Statistical Reasoning for Public Health 2: Regression Methods

About this course: A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.

Created by:  Johns Hopkins University

• Taught by:  John McGready, PhD, MS, Associate Scientist, Biostatistics

  Bloomberg School of Public Health

Commitment 8 weeks of study, 2-3 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.7 stars Average User Rating 4.7See what learners said Coursework

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Syllabus


WEEK 1


Introduction and Module 1A: Simple Regression Methods
In this module, a unified structure for simple regression models will be presented, followed by detailed treatises and examples of both simple linear and logistic models.


11 videos, 3 readings expand

1. Video: Welcome to Statistical Reasoning for Public Health 2
2. Reading: Syllabus
3. Reading: Learning Objectives, Lecture 1
4. Video: Lecture 1a: Simple Regression: An Overview
5. Video: Lecture 1b: Simple Linear Regression with a Binary (or Nominal Categorical) Predictor
6. Video: Lecture 1c: Simple Linear Regression with a Continuous Predictor
7. Video: Lecture 1d: Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates
8. Video: Lecture 1e: Measuring the Strength of a Linear Association
9. Reading: Learning Objectives, Lecture 2
10. Video: Lecture 2 Introduction: Simple Logistic Regression
11. Video: Lecture 2a: Simple Logistic Regression with a Binary (or Categorical) Predictor
12. Video: Lecture 2b: Simple Logistic Regression with a Continuous Predictor
13. Video: Lecture 2c: Simple Logistic Regression: Accounting for Uncertainty in the Estimates
14. Video: Lecture 2d: Estimating Risk and Functions of Risk from Logistic Regression Results

WEEK 2


Module 1B: Simple Regression Methods



In this model, more detail is given regarding Cox regression, and it's similarities and differences from the other two regression models from module 1A. The basic structure of the model is detailed, as well as its assumptions, and multiple examples are presented.


5 videos, 3 readings, 7 practice quizzes expand

1. Reading: Learning Objectives, Lecture 3
2. Video: Lecture 3 Introduction: Simple Cox (Proportional Hazards) Regression
3. Video: Lecture 3a: Simple Cox Regression: The Concept of Proportional Hazards
4. Video: Lecture 3b: Simple Cox Regression with Binary or Categorical Predictors
5. Video: Lecture 3d: Accounting for Uncertainty in Slope Estimate and Translating Cox Regression Results to Predicted Survival Curves
6. Video: Lecture 3c: Simple Cox Regression with a Continuous Predictor
7. Reading: Supporting Information for Homework 1
8. Practice Quiz: Homework 1A
9. Practice Quiz: Homework 1B
10. Practice Quiz: Homework 1C
11. Practice Quiz: Homework 1D
12. Practice Quiz: Homework 1E
13. Practice Quiz: Homework 1F
14. Practice Quiz: Homework 1G
15. Reading: Quiz 1 Solutions

Graded: Module 1 Quiz: Covers Lectures 1-3

WEEK 3


Module 2A: Confounding and Effect Modification (Interaction)



This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.


4 videos, 1 reading expand

1. Reading: Learning Objectives, Lecture 4
2. Video: Lecture 4 Introduction: Confounding
3. Video: Lecture 4a: Confounding: A Formal Definition and Some Examples
4. Video: Lecture 4b: Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding
5. Video: Lecture 4c: Adjusted Estimates: The General Idea Behind the Computations

WEEK 4


Module 2B: Effect Modification (Interaction



Effect modification (Interaction), unlike confounding, is a phenomenon of "nature" and cannot be controlled by study design choice. However, it can be investigated in a manner similar to that of confounding. This set of lectures will define and give examples of effect modification, and compare and contrast it with confounding.


4 videos, 3 readings, 4 practice quizzes expand

1. Reading: Learning Objectives, Lecture 5
2. Video: Lecture 5 Introduction: Effect Modification
3. Video: Lecture 5a: Effect Modification: Introduction with Some Examples
4. Video: Lecture 5b: Effect Modification: Examples of Investigating Effect Modification
5. Video: Lecture 5c: Confounding versus Effect Modification: A Review
6. Reading: Supporting Information for Homework 2
7. Practice Quiz: Homework 2A
8. Practice Quiz: Homework 2B
9. Practice Quiz: Homework 2C
10. Practice Quiz: Homework 2D
11. Reading: Quiz 2 Solutions

Graded: Module 2 Quiz: Covers Lectures 1-5

WEEK 5


Module 3A: Multiple Regression Methods
This module extends linear and logistic methods to allow for the inclusion of multiple predictors in a single regression model.


8 videos, 2 readings expand

1. Reading: Learning Objectives, Lecture 6
2. Video: Lecture 6a: An Overview of Multiple Regression for Estimation, Adjustment and Basic Prediction and Multiple Linear Regression
3. Video: Lecture 6b: Multiple Linear Regression: Some Examples
4. Video: Lecture 6c: Multiple Linear Regression: Basics of Model Selection and Estimating Outcomes
5. Video: Lecture 6d: Multiple Linear Regression: Some Examples from the Literature
6. Reading: Learning Objectives, Lecture 7
7. Video: Lecture 7 Introduction: Multiple Logistic Regression
8. Video: Lecture 7a: Multiple Logistic Regression: Some Examples
9. Video: Lecture 7b: Basics of Model Selection and Estimating Outcomes
10. Video: Lecture 7c: Some Examples from the Literature

WEEK 6


Module 3B: Multiple Regression Methods
This set of lectures extends the techniques debuted in lecture set 3 to allow for multiple predictors of a time-to-event outcome using a single, multivariable regression model.


8 videos, 4 readings, 4 practice quizzes expand

1. Reading: Learning Objectives, Lecture 8
2. Video: Lecture 8 Introduction: Multiple Cox Regression
3. Video: Lecture 8a: Multiple Cox PH Regression: Some Examples
4. Video: Lecture 8b: Multiple Cox Regression: Basics of Model Selection and Estimating Outcomes
5. Video: Lecture 8c: Multiple Cox Regression: Some Examples from the Literature
6. Reading: Learning Objectives, Lecture 9
7. Video: Lecture 9 Introduction: Investigating Effect Modification and Non-Linear Relationships with Multiple Regression
8. Video: Lecture 9a: Effect Modification and Non-Linear Associations: Regression Based Approaches
9. Video: Lecture 9b: Examples of Interaction Terms from Published Research
10. Video: Lecture 9c: Non-Linear Relationships with Continuous Predictors in Regression: The Spline Approach
11. Reading: Supporting Information for Homework 3
12. Practice Quiz: Homework 3A
13. Practice Quiz: Homework 3B
14. Practice Quiz: Homework 3C
15. Practice Quiz: Homework 3D
16. Reading: Quiz 3 Solutions

Graded: Module 3 Quiz: Covers Lectures 1-8

WEEK 7


Module 4: Additional Topics in Regression



4 videos, 3 readings, 3 practice quizzes expand

1. Video: Lecture 10 Introduction: Propensity Scores: Another Approach to Estimating Adjusted Associations
2. Video: Lecture 10a: Propensity Scores: Definition and Adjustment
3. Video: Lecture 10b: Examples of Propensity Score Adjustment
4. Video: Lecture 10c: Propensity Score Matching
5. Reading: Supporting Information for Homework 4
6. Practice Quiz: Homework 4A
7. Practice Quiz: Homework 4B
8. Practice Quiz: Homework 4C
9. Reading: Quiz 4 Solutions
10. Reading: Learning Objectives, Lecture 10

Graded: Module 4 Quiz: Covers Lectures 1-10