20 research outputs found

    Topics in Bayesian sample size determination and Bayesian model selection.

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    Includes bibliographical references (p. 84-87).This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the dependence structure, the required sample size will be larger than that assuming independence between the tests. The second topic is also concerned with Bayesian sample size calculation with a misclassified binary response variable. Differing from the first topic, an error-free covariate is added. Simulations demonstrate that choices of prior distributions have a great impact on the resultant sample size. The last topic is about Bayesian variable selection under the multiple regression model. Two competing Bayesian methods are Bayesian model averaging and reversible jump MCMC. It is found that reversible jump MCMC, expected to give better models, does not seem to differ from Bayesian model averaging in examples considered.by Dunlei Cheng.Ph.D

    A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests

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    We develop a Bayesian approach to sample size and power calculations for cross-sectional studies that are designed to evaluate and compare continuous medical tests. For studies that involve one test or two conditionally independent or dependent tests, we present methods that are applicable when the true disease status of sampled individuals will be available and when it will not. Within a hypothesis testing framework, we consider the goal of demonstrating that a medical test has area under the receiver operating characteristic (ROC) curve that exceeds a minimum acceptable level or another relevant threshold, and the goals of establishing the superiority or equivalence of one test relative to another. A Bayesian average power criterion is used to determine a sample size that will yield high posterior probability, on average, of a future study correctly deciding in favor of these goals. The impacts on Bayesian average power of prior distributions, the proportion of diseased subjects in the study, and correlation among tests are investigated through simulation. The computational algorithm we develop involves simulating multiple data sets that are fit with Bayesian models using Gibbs sampling, and is executed by using WinBUGS in tandem with R.

    Resource utilization and treatment costs of patients with severe hemophilia A: Real‐world data from the ATHNdataset

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    Abstract Hemophilia A is characterized by unpredictable spontaneous bleeds and chronic comorbidities. However, limited data exists at the national level into detailed management patterns related to patient clinical characteristics, representative real‐world dosing and treatment frequency, and costs. To assess and characterize the US severe hemophilia A (SHA) population, including subgroups of patients, in terms of clinical and demographic characteristics, healthcare resource utilization received at hemophilia treatment centers (HTCs), and projected annual costs of treatment utilizing data from the ATHNdataset of the American Thrombosis and Hemostasis Network (ATHN). Adult male people with SHA (PwSHA) (FVIII < 1%) were identified in the ATHNdataset between January 2013 and September 2019. This retrospective cohort study described patients’ demographic and clinical characteristics, clinical history, as well as the HTC‐related health resource utilization (HRU), treatment utilization, and projected annual treatment costs of US PwSHA received over the most recent year. Results are reported for the overall population and for three mutually exclusive subpopulations of patients: PwSHA with a history of and/or current inhibitors, PwSHA without a history of inhibitors but with (or a history of) one or more transfusion‐transmitted infections (hepatitis B virus [HBV], hepatitis C virus [HCV], or human immunodeficiency virus [HIV]), and PwSHA without a history of inhibitors or of transfusion‐transmitted infections (HBV, HCV, or HIV). Of the overall PwSHA cohort (N = 3677), there was a high prevalence of HCV (24.1%) and HIV (13.7%), while the prevalence of HBV (4.9%) was lower. Note that 20.5% of PwSHA overall currently or ever had FVIII inhibitors. On average, PwSHA had 2.8 total HTC visits per year, including 0.9 comprehensive care visits, 1.1 telephone contact visits, 0.5 office visits, and 0.1 surgeries or other procedures. However, 23.3% of PwSHA were not seen at an HTC, and 33.8% of PwSHA did not have a comprehensive care visit during their most recent year of data. HTC‐related HRU was similar between the overall cohort and across the patient subpopulations, although PwSHA and inhibitors had more frequent HTC visits (a mean of 3.6 visits annually vs. 2.5–2.8 in the other groups). Using reported treatment frequency and dosing, estimated mean annual hemophilia treatment costs varied by treatment and across the three subpopulations: extended half‐life factor product (893,609–934,301bysubpopulation),standardhalf‐lifefactorproduct(893,609–934,301 by subpopulation), standard half‐life factor product (798,700–930,812), plasma‐derived factor product (613,220–801,061),andnon‐factorproducttreatment(613,220–801,061), and non‐factor product treatment (765,289—833,240). This study summarized recent sociodemographic and clinical characteristics, HTC‐related HRU, and HA treatments and projected costs among adult PwSHA, including among key subpopulations of PwSHA. PwSHA experience substantial clinical and resource burden on a chronic basis, despite the care coordination efforts of ATHN‐affiliated HTCs. These findings motivate further exploration of the drivers of resource utilization, observed differences across subpopulations and other disparities, and ongoing monitoring of clinical and treatment burden in the face of an evolving care landscape
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