Article
A bayesian twopart latent class model for longitudinal medical expenditure data: assessing the impact of mental health and substance abuse parity.
Nicholas School of the Environment, Duke University, Durham, North Carolina 27708, USA.
Biometrics (Impact Factor: 1.52). 03/2011; 67(1):2809. DOI: 10.1111/j.15410420.2010.01439.x Source: PubMed

Article: A Spatial Poisson Hurdle Model for Exploring Geographic Variation in Emergency Department Visits.
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ABSTRACT: We develop a spatial Poisson hurdle model to explore geographic variation in emergency department (ED) visits while accounting for zero inflation. The model consists of two components: a Bernoulli component that models the probability of any ED use (i.e., at least one ED visit per year), and a truncated Poisson component that models the number of ED visits given use. Together, these components address both the abundance of zeros and the rightskewed nature of the nonzero counts. The model has a hierarchical structure that incorporates patient and arealevel covariates, as well as spatially correlated random effects for each areal unit. Because regions with high rates of ED use are likely to have high expected counts among users, we model the spatial random effects via a bivariate conditionally autoregressive (CAR) prior, which introduces dependence between the components and provides spatial smoothing and sharing of information across neighboring regions. Using a simulation study, we show that modeling the betweencomponent correlation reduces bias in parameter estimates. We adopt a Bayesian estimation approach, and the model can be fit using standard Bayesian software. We apply the model to a study of patient and neighborhood factors influencing emergency department use in Durham County, North Carolina.Journal of the Royal Statistical Society Series A (Statistics in Society) 02/2013; 176(2):389413. · 1.57 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: There is an urgent need for an evidence base to guide care for patients with multiple chronic medical conditions (MCC). Comparative effectiveness research (CER) has been touted as 1 solution to generating such evidence. However, the majority of CER topics and methods are designed to generate evidence applicable to single diseases. Generating evidence to guide the care of MCC populations requires thoughtful, and often alternative, approaches to using the existing armamentarium of CER methods. To initiate a dialog about appropriate methods for CER in MCC populations, we discuss advantages and disadvantages of experimental and quasiexperimental study designs for CER in MCC populations, estimating heterogeneity of treatment effects, developing meaningful outcome measures, and aligning morbidity measurement with relevant outcomes. Through an engaged dialog with clinicians, methodologists, and patients, evidence about strengths and limitations of alternative approaches, recommendations about preferred methods for CER in MCC can be developed to ensure that knowledge gaps are filled by valid evidence.Medical care 03/2014; 52 Suppl 3:S2330. · 2.94 Impact Factor 
Conference Paper: Bayesian Latent Class Models in Veterinary and Human Epidemiology
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ABSTRACT: Latent class models have been widely used to evaluate the performance of veterinary and human diagnostic tests, in the absence of a gold standard. In this work, we explore Bayesian latent class models, with and without restrictions, in different populations. We previously reported the Bayesian latent class analysis of the malaria dataset (n =3317) and now we apply a similar approach to a smaller dataset in the context of canine dirofilariasis (n =308). Although the last study presents a small sample size, it was important to obtain the first estimates of the prevalence (and performance measures of diagnostic techniques) of canine dirofilariasis in three districts of Portugal, taking into account the relationship with human dirofilariasis.46th Scientific Meeting of the Italian Statistical Society, Sapienza University of Rome  Faculty of Economics, June 2022, 2012, Rome, Italy; 06/2012
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