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Full-Text Articles in Medicine and Health Sciences
Bios 6332 - Experimental Design In Biostat, Jingjing Yin
Bios 6332 - Experimental Design In Biostat, Jingjing Yin
Jiann-Ping Hsu College of Public Health Syllabi
This course introduces the methods for analyzing biomedical and health related data using ANOVA methods. The course will involve one-way and two-way ANOVA with fixed or/and random effects and sample size/power calculation. And Logistic and Poisson regression models will also be addressed. The students will learn how to apply SAS procedures PROC POWER, PROC GLM, PROC MIXED, PROC GENMOD and PROC GLIMMIX and interpret the results of analysis. Emphasis will also be placed on the development of critical thinking skills.
Bios 7131 - Survival Analysis, Lili Yu
Bios 7131 - Survival Analysis, Lili Yu
Jiann-Ping Hsu College of Public Health Syllabi
This course introduces statistical methods for analyzing data collected on the time to an event, referred to as survival data, in medical research and other health-related fields. Emphasis will be placed on the application of the methodology and computational aspects rather than theory. The students will learn how to apply SAS procedures to data and interpret the results.
Bios 9134 - Stochastic Processes For Systems Biology, Xinyan Zhang
Bios 9134 - Stochastic Processes For Systems Biology, Xinyan Zhang
Jiann-Ping Hsu College of Public Health Syllabi
This course provides the student with an introduction to stochastic processes with emphasis on Markov chains, The Poisson Process, Brownian Motion and other continuous time processes. The theory developed will be used to model and simulate complex biochemical reaction networks and perform network inference given data from the stochastic trajectory of a biological process, typically arising from microarray or next generation sequencing experiments.
Bios 9433 - Analysis With Missing & Miss-Specified Data, Haresh Rochani
Bios 9433 - Analysis With Missing & Miss-Specified Data, Haresh Rochani
Jiann-Ping Hsu College of Public Health Syllabi
This course is designed to provide the student with the basics of methods for analyzing data with missing data and misspecified data. This course will cover the following topics: missing data in experiments, complete case analysis, weighted complete case analysis, available case analysis, single imputation methods such as mean, regression, last value varied forward, hot deck imputation, cold deck imputation, Bayes Imputation, Multiple imputation, and non-ignorable missing data models. Prerequisite: A minimum grade of “B” in BIOS 9131. Co-requisite: BIOS 9231.
Pubh 3231 - Epidemiology And Biostatistics, Stuart H. Tedders
Pubh 3231 - Epidemiology And Biostatistics, Stuart H. Tedders
Jiann-Ping Hsu College of Public Health Syllabi
This course introduces the student to the principles and practice of epidemiology and vital statistics. Students will be exposed to the historical development of epidemiology, concepts of causality, definitions of health and disease, and sources of community health data. Current principles and practices in the cause, prevention and control of diseases in various community settings will be emphasized.
Chbe 9230 - Community-Based Public Health Program Planning And Evaluation, Andrew Hansen
Chbe 9230 - Community-Based Public Health Program Planning And Evaluation, Andrew Hansen
Jiann-Ping Hsu College of Public Health Syllabi
This course introduces students to the theory and application of community-based program planning and evaluation. Concepts in community assessment, organization, and mobilization for the purposes of addressing identified public health concerns will serve as the foundation for the public health planning process. Appropriate techniques of partnership building, planning strategies, data collection, data analysis, and evidence-based decision-making will also be introduced.
Pubh 6541 - Biostatistics, Lili Yu
Pubh 6541 - Biostatistics, Lili Yu
Jiann-Ping Hsu College of Public Health Syllabi
This 4 credit course examines statistics in public health and related sciences, including sampling, probability, basic discrete and continuous distributions, descriptive statistics, hypothesis testing, confidence intervals, categorical data analysis, regression, and correlation. Emphasis will be on the development of critical thinking skills and health data analysis applications with computer software.