An Atypical Presentation of a Rare Disease

A 76-year-old white woman presented for evaluation of asymptomatic skin lesions on her right shin, right buttock, and left arm. All lesions initially underwent slow growth and plateaued and then remained stable in size. A complete review of systems revealed normal results. She had 3 well-demarcated erythematous round plaques ranging from 1.5 to 3 cm, all with a central depression, yellow hue, and prominent telangiectasias (Figs 1 and 2). An excisional biopsy was performed. Histologically, there were palisading granulomas within the papillary and reticular dermis, predominantly composed of a histiocytic cell population with multiple large giant cells (S100-; Fig 3)

A Guide to Functional Communication Training and Autism

Individuals diagnosed with Autism Spectrum Disorder have complex communication needs, and many use challenging behaviors to express themselves. This study focused on the effectiveness of the three-step Functional Communication Training process to decrease self-injurious behaviors, when used with a child with an Autism Spectrum Disorder. After conducting an extensive review of existing studies, the current project focused on areas of weakness in the literature. One case study subject participated in therapy that utilized Functional Communication Training. The participant was taught to functionally communicate via a speech generating device and picture symbols, while the numbers of self-injurious behaviors were recorded. At the end of therapy, this training was effectively shown to increase the usage of functional communication, while simultaneously decreasing the participant’s self-injurious behaviors

Beyond Parameter Estimation: Analysis of the Case-Cohort Design in Cox Models

Cohort studies allow for powerful analysis, but an exposure may be too expensive to measure in the whole cohort. The case-cohort design measures covariates in a random sample (subcohort) of the full cohort, as well as in all cases that emerge, regardless of their initial presence in the subcohort. It is an increasingly popular method, particularly for medical and biological research, due to its efficiency and flexibility. However, the case-cohort design poses a number of challenges for estimation and post-estimation procedures. Cases are over-represented in the dataset, and hence estimation of coefficients in this design requires weighting of observations. This results in a pseudopartial likelihood, and standard post-estimation methods may not be readily transferable to the case-cohort design. This thesis presents theory and simulation studies for application of estimation and post-estimation methods in the case-cohort design. In the majority of extant literature considering methods for the case-cohort design, simulation studies generally consider full cohort sizes, sampling fractions, and case percentages that are dissimilar to those seen in practice. In this thesis the design of the simulation studies aims to provide circumstances which are similar to those encountered when using case-cohort designs in practice. Further, these methods are applied to the InterAct dataset, and practical advice and sample code for STATA is presented. Estimation of Coefficients & Cumulative Baseline Hazard: For estimation of coefficients, Prentice weighting and Barlow weighting are the most commonly used (Sharp et al, 2014). Inverse Probability Weighting (IPW), in this context, refers to methods where the entire case-cohort sample at risk is used in the analysis, as opposed to Prentice and Barlow weighting systems, where cases outside the subcohort sample are only included in risk sets just prior to their time of failure. This thesis assesses bias and precision of Prentice, Barlow and IPW weighting methods in the case-cohort design. Simulation studies show IPW, Prentice and Barlow weighting to have similar low bias. Where case percentage is high, IPW weighting shows an increase in precision over Prentice and Barlow, though this improvement is small. Checks of Model Assumptions: Appropriateness of covariate functional form in the standard Cox model can be assessed graphically by smoothed martingale residuals against various other values, such as time and covariates of interest (Therneau et al, 1990). The over-representation of cases in the case-cohort data, as compared to the full cohort, distorts the properties of such residuals. Methods related to IPW that adapt such plots to the case-cohort design are presented. Detection of non-proportional hazards by use of Schoenfeld residuals, scaled Schoenfeld residuals, and inclusion of time-varying covariates in the model are assessed and compared by simulation studies, finding that where risk set sizes are not overly variable, all three methods are appropriate for use in the case-cohort design, with similar power. Where case-cohort risk set sizes are more variable, methods based on Schoenfeld residuals and scaled Schoenfeld residuals show high Type 1 error rate. Model Comparison & Variable Selection: The methods of Lumley & Scott (2013, 2015) for modification of the Likelihood Ratio test (dLR), AIC (dAIC) and BIC (dBIC) in complex survey sampling are applied to case-cohort data and assessed in simulation studies. In the absence of sparse data, dLR is found to have similar power to robust Wald tests, with Type 1 error rate approximately 5%. In the presence of sparse data, the dLR is superior to robust Wald tests. In the absence of sparse data dBIC shows little difference from the naieve use of the pseudo-log-likelihood in the standard BIC formula (pBIC). In the presence of sparse data dBIC shows reduced power to select the true model, and pBIC is superior. dAIC shows improvement in power to select the true model over naieve methods. Where subcohort size and number of cases is not overly small, loss of power from the full cohort for dAIC, dBIC and pBIC is not substantial.The EPIC-InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). I thank all participants and staff for their contribution to this study. I thank the EPIC-InterAct PI, management team and wider consortium for their permission to use the data, and Nicola Kerrison (MRC Epidemiology Unit, University of Cambridge) for preparing the dataset which I used in Chapters 2 and 7. I acknowledge personal financial support from the UK Medical Research Council and St John's College, Cambridge

Local Labor Market Conditions and the Jobless Poor: How Much Does Local Job Growth Help in Rural Areas?

The employment outcomes of a group of jobless poor Oregonians are tracked in order to analyze the relative importance of local labor market conditions on their employment outcomes. Local job growth increases the probability that a jobless poor adult will get a job and shortens the length of time until she finds a job. After accounting for both the effects of personal demographic characteristics and local job growth, there is little evidence that the probability of employment or the duration of joblessness differs in rural compared with urban areas.employment, local labor markets, rural labor markets, rural poverty, unemployment, welfare reform, Labor and Human Capital,

EMPLOYMENT OUTCOMES FOR LOW-INCOME ADULTS IN RURAL AND URBAN LABOR MARKETS

This study analyzes the impact of local labor market conditions on the probability of employment and duration of employment for low-income adults in Oregon. We find that economic conditions (lower employment growth and higher unemployment rates) help to explain the less successful employment outcomes for low-income adults in non-metro areas.rural labor markets, employment, low-income workers, Labor and Human Capital,