regard to obtaining reliable means for key travel behavior variables utilizes Monte Carlo re-sampling techniques to assess the trend of means against various sample size and survey length combinations. The basis for the framework and methods of sample size estimation related to regression analysis and before-and-after studies are derived from various sample size procedures based on the generalized estimating equation (GEE) method. These sample size procedures have been proposed for longitudinal studies in biomedical research. This dissertation adapts these procedures to the design of panel surveys for urban travel behavior studies with the information made available from the Commute Atlanta study. The findings from this research indicate that the required sample sizes should be much larger than the sample sizes in existing GPS-based panel surveys. This research recommends a desired range of sample sizes based on the objectives and survey lengths of urban travel behavior studies.

Penalized methods are becoming more and more popular in statistical research. This dissertation research covers two major aspects of applications of penalized methods: variable selection and nonparametric function estimation. The following two paragraphs give brief introductions to each of the two topics. Infinite variance autoregressive models are important for modeling heavy-tailed time series. We use a penalty method to conduct model selection for autoregressive models with innovations in the domain of attraction of a stable law indexed by alpha is an element of (0, 2). We show that by combining the least absolute deviation loss function and the adaptive lasso penalty, we can consistently identify the true model. At the same time, the resulting coefficient estimator converges at a rate of n^(?1/alpha) . The proposed approach gives a unified variable selection procedure for both the finite and infinite variance autoregressive models. While automatic smoothing parameter selection for nonparametric function estimation has been extensively researched for independent data, it is much less so for clustered and longitudinal data. Although leave-subject-out cross-validation (CV) has been widely used, its theoretical property is unknown and its minimization is computationally expensive, especially when there are multiple smoothing parameters. By focusing on penalized modeling methods, we show that leave-subject-out CV is optimal in that its minimization is asymptotically equivalent to the minimization of the true loss function. We develop an efficient Newton-type algorithm to compute the smoothing parameters that minimize the CV criterion. Furthermore, we derive one simplification of the leave-subject-out CV, which leads to a more efficient algorithm for selecting the smoothing parameters. We show that the simplified version of CV criteria is asymptotically equivalent to the unsimplified one and thus enjoys the same optimality property. This CV criterion also provides a completely data driven approach to select working covariance structure using generalized estimating equations in longitudinal data analysis. Our results are applicable to additive, linear varying-coefficient, nonlinear models with data from exponential families.

Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.

Background: Childhood excess weight and family food insecurity are food-system related public health problems that exist in Canada. Since both relate to issues of food accessibility and availability, which have elements of “place”, they may share common risk factors in the local environment that are amenable to intervention. In this area of research, the literature derives mostly from a US context, and there is a dearth of high quality evidence, specifically from longitudinal studies. Objectives: The main objectives of this thesis were to examine the adjusted associations between the place factors: material deprivation, social deprivation, social cohesion, disorder, and living location, with change in child BMI Z-score and with change in family food insecurity status in a Canadian cohort of children. Methods: The Québec Longitudinal Study of Child Development was used to meet the main objectives of this thesis. Response data from six collection cycles (4 – 10 years of age) were used in three main analyses. The first analysis examined change in child BMI Z-score as a function of the place factors using mixed models regression. The second analysis examined change in child BMI Z-score as a function of place factors using group-based trajectory modeling. The third and final analysis examined change in family food insecurity status as a function of the place factors using generalized estimating equations. Results: Social deprivation, social cohesion and disorder were strongly and positively associated with family food insecurity, increasing the odds by 45-76%. These place factors, on the other hand, were not consistently associated with child weight status. Material deprivation was not important for either outcome, except for a slight positive association in the mixed models analysis of child weight status. Living location was not important in explaining family food insecurity. On the other hand, it was associated with child weight status in both analyses, but the nature of the relationship is still unclear. Conclusions: Results do not suggest that addressing similar place factors may alleviate both child excess weight and family food insecurity. More high quality longitudinal and experimental studies are needed to clarify relationships between the local environment and child weight status and family food insecurity.

