8 research outputs found

    A Likelihood Based Approach to the Assessment of Large Sample Convergence and Model Based Clustering.

    Get PDF
    The likelihood is a function of model parameter(s) and data using a pre-defined probability density function (pdf). Thus, the likelihood can be viewed as model-data combination that can be utilized to address questions of interest. The relative likelihood function is the likelihood function scaled by its mode so as to have its maximum at one. Unlike likelihood functions, relative likelihood functions have attracted little attention and use by statisticians. The proposed dissertation work explores the properties and applications of relative likelihood functions in examining the large-sample convergence properties of maximum likelihood estimator (MLE) and in relation to clustering. The dissertation consists of three chapters. The first chapter presents a simulation based approach to examine the relationship between sample size and the asymptotic behavior of the MLE. The convergence of the observed relative likelihood function (RLF) to the asymptotic relative likelihood function (RLF) is assessed for different sample sizes using two measures of convergence; difference in areas and dissimilarity in shape. The proposed approach has been applied to data from the literature as well as to data simulated from different exponential family distributions. The second chapter proposes a novel clustering approach based on the observed RLFs. Observations in the dataset are assumed to follow a known distribution and observed RLFs are obtained. The observed RLFs are further scaled by the inverse of the asymptotic variation (Fisher Information) evaluated at the mode of the likelihood functions. The weighted RLFs reflect information based similarity among observations in the data. A data matrix is then developed by evaluating the weighted RLFs at different values in the parameter space. The data matrix allows for direct application of standard clustering algorithms such as k-means algorithm. This clustering approach was applied to simulated dataset based on real data and to datasets simulated from known distributions. The third chapter examines the proposed RLF based clustering approach to a publicly available gene expression dataset consisting of 70 gene expression profiles used to classify patients into prognostic groups. The agreement between the RLF clustering results and previous classification is also presented. The clusters obtained are also examined in relation to differences in two clinical features – time to overall survival; and time to metastases

    A Geo-Stratified Analysis of Associations Between Socio-Economic Factors and Diabetes Risk

    Get PDF
    Introduction. In 2019, diabetes was the seventh leading cause of death in the United States. The association between diabetes risk and socio-economic factors in the United States has been examined primarily at the national level; little is known about this association at the regional level. This study examines and compares the association between diabetes risk and previously established socio-economic factors across four geographic regions (South, Midwest, West, and Northwest). Methods. We analyzed the 2014 Behavioral Risk Factor Surveillance System (BRFSS) data stratified by four geographic regions of the United States. The risk estimates of diabetes associated with previously established socio-economic factors as well as diabetes prevalence were compared across four geographic regions. Results. There was marked variation in association between diabetes risk and previously established risk factors across the four geographic regions. In the South, rural residency was associated with increased diabetes risk, whereas in the other geographic regions rural residency had a protective effect. In the South, the diabetes risk for males was 22% higher compared to females in the South, whereas the risk for males was 41% higher than females in the Northeast. Independently, age had the strongest discriminative ability to distinguish between a person with diabetes and a person without diabetes, whereas ethnicity, race and sex had the weakest discriminative abilities. Conclusions. Our findings suggest a higher prevalence of diabetes by race/ethnicity (Non-Hispanic Black and Hispanic) and income across all four regions. While rural residency is highest in the South, but protective in other regions. Overall, we found age and income provide the highest predictive ability for diabetes risk.  This study highlights differences in diabetes prevalence in association between previously established socio-economic variables and diabetes risk across four geographic regions. These findings could help public health professionals and policy makers in understanding the dynamic relationship between diabetes and risk factors at the regional level

    Active vs Traditional Methods of Recruiting Children for a Clinical Trial in Rural Primary Care Clinics: A Cluster-Randomized Clinical Trial

    Get PDF
    Importance: To our knowledge, there are no published randomized clinical trials of recruitment strategies. Rigorously evaluated successful recruitment strategies for children are needed. Objective: To evaluate the feasibility of 2 recruitment methods for enrolling rural children through primary care clinics to assess whether either or both methods are sufficiently effective for enrolling participants into a clinical trial of a behavioral telehealth intervention for children with overweight or obesity. Design, setting, and participants: This cluster-randomized clinical trial of 2 recruitment methods was conducted at 4 primary care clinics in 4 separate states. Each clinic used both recruitment methods in random order. Clinic eligibility criteria included at least 40% pediatric patients with Medicaid coverage and at least 100 potential participants. Eligibility criteria for children included a rural home address, age 6 to 11 years, and body mass index at or above the 85th percentile. Recruitment began February 3, 2020, and randomization of participants occurred on August 17, 2020. Data were analyzed from October 3, 2021, to April 21, 2022. Interventions: Two recruitment methods were assessed: the active method, for which a list of potential participants seen within the past year at each clinic was generated through the electronic health record and consecutively approached by research staff based on visit date to the clinic, and the traditional method, for which recruitment included posters, flyers, social media, and press release. Clinics were randomized to the order in which the 2 methods were implemented in 4-week periods, followed by a 4-week catch-up period using the method found most effective in previous periods. Main outcomes and measures: For each recruitment method, the number and proportion of randomized children among those who were approached was calculated. Results: A total of 104 participants were randomized (58 girls [55.8%]; mean age, 9.3 [95% CI, 9.0-9.6] years). Using the active method, 535 child-parent dyads were approached and 99 (18.5% [95% CI, 15.3%-22.1%]) were randomized. Using the traditional method, 23 caregivers expressed interest, and 5 (21.7% [95% CI, 7.5%-43.7%]) were randomized. All sites reached full enrollment using the active method and no sites achieved full enrollment using the traditional method. Mean time to full enrollment was 26.3 (range, 21.0-31.0) days. Conclusions and relevance: This study supports the use of the active approach with local primary care clinics to recruit children with overweight and obesity from rural communities into clinical trials

    Safety and Tolerability of Carboplatin and Paclitaxel in Cancer Patients with HIV (AMC-078), an AIDS Malignancy Consortium (AMC) Study

    No full text
    Patients with cancer and HIV are an underserved population. Paclitaxel and carboplatin is an active regimen against a variety of solid tumors, including several seen in excess in patients with HIV infection. This pilot trial evaluated the safety of full dose paclitaxel and carboplatin in people living with HIV and cancer
    corecore