Course Description
This advanced graduate course equips students with the biostatistical knowledge and skills essential for conducting, interpreting, and critically evaluating public health research and practice. Students explore descriptive and inferential statistics, probability, sampling distributions, hypothesis testing, confidence intervals, power and sample size determination, and advanced techniques including regression analysis, ANOVA, non-parametric methods, survival analysis, and multivariate approaches. Emphasis is placed on the application of biostatistical methods to real-world public health data, integration with epidemiologic study designs, assessment of assumptions and limitations, and effective communication of statistical findings to diverse audiences.Through intensive readings, data analysis exercises, software applications, literature critiques, and applied projects, students develop competencies to analyze complex datasets, address health disparities through statistical methods, and support evidence-based decision-making in public health. The course prepares doctoral-level students and advanced practitioners for roles involving research design, data management, analysis, and translation of results in academia, government agencies, NGOs, and global health settings.Credit Hours: 5

Prerequisites: Graduate standing; prior coursework in epidemiology (e.g., PUBH 8100 or PUBH 6320) and introductory biostatistics strongly recommended. Basic familiarity with statistical software is helpful.
Format: Accelerated online/hybrid (8 weeks); rigorous pace with weekly modules, asynchronous discussions, hands-on data analysis, and substantial applied work.

Course Objectives / Learning Outcomes
Upon successful completion, students will be able to:Explain and apply core biostatistical concepts, including probability, distributions, sampling, and inference in public health contexts.
Select, perform, and interpret appropriate statistical tests and models for different types of public health data and research questions.
Critically evaluate statistical methods, assumptions, limitations, and potential biases in published public health research.
Use statistical software to manage, analyze, and visualize data while addressing issues such as missing data and outliers.
Calculate and interpret sample size, power, and effect sizes for study planning and evaluation.
Integrate biostatistical analysis with epidemiologic and environmental health perspectives to address health equity and disparities.
Communicate statistical results clearly and ethically through written reports, visualizations, and presentations for scientific and lay audiences.

Required Resources
Primary Textbook: Sullivan, L.M. Essentials of Biostatistics in Public Health (latest edition) or similar applied text (e.g., Rosner, B. Fundamentals of Biostatistics).
Additional: CDC biostatistics resources; selected chapters from Biostatistics for Public Health texts; peer-reviewed articles with statistical applications (via LMS).
Software: SPSS (preferred for Walden alignment), R, or SAS (students may use any; conceptual guidance provided). Public datasets (e.g., NHANES, CDC Wonder, BRFSS).
Supplemental: Online calculators, video tutorials for software, and statistical tables.

Weekly Structure
Each week includes extensive readings, multimedia (software demos, concept videos), one Weekly Discussion (initial post + 2–3 substantive replies with data/literature integration), and one intensive Assignment/Activity involving calculations, software output interpretation, or analysis. The 5-credit accelerated format requires significant hands-on practice.

Week 1: Foundations of Biostatistics – Descriptive Statistics, Probability, and Levels of Measurement

Discussion: Introduce yourself and your prior experience with statistics. Discuss why strong biostatistical skills are critical for evidence-based public health practice today. Respond to at least two peers.

Assignment: Data exploration exercise: Analyze a provided public health dataset using descriptive statistics (measures of central tendency, dispersion, graphs). Submit output, interpretations, and a 2–3 page reflection on data types and visualization best practices.

Week 2: Sampling Distributions, Confidence Intervals, and Hypothesis Testing

Discussion: Explain the role of sampling distributions and confidence intervals in public health inference. How do these concepts help (or sometimes mislead) when interpreting study results?
Assignment: Perform hypothesis tests (e.g., t-tests, z-tests) on sample data using software; interpret p-values, confidence intervals, and Type I/II errors with examples from public health literature.

Week 3: Comparing Groups – t-Tests, ANOVA, and Non-Parametric Alternatives
Discussion: Compare parametric vs. non-parametric tests for group comparisons. When would you choose each in analyzing health disparities or intervention outcomes?
Assignment: Group comparison analysis: Use software to run t-tests or ANOVA on a dataset; check assumptions, interpret results, and discuss implications for a public health scenario.

Week 4: Correlation, Regression, and Measures of Association

Discussion: Discuss simple and multiple linear regression in public health research. How do these methods help control for confounders when linking exposures (e.g., environmental factors) to outcomes?
Assignment: Regression modeling: Build and interpret a regression model using software; assess model fit, multicollinearity, and residuals.

Week 5: Categorical Data Analysis, Chi-Square, and Logistic Regression (Mid-Term Focus)
Discussion: Explain the use of chi-square tests and logistic regression for binary/categorical outcomes. Provide examples from infectious disease or chronic disease epidemiology.
Assignment: Mid-term applied analysis (4–5 pages + output): Analyze categorical data or run logistic regression; interpret odds ratios and discuss limitations in the context of health equity.

Week 6: Survival Analysis, Power & Sample Size, and Advanced Topics

Discussion: Describe Kaplan-Meier methods or Cox regression and their value in longitudinal public health studies. How does sample size planning influence study validity?
Assignment: Survival or power analysis exercise: Calculate sample size for a hypothetical study or interpret survival curves from a provided dataset.

Week 7: Multivariate Methods, Data Management, and Critical Appraisal of Statistical ReportingDiscussion: How do advanced multivariate techniques (e.g., MANOVA, multilevel modeling) address complex public health questions? Discuss common pitfalls in statistical reporting in journals.
Assignment: Critical appraisal: Review 2–3 published studies for appropriate statistical methods, assumptions, and interpretations; propose improvements.

Week 8: Course Synthesis, Integration, Ethics, and Culminating Project
Discussion: Reflect on how biostatistics integrates with epidemiology, environmental health, and leadership in your program. What is one key statistical concept or skill you will apply in future research or practice? Respond thoughtfully to peers.
Assignment: Culminating Project – Biostatistical Analysis Report or Research Application (12–15 slides or 10–12 pages): Select a public health research question or dataset. Perform a comprehensive analysis (descriptive + inferential methods, including regression or advanced techniques), interpret results in context, address assumptions/biases/ethics, integrate with other domains (e.g., epidemiology or environmental health), and communicate findings with clear visualizations and policy/practice recommendations. Include software output appendix. (Major project, weighted heavily.)

Grading Breakdown (Example)Weekly Discussions: 20%
Weekly Assignments & Data Analyses: 45%
Culminating Biostatistical Project: 35%

Total: 100%

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