Showing papers by "D. Leann Long published in 2014"

A marginalized zero-inflated Poisson regression model with overall exposure effects.

Abstract: The zero-inflated Poisson (ZIP) regression model is often employed in public health research to examine the relationships between exposures of interest and a count outcome exhibiting many zeros, in excess of the amount expected under sampling from a Poisson distribution. The regression coefficients of the ZIP model have latent class interpretations, which correspond to a susceptible subpopulation at risk for the condition with counts generated from a Poisson distribution and a non-susceptible subpopulation that provides the extra or excess zeros. The ZIP model parameters, however, are not well suited for inference targeted at marginal means, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. We develop a marginalized ZIP model approach for independent responses to model the population mean count directly, allowing straightforward inference for overall exposure effects and empirical robust variance estimation for overall log-incidence density ratios. Through simulation studies, the performance of maximum likelihood estimation of the marginalized ZIP model is assessed and compared with other methods of estimating overall exposure effects. The marginalized ZIP model is applied to a recent study of a motivational interviewing-based safer sex counseling intervention, designed to reduce unprotected sexual act counts.

Dental Hygiene Students’ Perceptions of Older Adults

Abstract: Geriatric education is an important component of the dental hygiene curriculum because, in it, students acquire skills and attitudes to help provide quality care to older adults. The purpose of this study was to determine if off-site exposure to nursing home residents with supervised oversight had the potential to improve dental hygiene students’ attitudes toward older adults. Senior dental hygiene students at one school completed a pre-nursing home experience questionnaire. A series of geriatric lectures and discussions, which included discussions about students’ anxieties of working with institutionalized older adults, were held prior to the nursing home experience. The students then participated in two supervised four-hour nursing home experiences, were debriefed after the experiences, and completed a second questionnaire. Of thirty-nine potential participants in the study, thirty-two took part in the pre-nursing home experience questionnaire (82.1 percent). They had a mean split Fabroni score of 34.2 (95 percent confidence interval: 32.2, 36.3). The thirty participants in the post-experience questionnaire (76.9 percent of total) had a mean split score of 32.7 (95 percent confidence interval: 30.1, 35.3). This study failed to reject the null hypothesis of no mean difference between the pre- and post-nursing home experience; however, the post-experience mean score was lower than the pre-nursing home experience mean score, indicating a more positive attitude toward older adults after the experience.

A Marginalized Zero-Inflated Negative Binomial Regression Model with Overall Exposure Effects

Abstract: The zero-inflated negative binomial regression model (ZINB) is often employed in diverse fields such as dentistry, health care utilization, highway safety, and medicine, to examine relationships between exposures of interest and overdispersed count outcomes exhibiting many zeros. The regression coefficients of ZINB have latent class interpretations for a susceptible subpopulation at risk for the disease/condition under study with counts generated from a negative binomial distribution and for a non-susceptible subpopulation that provides only zero counts. The ZINB parameters, however, are not well-suited for estimating overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. In this paper, a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation. Through simulation studies, the performance of MZINB with respect to test size is compared to marginalized zero-inflated Poisson, Poisson, and negative binomial regression. The MZINB model is applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. A Marginalized Zero-Inflated Negative Binomial Regression Model with Overall Exposure Effects John S. Preisser, Kalyan Das, D. Leann Long, John W. Stamm Department of Biostatistics, University of North Carolina at Chapel Hill Department of Statistics, University of Calcutta, Kolkata, India Department of Biostatistics, West Virginia University, Morgantown, WV Department of Dental Ecology, University of North Carolina at Chapel Hill