Prostate Cancer (PC) is associated with disease- and treatment-related side effects that can compromise quality of life (QoL). Psychosocial interventions designed to improve adjustment and quality of life (QoL) for post-treatment PC patients have been conducted with mixed results. Intervention effects are typically analyzed using either mean difference scores or a single estimate of growth parameters (e.g., intercept and slope factors) across groups. These methods assume homogeneity within groups. Evidence suggests, however, considerable variability both in the experience of disease-specific outcomes and in the long-term adjustment and emotional well-being of men with PC. The present study used growth mixture modeling (GMM) to explore the effects of a cognitive behavioral stress management (CBSM) intervention on emotional well-being among men recently treated for localized PC. This methodology allowed examination of intervention effects across unobserved subgroups characterized by different trajectories of emotional well-being and identified factors associated with intervention efficacy.

This study integrated variable- and child-centered techniques to investigate trajectories of four learning behaviors (initiative, persistence, planning, and problem-solving flexibility) and their influence on Head Start preschoolers' academic school readiness. Variable-centered findings revealed differential, quadratic growth trajectories for each of the four learning behaviors. However, where children began the year (intercept), how much they changed across the year (slope), and how much their rate of change changed across the year (quadratic) differed depending on the learning behavior. Initiative and problem-solving flexibility emerged as significant predictors of end-of-year academic school readiness skills, controlling for persistence and planning. There was no evidence of moderation of the relations between learning behaviors and academic skills by child demographic characteristics. Child-centered results provided a more nuanced description of the development of these four learning behaviors. Analyses suggested there may be subgroups of children with different developmental trajectories for each of the four learning behaviors and that these subgroups have significantly different school readiness skills at the end of the year. These findings help extend our current understanding of learning behaviors and, if replicated, may inform the content and timing of early childhood teaching practices and interventions.

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This study examined heterogeneity in adolescents experimentation with partnered sexual behaviours. Participants were 88 high school students in Edmonton, Alberta (M age = 16.59, SD = .95). Students completed surveys online once per two months from December, 2008 through December, 2009. Surveys tracked students reports of seven sexual behaviours ranging in intimacy from holding hands to intercourse. Growth mixture models were used to sort students trajectories of sexual behaviours across months into latent classes based on similar profiles. The best-fitting model revealed three distinct classes, labeled inexperienced, experimenting, and experienced. Students classified as inexperienced primarily reported only lower-intimacy, non-genital sexual behaviours across months, and many reported no sexual behaviours. Students classified as experimenting and experienced reported similar levels of higher-intimacy sexual behaviours across months. Most experimenting students behaviours appeared to increase gradually from less to more intimate, whereas experienced students appeared to make abrupt transitions between lower- and higher-intimacy behaviours, month-to-month. Demographic, personal, peer, and family variables provided additional information that increased distinction among classes, and explained residual within-class heterogeneity. The probability of being classified as inexperienced was highest for students who were younger, reported fewer sexually experienced friends, and lower parent behavioural control. Students who reported higher parent behavioural control had the highest probability of being classified as experimenting. Relations between trajectories of sexual behaviour intimacy and risk factors (e.g., later pubertal timing, fewer problem behaviours) and protective-enhancing resources (e.g., higher psychosocial maturity, more intimate friendships) varied across classes. This study shows that there are multiple pathways of experimentation with sexual behaviour in adolescence. Results are consistent both with studies that emphasize the potential for sex in adolescence to be high-risk, and with studies and arguments that emphasize the potential for sex in adolescence to play an important preparatory role toward healthy adult sexual functioning. Theoretical arguments and discussion are guided by a person-centered, developmental systems approach.

