Applied Biostatistical Methods II

Description from DukeHub

This course provides an introduction to general linear models and the concept of experimental designs. Topics include linear regression models, analysis of variance, mixed-effects models, generalized linear models (GLM) including binary, multinomial responses and log-linear models, basic models for survival analysis and regression models for censored survival data, and model assessment, validation and prediction. Core concepts are mastered through statistical methods application and analysis of practical research problems encountered by program faculty and demonstrated in practicum experiences in concert with BIOSTAT 706/BIOSTAT 706A. Computational examples and exercises will use the SAS and R packages. Credits: 3

Skills

• data and statisticsdescription: "regression" and 2 more
• programmingAI review: "Computational examples and exercises will use the SAS and R packages"

AI-resilient: 0 of 5 0/5

• No: Physical, lab or field work
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

More in BIOSTAT

• Introduction to Statistical Theory and Methods II BIOSTAT 704
• Introduction to the Practice of Biostatistics II BIOSTAT 706
• Clinical Trial Design and Analysis BIOSTAT 708
• Introduction to Causal Inference BIOSTAT 709
• Analysis of Correlated and Longitudinal Data BIOSTAT 718
• Introduction to Statistical Programming II (SAS) BIOSTAT 722

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