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MUSC Department of Public Health Sciences Working Papers

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1.
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Measuring the Impact of Biostatistical Methods on General Medical Research
Measuring the Impact of Biostatistical Methods on General Medical Research Abstract: Background: Novel statistical methods are constantly being developed within the context of biomedical research; however, the rate of diffusion of this knowledge into the field of general / internal medicine is unclear. This study highlights the statistical journal articles, the statistical journals, and the statistical methods that appear to be having the most direct impact on research in the field of general / internal medicine. Methods: Descriptive techniques, including analyses of articles’ keywords and controlled vocabulary terms, were used to characterize the articles published in statistics and probability journals that were subsequently referenced within general / internal medicine journal articles during a recent 10-year period (2000-2009). Results: From the 45 statistics and probability journals of interest, a total of 597 unique articles were identified as being cited by 900 (out of a total of about 10,501) unique general / internal medicine journal articles. The most frequently cited statistical topics included general/other statistical methods, followed by epidemiologic methods, randomized trials, generalized linear models, meta-analysis, and missing data. Conclusion: As statisticians continue to develop and refine techniques, the promotion and adoption of these methods should also be addressed so that their efforts spent in developing the methods are not done in vain.
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Evaluation of Bayesian spatial-temporal latent models in small area health data
Evaluation of Bayesian spatial-temporal latent models in small area health data Abstract: Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in spatial health data have locally different patterns. In such cases, latent modeling is useful in the disaggregation of risk profiles. In particular, spatial-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. Mixture models are assumed as they have various weight structures and considered in two situations: the number of underlying components is known or unknown. In this paper, we compare spatial-temporal mixture models with different weight structures in both situations. For comparison, we propose a set of spatial cluster detection diagnostics which are based on the posterior distribution of weights. We also develop new accuracy measures to assess the recovery of true relative risk. Based on the simulation study, we examine the performance of various spatial-temporal mixture models in terms of proposed methods and goodness-of-fit measures. We examine two real data sets: low birth weight data and chronic obstructive pulmonary disease data.
3.
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Prospective surveillance of multivariate spatial disease data
Prospective surveillance of multivariate spatial disease data Abstract: Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this paper, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. Keywords: disease surveillance; multiple diseases; Shared component model; conditional predictive ordinate
4.
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Bayesian Point Event Modeling in Spatial and Environmental Epidemiology: a review
Bayesian Point Event Modeling in Spatial and Environmental Epidemiology: a review Abstract: This paper reviews the current state of point event modeling in spatial epidemiology from a Bayesian perspective. Point event (or case event) data arise when geo-coded addresses of disease events are available. Often this level of spatial resolution would not be accessible due to medical confidentiality constraints. However, for the examination of small spatial scales it is important to be capable of examining point process data directly. Models for such data are usually formulated based on point process theory. In addition, special conditioning arguments can lead to simpler Bernoulli likelihoods and logistic spatial models. Goodness-of-fit diagnostics and Bayesian residuals are also considered. Applications within putative health hazard risk assessment, cluster detection, and linkage to environmental risk fields (misalignment) are considered.
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Bayesian 2-stage space-time mixture modeling with spatial misalignment of the exposure in small area
Bayesian 2-stage space-time mixture modeling with spatial misalignment of the exposure in small area health data Abstract: We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008. Key words: Space-time mixture model; air pollution; covariate adjustment; asthma; Bayesian modeling
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A likelihood-based approach for computing the operating characteristics of the standard phase I clinical
A likelihood-based approach for computing the operating characteristics of the standard phase I clinical trial design Keywords: phase I clinical trials, standard algorithm, likelihood method, evidential paradigm. Abstract: In phase I clinical trials, the standard ‘3+3’ design has passed the test of time and survived various sample size adjustments, or other dose-escalation dynamics. The objective of this study is to provide a probabilistic support for analyzing the heuristic performance of the ‘3+3’ design. Our likelihood method is based on the evidential paradigm that uses the likelihood ratio to measure the strength of statistical evidence for one simple hypothesis over the other. We compute the operating characteristics and compare the behavior of the standard algorithm under different hypotheses, levels of evidence, and true (or best guessed) toxicity rates. Given observed toxicities per dose level, the likelihood-ratio is evaluated according to a certain k threshold (level of evidence). Under an assumed true toxicity scenario the following statistical characteristics are computed and compared: i) probability of weak evidence, ii) probability of favoring under (analogous to 1-α), iii) probability of favoring under (analogous to 1-β). This likelihood method allows consistent inferences to be made and evidence to be quantified regardless of cohort size. Moreover, this approach can be extended and used in phase I designs for identifying the highest acceptably safe dose and is akin to the sequential probability ratio test.
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A likelihood-based approach to early stopping in single arm phase II clinical trials
A likelihood-based approach to early stopping in single arm phase II clinical trials Phase II studies in oncology have evolved over the previous several decades. Currently, the number of drugs in phase II development has increased, and patient eligibility has narrowed due to targeted agents, competing trials and curative therapies in the first-line setting. As a result of these changes, more attention needs to be focused toward conducting more efficient phase II trials. Given the increased difficulty in accruing patients to phase II studies and the ethical concern of treating patients with agents that are ineffective, there is significant motivation to stop a single arm trial early when the investigational agent shows evidence of a low response rate.
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Scoring of Scales in the Statewide Investigation of Traumatic Brain Injury in Prison Study
Scoring of Scales in the Statewide Investigation of Traumatic Brain Injury in Prison Study The Statewide Investigation of Traumatic Brain Injury in Prisons (SITBIP) is a Centers for Disease Control and Prevention funded research project with three specific aims. The primary aim is to examine the relationship of recidivism and traumatic brain injury (TBI) among a population-based sample of inmates being released from South Carolina’s state prisons. The second aim is to estimate the prevalence of TBI, substance abuse, and violence in this sample and the third aim is to examine the validity of inmate report of TBI against SC hospital records. In order to fulfill these aims, 636 inmates in the SC Department of Corrections were interviewed. The interview contains numerous scales, many are complete scales that have been previously validated and published, some are parts of such scales, and some are original questions created specifically for this study. This paper’s purpose is to detail the scoring of these scales and questions, especially to inform readers of details unique to this sample and study. The questions used in the interview are in the SITBIP Data Codebook accessible at http://people.musc.edu/~pickelse/TBI_Prison_Study/SITBIP-Data_Codebook.pdf. It is very important for the reader to download the file and review the entire questionnaire.