Lectures in course MATH-493: Applied biostatistics

Reproducible Research: Principles and Tools

Covers the principles and tools for reproducible research in biostatistics, emphasizing the importance of complete documentation and the use of text editors for compiling source documents.

Bivariate Data Analysis: Correlation and Regression

Explores bivariate data analysis, correlation, and regression techniques for model assessment.

Regression Analysis: Model Selection and Diagnostic Tools

Explores regression analysis tools, model selection, influential points, outliers, and diagnostic plots.

Experimental Design in Biostatistics

Introduces experimental design in biostatistics, covering research process, hypothesis testing, ANOVA modeling, and interpretation of results.

Model Formulas in R: Basics and Applications

Explores model formulas in R, covering basics, interactions, ANOVA, and factorial designs with practical examples.

General Linear Model: Model Selection

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Model Selection Methods in Biostatistics

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

Horseshoe Crabs: Logistic Regression Analysis

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

Generalized Linear Models: Theory and Applications

Explores Generalized Linear Models theory, including logistic and Poisson regression, model evaluation, and coefficient tests.

NGS Data Analysis: Tools and Models

Explores tools and models for Next-Generation Sequencing data analysis, covering DNA sequencing technologies, data analysis pipelines, and statistical models.

Survival Analysis: Modeling and Estimation

Covers survival data analysis, Cox regression, Kaplan-Meier estimator, and log-rank test.

Survival Analysis: Cox Proportional Hazards Model

Introduces the Cox proportional hazards model for survival analysis, covering estimation, assessment of assumptions, and interpretation using residuals.

Categorical Data Analysis: Methods and Examples

Explores the analysis of categorical and discrete data with examples.

Visualizing Categorical Data: Methods and Examples

Explores tools for visualizing categorical data to detect patterns and trends.

Genetic Linkage and Association Studies

Explores genetic linkage, association studies, polymorphisms, and LD in genetic mapping.

Genetic Association Studies: Methods and Challenges

Explores genetic association study methods, challenges, and limitations in analyzing genetic data.

Principal Component Analysis: Understanding Data Structure

Explores Principal Component Analysis, dimensionality reduction, data quality assessment, and error rate control.