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Relevant bibliographies by topics / Biostatistics and Epidemiology

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Academic literature on the topic 'Biostatistics and Epidemiology'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Biostatistics and Epidemiology"

1

Gill, David. "Biostatistics and epidemiology." Journal of Psychosomatic Research 41, no. 4 (1996): 385. http://dx.doi.org/10.1016/s0022-3999(96)00094-3.

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Minassian, Vatche A., and Mitesh Parekh. "Epidemiology and Biostatistics." Journal of Pelvic Medicine and Surgery 13, no. 1 (2007): 1–12. http://dx.doi.org/10.1097/01.spv.0000256865.80242.8d.

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Lash, T. L. "Epidemiology and Biostatistics." American Journal of Epidemiology 176, no. 7 (2012): 656–57. http://dx.doi.org/10.1093/aje/kws339.

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Kocher, Mininder S., and David Zurakowski. "Clinical Epidemiology and Biostatistics." Journal of Bone & Joint Surgery 86, no. 3 (2004): 607–20. http://dx.doi.org/10.2106/00004623-200403000-00024.

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Goldberg, M. F. "Ophthalmic Epidemiology and Biostatistics." Archives of Ophthalmology 106, no. 6 (1988): 737–38. http://dx.doi.org/10.1001/archopht.1988.01060130807026.

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Burden, A. C. "Clinical Epidemiology and Biostatistics." Postgraduate Medical Journal 66, no. 775 (1990): 415. http://dx.doi.org/10.1136/pgmj.66.775.415.

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Chu, Joseph. "Active learning in epidemiology and biostatistics." Teaching and Learning in Medicine 6, no. 3 (1994): 191–93. http://dx.doi.org/10.1080/10401339409539674.

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Gurka, Matthew J. "Basic Concepts in Biostatistics and Epidemiology." Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, no. 2 (2008): 506–7. http://dx.doi.org/10.1111/j.1467-985x.2007.00528_6.x.

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Curnow, R. N., N. E. Morton, D. C. Rao, and J. M. Lalouel. "Methods in Genetic Epidemiology (Contributions to Epidemiology and Biostatistics)." Biometrics 41, no. 2 (1985): 595. http://dx.doi.org/10.2307/2530890.

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Hilden, J. "Effect measures in biostatistics." Journal of Clinical Epidemiology 56, no. 4 (2003): 391–92. http://dx.doi.org/10.1016/s0895-4356(02)00540-1.

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Dissertations / Theses on the topic "Biostatistics and Epidemiology"

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Matoušů, Barbora. "Incidenční a prevalnční onemocnění v okrsní nemocnici v průběhu 3 let." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-3969.

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Branches of science such as epidemiology, clinical epidemiology, statistics and various statistical methods are by ground for activity of epidemiological-manager indicators. Incidence and prevalence are numbered among epidemiological-manager indicators. In condition of hospital Pelhřimov are these indicators written, analyzed and predicated for department of hospitalized in years 2005 -- 2007. At the same time is appraised use of capacities department of hospitalized, is made analyse and estimation of most often treated diagnosis and is watched progress their average time of treatment. The watching indicators are by one of series others records for manager decision-making by control of hospital.

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Benedetti, Andrea. "Generalized models in epidemiology research." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=84472.

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Traditionally, epidemiologists have used methods that categorize or assume a linear or log-linear form to model dose-response associations between continuous independent variables and binary or continuous outcomes. Recent advances in both statistical methodology and computing resources have made it possible to model relationships of greater complexity. Generalized additive models (GAMs) are a flexible nonparametric modelling tool that allows the user to model a variety of non-linear dose-response curves without imposing a priori assumptions about the functional form of the relationship. In GAMs, the extent of smoothing is controlled by the user-defined degrees of freedom (df). GAMs are generally used to: (i) suggest the parametric functional form for the association of interest; (ii) model the main effect nonparametrically; and (iii) control confounding by continuous covariates. By way of a series of simulation studies, this thesis addresses several unresolved methodological issues involving all three of these uses. Although GAMs have been used to detect and estimate thresholds in the association of interest, the methods have been mostly subjective or ad hoc, and the statistical properties have not been evaluated for the most part. In the first simulation study, a formal approach to the use of GAMs for this purpose is suggested and compared with simpler approaches. When GAMs are used to estimate the effect of the primary exposure of interest different approaches to determining the amount of smoothing are employed. In the second simulation study, the impact on statistical inference of various a priori and automatic df-selection strategies is investigated and a method to correct the type I error is introduced and evaluated.<br>In the final simulation study, parametric multiple logistic regression was compared with its nonparametric GAM extension in their ability to control for a continuous confounding variable and several issues related to the implementation of GAMs in this context are investigated.<br>The results of these simulations will help researchers make optimal use of the potential advantages of flexible assumption-free modelling.

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Lin, Xinyi (Cindy). "Statistical Methods for High-Dimensional Data in Genetic Epidemiology." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11326.