Growth mixture modeling (GMM) is a useful tool for examining both between- and within-persons change over time and uncovering unobserved heterogeneity in growth trajectories. Importantly, the correct extraction of latent classes and parameter recovery can be dependent upon the type of covariates used. Time-varying covariates (TVCs) can influence class membership but are scarcely included in GMMs as predictors. Other times, TVCs are incorrectly modeled as time-invariant covariates (TICs). Additionally, problematic results can occur with the use of maximum likelihood (ML) estimation in GMMs, including convergence issues and sub-optimal maxima. In such cases, Bayesian estimation may prove to be a useful solution. The present Monte Carlo simulation study aimed to assess class enumeration accuracy and parameter recovery of GMMs with a TVC, particularly when a TVC has been incorrectly specified as a TIC. Both ML estimation and Bayesian estimation were examined. Results indicated that class enumeration indices perform less favorably in the case of TVC misspecification, particularly absolute class enumeration indices. Additionally, in the case of TVC misspecification, parameter bias was found to be greater than the generally accepted cutoff of 10%, particularly for variance estimates. It is recommended that researchers continue to use a variety of class enumeration indices during class enumeration, particularly relative indices. Additionally, researchers should take caution when interpreting variance parameter estimates when the GMM contains a misspecified TVC.

In this thesis, the population pharmacokinetics of telapristone and its active metabolite, CDB-4453 was evaluated using nonlinear mixed effects modeling (NONMEM®). A two-compartment (parent) one compartment (metabolite) mixture model with first order absorption and elimination adequately described the pharmacokinetics of telapristone and CDB-4453. For the Phase I/II pharmacokinetic analysis (effect of renal and hepatic impairment), telapristone was rapidly absorbed with an absorption rate constant (Ka) of 1.26 h-1. Moderate renal impairment resulted in a 74% decrease in Ka. Population estimates for oral clearance (CL/F) for the high and low clearance groups were 11.6 L/h and 3.34 L/h, respectively. Twenty-five percent of the subjects were allocated to the high clearance group. Apparent volume of distribution for the central compartment (V2/F) was 37.4 L, apparent inter-compartmental clearance (Q/F) was 21.9 L/h, and apparent peripheral volume of distribution for the parent (V4/F) was 120 L. The ratio of the fraction of telapristone converted to CDB-4453 to the distribution volume of CDB-4453 (Fmetest) was 0.20/L and apparent clearance of the metabolite (CLM/F) was 2.43 L/h. For the pharmacokinetic analysis evaluating the effect of food; food decreased the Ka of telapristone (Ka for the fed and fasted state was 0.467 and 5.06 h-1, respectively). Population estimates of the high and low CL/F groups were 12.0 L/h and 3.15 L/h, respectively. Thirty-one percent of the subjects were allocated to the high clearance group. V2/F, Q/F and V/4 and Fmetest were 52.8 L, 7.53 L/h, 84.8 L and 0.193/L, respectively. CLM/F was 2.10 L/h. An external validation was performed using the final parameter estimates from the pooled pharmacokinetic analysis (effect of renal and hepatic impairment and the effect of food). From this pharmacokinetic analysis, Ka for the fed and fasted state was 0.299 and 2.35 h-1, respectively. Population estimates for the high and low CL/F groups were 11.6 L/h and 3.22 L/h, respectively. The percentage of subjects allocated to the high clearance group was 29%. V2/F, Q/F, V/4 and Fmetest were 52.8 L, 11.6 L/h and 93.8 L and 0.186/L, respectively. CLM/F was 2.23 L/h. The final model did not meet the requirement for adequate predictability using the external validation dataset. However, the external validation dataset only included samples with limited early time points. Because of the limited sampling times, it is difficult to make a conclusion about the overall adequacy of the model. An external validation dataset with more extensive sampling will be needed in order to better assess the predictability final model. This is the first comprehensive review of the pharmacokinetics of telapristone and CDB-4453.

Multiple facets of perceived value perceptions drive loyalty intentions. However, this value-loyalty link is not uniform for all customers. In fact, the present study identifies three different segments that are internally consistent and stable across different service industries, using two data sets: the wireless telecommunication industry (sample size 1,122) and the financial services industry (sample size 982). Comparing the results of a single-class solution with finite mixture results confirms the existence of unobserved customer segments. The three segments found are characterized as "rationalists", "functionalists" and "value maximizers". These results point the way for value-based segmentation in loyalty initiatives and reflect the importance of a multidimensional conceptualization of perceived value, comprising cognitive and affective components. The present results substantiate the fact that assuming a homogeneous value-loyalty link provides a misleading view of the market. The paper derives implications for marketing research and practice in terms of segmentation, positioning, loyalty programs and strategic alliances. (authors' abstract)