This thesis describes the design, maintenance and statistical analysis involved in undertaking a Longitudinal Survey. A longitudinal survey (or study) obtains observations or responses from individuals over several times over a defined period. This enables the direct study of changes in an individual's response over time. In particular, it distinguishes an individual's change over time from the baseline differences among individuals within the initial panel (or cohort). This is not possible in a cross-sectional study. As such, longitudinal surveys give correlated responses within individuals. Longitudinal studies therefore require different considerations for sample design and selection and analysis from standard cross-sectional studies. This thesis looks at the methodology for analysing social surveys. Most social surveys comprise of variables described as categorical variables. This thesis outlines the process of sample design and selection, interviewing and analysis for a longitudinal study. Emphasis is given to categorical response data typical of a survey. Included in this thesis are examples relating to the Goodna Longitudinal Survey and the Longitudinal Survey of Immigrants to Australia (LSIA). Analysis in this thesis also utilises data collected from these surveys. The Goodna Longitudinal Survey was conducted by the Queensland Office of Economic and Statistical Research (a portfolio office within Queensland Treasury) and began in 2002. It ran for two years whereby two waves of responses were collected.

Estudos em que a resposta de interesse é uma variável categorizada são bastante comuns nas mais diversas áreas da Ciência. Em muitas situações essa resposta é composta por mais de duas categorias não ordenadas, denominada então de uma variável politômica nominal, e em geral o objetivo do estudo é associar a probabilidade de ocorrência de cada categoria aos efeitos de variáveis explicativas. Ademais, existem tipos especiais de estudos em que os dados são coletados diversas vezes para uma mesma unidade amostral ao longo do tempo, os estudos longitudinais. Estudos assim requerem o uso de modelos estatísticos que considerem em sua formulação algum tipo de estrutura que suporte a dependência que tende a surgir entre observações feitas em uma mesma unidade amostral. Neste trabalho são abordadas duas extensões do modelo de logitos generalizados, usualmente empregado quando a resposta é politômica nominal com observações independentes entre si. A primeira consiste de uma modificação das equações de estimação generalizadas para dados nominais que se utiliza de razões de chances locais para descrever a dependência entre as observações da variável resposta politômica ao longo dos diversos tempos observados. Este tipo de modelo é denominado de modelo marginal. A segunda proposta abordada consiste no modelo de logitos generalizados com a inclusão de efeitos aleatórios no preditor linear, que também leva em conta uma dependência entre as observações. Esta abordagem caracteriza o modelo de logitos generalizados misto. Há diferenças importantes inerentes às interpretações dos modelos marginais e mistos, que são discutidas e que devem ser levadas em consideração na escolha da abordagem adequada. Ambas as propostas são aplicadas em um conjunto de dados proveniente de um experimento da área agronômica realizado em campo, conduzido sob um delineamento casualizado em blocos com esquema fatorial para os tratamentos. O experimento foi acompanhado ao longo de seis estações do ano, caracterizando assim uma estrutura longitudinal, sendo a variável resposta o tipo de vegetação observado no campo (touceiras, plantas invasoras ou espaços vazios). Os resultados encontrados são satisfatórios, embora a dependência presente nos dados não seja tão caracterizada; por meio de testes como da razão de verossimilhanças e de Wald diversas diferenças significativas entre os tratamentos foram encontradas. Ainda, devido às diferenças metodológicas das duas abordagens, o modelo marginal baseado nas equações de estimação generalizadas mostra-se mais adequado para esses dados. / Studies where the response is a categorical variable are quite common in many fields of Sciences. In many situations this response is composed by more than two unordered categories characterizing a nominal polytomous outcome and, in general, the aim of the study is to associate the probability of occurrence of each category to the effects of variables. Furthermore, there are special types of study where many measurements are taken over the time for the same sampling unit, called longitudinal studies. Such studies require special statistical models that consider some kind of structure that support the dependence that tends to arise from the repeated measurements for the same sampling unit. This work focuses on two extensions of the baseline-category logit model usually employed in cases when there is a nominal polytomous response with independent observations. The first one consists in a modification of the well-known generalized estimating equations for longitudinal data based on local odds ratios to describe the dependence between the levels of the response over the repeated measurements. This type of model is also known as a marginal model. The second approach adds random effects to the linear predictor of the baseline-category logit model, which also considers a dependence between the observations. This characterizes a baseline-category mixed model. There are substantial differences inherent to interpretations when marginal and mixed models are compared, what should be considered in the choice of the most appropriated approach for each situation. Both methodologies are applied to the data of an agronomic experiment installed under a complete randomized block design with a factorial arrangement for the treatments. It was carried out over six seasons, characterizing the longitudinal structure, and the response is the type of vegetation observed in field (tussocks, weeds or regions with bare ground). The results are satisfactory, even if the dependence found in data is not so strong, and likelihood-ratio and Wald tests point to several differences between treatments. Moreover, due to methodological differences between the two approaches, the marginal model based on generalized estimating equations seems to be more appropriate for this data.

Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.