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Recent technological advancements have enabled us to collect an unprecedented amount of genetic epidemiological data. The overarching goal of these genetic epidemiology studies is to uncover the underlying biological mechanisms so that improved strategies for disease prevention and management can be developed. To efficiently analyze and interpret high-dimensional biological data, it is imperative to develop novel statistical methods as conventional statistical methods are generally not applicable or are inefficient. In this dissertation, we introduce three novel, powerful and computationally efficient kernel machine set-based association tests for analyzing high-throughput genetic epidemiological data. In the first chapter, we construct a test for identifying common genetic variants that are predictive of a time-to-event outcome. In the second chapter, we develop a test for identifying gene-environment interactions for common genetic variants. In the third chapter, we propose a test for identifying gene-environment interactions for rare genetic variants.

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Scott, Susan C. (Susan Catherine). "An examination of the proportionality of hazards in epidemiology /." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=56966.

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Additive and multiplicative statistical models in epidemiology invoke different assumptions regarding exposure-outcome relationships. While either may seem to provide an adequate fit in small data sets, a comparative parameter based on the correct set of assumptions will be more stable when applied in other contexts. Important implications of model selection and the scant literature on theoretical reasoning are presented. Given a lack of biological knowledge, a body of corroborative empirical knowledge would be helpful in choosing between statistical models. Traditionally, the fit of a multiplicative model is evaluated by a $ chi sp2$ test of homogeneity. However, this is a test rather than a measure and is sample-size dependent. In this thesis, the development of a new measure of heterogeneity of rate ratios, phi prime, is presented and applied to stomach cancer registry data. Results suggest homogeneity when comparing regions within countries, but some heterogeneity between continents.

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Quinn, Megan, and M. Baker. "Public Health Opportunities in Northeast Tennessee." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etsu-works/6793.

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Quinn, Megan, Julie Obenauer, and Andrew Curtis. "Spatial Analysis of Mosquito-Borne Illness Prevalence in Nueva Vida, Nicaragua." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/6795.

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Recently mosquito-borne illnesses (MBI) such as Chikungunya (CHIKV) and Zika virus (ZV) proved to be of major public health importance in the Western Hemisphere. The purpose of this study was to determine the prevalence of selfreported MBI by geographic region in Nueva Vida, Nicaragua and gain insight into the number of community members seeking medical attention for MBI. This study involved a door-to-door survey of 1015 households (N=5778) in Nueva Vida, a community within Ciudad Sandino, Nicaragua and capture of spatial video of the community to determine areas of potential high risk for MBI outbreaks. The community is divided into sections, or etapas, and surveys were completed for each of the five etapas. The head of the household completed the survey and a response rate of approximately 82% was achieved for Nueva Vida. The number of selfreported MBI totaled 1,730 individuals or 29.9% of the surveyed population. Of the self-reported illnesses, 2.4 % were malaria (N=42), 5.0% were dengue (N=87), 91.9 % were chikungunya (N= 1589), and 0.7% were Zika (N =12). For all MBI 21.25 % of the population visited a doctor (N=1228) and 2.71 % visited a hospital (N= 157). Environmental risk factors such as standing water and trash were documented through spatial video. MBI and environmental risk factors were mapped using geospatial analysis. MBI varied by location with etapas 2 and 3 having the highest prevalence MBI (30% and 26%, respectively). Maps provided a visualization of MBI prevalence and environmental risk factors, illustrating sections of etapas 2, 3, and 4 as the highest risk zones in the community for potential MBI outbreaks, such as ZV or CHIKV. The data demonstrates that MBI have a great potential to impact the Nueva Vida community. Results suggest a need for educational programs onMBI transmission and targeted prevention activities in high-risk areas, specifically with the potential spread of ZV in a community where CHIKV prevalence was previously high.

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Intagliata, Nicole, and Megan Quinn. "Healthy Homes: Warmer, Safer, Drier Equals Healthier." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etsu-works/6801.

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Housing and health care are more closely related than ever before. To address this issue head-on, Ballad Health, Appalachia Service Project, and East Tennessee State University's College of Public Health created a regional partnership to research and provide a more holistic and effective approach to healthcare by meeting the housing needs of families in northeast Tennessee and southwest Virginia. Presenters will be Nicole Intagliata, Director of Programs at ASP, and Megan Quinn, Associate Professor at East Tennessee State University. (Megan is replacing Paula Masters from Ballad Health who was originally scheduled to speak.) The session will conclude with a short presentation by Nick Oliver of Hinton Rural Life Center, winner of the Best Practices Award for Home Repair Practices. He'll share about their Healthy Homes Checklist, work with their local Red Cross chapter to check for proper smoke detector installation, and partnership with health care providers to help improve conditions at homes that are linked to health concerns.

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Quinn, Megan, Timothy Joyner, and Julie Obenauer. "The Importance of Human Population Characteristics in Modeling Mosquito Vectors: A Comparative Analysis of Model Components." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/6803.