Problems with the current categorical approach to classification used by the Diagnostic and Statistical Manual of Mental Disorders (DSM) have led to proposals that classify the emotional disorders (EDs; anxiety and mood disorders) using a dimensional-categorical system based on shared ED vulnerabilities and phenotypes. Such profile-based approaches have yet to be empirically evaluated, in part because a single multidimensional assessment of shared ED vulnerabilities and phenotypes amenable to profile-based classification has not been developed. The present studies aimed to provide an initial examination of a categorical-dimensional approach to ED classification (Study 1) as well as develop and evaluate a multidimensional self-report assessment of shared ED vulnerabilities and phenotypes (the Multidimensional Emotional Disorder Inventory [MEDI], Study 2). The samples consisted of 1,218 (Study 1) and 227 (Study 2) participants who presented for assessment and treatment at an outpatient ED treatment center. All participants were assessed using a semi-structured ED interview and a set of ED self-report questionnaires. The MEDI was completed only by the participants in Study 2. Study 1 used mixture modeling to identify six unobserved groups (classes) of individuals sharing similar profiles across seven dimensional ED vulnerability and phenotype indicators. The external validity of the profiles was supported when related ED covariates were added to the solution. The incremental validity of the profiles was supported using hierarchical regression models; the profiles accounted for unique variance in ED outcomes beyond DSM diagnoses. In Study 2, exploratory structural equation modeling (ESEM) and confirmatory factor analysis were used to evaluate the factor structure of the MEDI. ESEM supported an eight-factor solution of a 47-item version of the MEDI. Differential magnitude of correlation analyses supported the convergent/discriminant validity of seven of the eight MEDI scales. A five-class (profile) solution, consistent with Study 1, was found when mixture modeling was applied to the MEDI scales. Collectively, the present studies provide compelling evidence in support of the development and utility of a hybrid dimensional-categorical profile approach to emotional disorder classification using multidimensional self-report assessment methods such as the MEDI.

Current manufacturing process controls are principally based only on statistical performance. The next evolution is to make physics based models combined with the state of the art sensors and actuators to control the manufacturing processes. In this paper, metal inert gas welding is used as an example of how the first steps in developing a reliable estimation technique to implement a physics based controller. The weld bead geometry will be the main focus because it is crucial to creating a quality weld. This paper uses an IR camera to generate and evaluate multiple weld bead width estimation techniques and characterizes their corresponding standard deviations. Also a Gaussian Mixture Model (GMM) is used to fit the temperature linescan data to fit an analytical function to the numerical data. The GMM is then used to estimate the weld bead width. Finally, the optimal linescan location is calculated to produce the best possible weld bead estimation. The result is that only one of the estimation techniques actually follows a step input and vi the optimal linescan location is 4 mm from the back of the arc. Furthermore, the GMM provides an excellent fit to the temperature linescan, but does not increase the accuracy of the estimate. / text

Politicians and social marketers considering whether, and how, to implement a national smoking ban in their countries require sound evidence regarding what the causes of support are amongst key stakeholders, how this support will develop over the short to medium term in which they seek to be re-elected, and how support relates to critical outcomes like enforcement. In response to this need, I use structural equation models to develop a model of the antecedents of support, based on theories of self interest and common sense justice, amongst hospitality industry managers. I show that support is determined more by fairness related constructs than self interest constructs, that support for national smoking bans increases consistently over time, and that the initial level of support, and the rate at which support increases, is positively related to subsequent enforcement behaviour by bar managers, in the year after implementation of such a ban, in New Zealand. I use growth mixture modeling to identify two subgroups of bar managers whose support changes at different rates. First, a class of bar managers with a high proportion of smokers who reported fewer instances of respiratory related health problems, showed low initial support, and whose support for the legislation slowly decreased. And second, a class of bar managers comprised of fewer smokers, but reporting more instances of respiratory related health problems. This class began with a high degree support, and steadily increased in support for the national smoking ban. I discuss the implications of these findings for social marketers, health educationalists, and politicians interested in introducing a similar ban in other countries.