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The current Zika virus epidemic in the Western hemisphere is representative of the confluence of global climate change and infectious disease expansion, and vector modeling represents a pertinent and timely method to analyze the environment associated with Zika-carrying mosquitoes. Among many mosquito species distribution models, there are varying opinions on which variables are most predictive and, consequently, should be included in modeling efforts. While climate variables (e.g.,mean temperature, mean precipitation) are routinely included, some argue that human population dynamics, in the form of population density and socioeconomic status, should also be included. This project aimed to test the importance of including human population characteristics by modelling the Zika virus vector Aedes aegypti in the Southeastern United States with climate variables, population density, and poverty characteristics. A. aegypti occurrences, global climate data, and population characteristics were obtained from publicly available sources and sampled at a resolution of 2.5 arc-minutes. Data pre and post-processing was completed in ArcMap 10.3 and models were created in Maxent v.3.3.3k. Four models were developed for this project: a climate-only model, a climate and population density model, a climate and poverty model, and a combined model with climate, population density, and poverty. Models were evaluated by comparing test and training area under the curve metrics, omission and commission errors, and variable jackknifing results. The climate-only model performed poorly compared to models with human population characteristics. The combined model was the best fit, though the model with climate and population density had a lower commission rate (21.0% and 20.6%, respectively). Jackknife results for the full model showed that population density was the most significant contributor to the model. This research indicates that more consideration should be given to human population characteristics when modelling mosquito habitats.

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Quinn, Megan, H. Owens, and Elaine Loudermilk. "Effects of Physical and Sexual Abuse on Mental Health." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/6804.

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Quinn, Megan, and B. O'Connell. "Assessing Pap Smear Utilization in Ciudad Sandino, Nicaragua: An Example of an Academic, Professional, and Lay Health Worker Partnership." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/6807.

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Books on the topic "Biostatistics and Epidemiology"

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M, Linardakis Nikos, ed. Biostatistics and epidemiology. McGraw-Hill, Health Professions Division, 1998.

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Wassertheil-Smoller, Sylvia, and Jordan Smoller. Biostatistics and Epidemiology. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2134-8.

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Kestenbaum, Bryan. Epidemiology and Biostatistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97433-0.

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Wassertheil-Smoller, Sylvia. Biostatistics and Epidemiology. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4757-3887-2.

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Kestenbaum, Bryan. Epidemiology and Biostatistics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-88433-2.

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Wassertheil-Smoller, Sylvia. Biostatistics and Epidemiology. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2424-0.

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Kestenbaum, Bryan. Epidemiology and Biostatistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-96644-1.

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Knapp, Rebecca Grant. Clinical epidemiology and biostatistics. Williams & Wilkins, 1992.

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Kramer, Michael S. Clinical Epidemiology and Biostatistics. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-61372-2.

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G, Elmore Joann, and Katz David L. MD, eds. Epidemiology, biostatistics, and preventive medicine. Saunders, 1996.

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Book chapters on the topic "Biostatistics and Epidemiology"

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Andresen, Elena M. "Epidemiology and Biostatistics." In Public Health Perspectives on Disability. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7341-2_2.

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Andresen, Elena M., and Erin D. Bouldin. "Epidemiology and Biostatistics." In Public Health Perspectives on Disability. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0888-3_2.

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Garhöfer, Gerhard, and Leopold Schmetterer. "Epidemiology and Biostatistics." In Clinical Pharmacology: Current Topics and Case Studies. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27347-1_14.

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Wassertheil-Smoller, Sylvia. "Mostly about Epidemiology." In Biostatistics and Epidemiology. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2424-0_4.

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Wassertheil-Smoller, Sylvia, and Jordan Smoller. "Mostly About Epidemiology." In Biostatistics and Epidemiology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2134-8_4.

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Wassertheil-Smoller, Sylvia. "Mostly about Epidemiology." In Biostatistics and Epidemiology. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4757-3887-2_4.

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Wassertheil-Smoller, Sylvia. "The Scientific Method." In Biostatistics and Epidemiology. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2424-0_1.

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Wassertheil-Smoller, Sylvia. "A Little Bit of Probability." In Biostatistics and Epidemiology. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2424-0_2.

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Wassertheil-Smoller, Sylvia. "Mostly about Statistics." In Biostatistics and Epidemiology. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2424-0_3.

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Wassertheil-Smoller, Sylvia. "Mostly about Screening." In Biostatistics and Epidemiology. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2424-0_5.

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Conference papers on the topic "Biostatistics and Epidemiology"

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Zheng, Qi. "Improving the Teaching of Biostatistics in an Online Master Degree Program in Epidemiology." In ICDEL 2020: 2020 the 5th International Conference on Distance Education and Learning. ACM, 2020. http://dx.doi.org/10.1145/3402569.3402582.

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Prasetya, Kharisma, Dani Adhipta, Sunu Wibirama, and Adhistya Erna Permanasari. "Information System Platform for Outbreak Investigation: A Case Study in Department of Biostatistic, Epidemiology and Public Health, Gadjah Mada University." In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2018. http://dx.doi.org/10.1109/iciteed.2018.8534885.

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