**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.

Johns Hopkins University

**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.

**Taught by:**Â Â John McGready, PhD, MS, Associate Scientist, Biostatistics

Commitment | 8 weeks of study, 2-3 hours/week |

Language | English |

How To Pass | Pass all graded assignments to complete the course. |

User Ratings |

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

**Video:**Welcome to Statistical Reasoning for Public Health 2**Reading:**Syllabus**Reading:**Learning Objectives, Lecture 1**Video:**Lecture 1a: Simple Regression: An Overview**Video:**Lecture 1b: Simple Linear Regression with a Binary (or Nominal Categorical) Predictor**Video:**Lecture 1c: Simple Linear Regression with a Continuous Predictor**Video:**Lecture 1d: Simple Linear Regression Model: Estimating the Regression Equationâ€”Accounting for Uncertainty in the Estimates**Video:**Lecture 1e: Measuring the Strength of a Linear Association**Reading:**Learning Objectives, Lecture 2**Video:**Lecture 2 Introduction: Simple Logistic Regression**Video:**Lecture 2a: Simple Logistic Regression with a Binary (or Categorical) Predictor**Video:**Lecture 2b: Simple Logistic Regression with a Continuous Predictor**Video:**Lecture 2c: Simple Logistic Regression: Accounting for Uncertainty in the Estimates**Video:**Lecture 2d: Estimating Risk and Functions of Risk from Logistic Regression Results

WEEK 2

Module 1B: More 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

**Reading:**Learning Objectives, Lecture 3**Video:**Lecture 3 Introduction: Simple Cox (Proportional Hazards) Regression**Video:**Lecture 3a: Simple Cox Regression: The Concept of Proportional Hazards**Video:**Lecture 3b: Simple Cox Regression with Binary or Categorical Predictors**Video:**Lecture 3d: Accounting for Uncertainty in Slope Estimate and Translating Cox Regression Results to Predicted Survival Curves**Video:**Lecture 3c: Simple Cox Regression with a Continuous Predictor**Reading:**Supporting Information for Homework 1**Practice Quiz:**Homework 1A**Practice Quiz:**Homework 1B**Practice Quiz:**Homework 1C**Practice Quiz:**Homework 1D**Practice Quiz:**Homework 1E**Practice Quiz:**Homework 1F**Practice Quiz:**Homework 1G**Reading:**Quiz 1 Solutions

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

**Reading:**Learning Objectives, Lecture 4**Video:**Lecture 4 Introduction: Confounding**Video:**Lecture 4a: Confounding: A Formal Definition and Some Examples**Video:**Lecture 4b: Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding**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

**Reading:**Learning Objectives, Lecture 5**Video:**Lecture 5 Introduction: Effect Modification**Video:**Lecture 5a: Effect Modification: Introduction with Some Examples**Video:**Lecture 5b: Effect Modification: More Examples of Investigating Effect Modification**Video:**Lecture 5c: Confounding versus Effect Modification: A Review**Reading:**Supporting Information for Homework 2**Practice Quiz:**Homework 2A**Practice Quiz:**Homework 2B**Practice Quiz:**Homework 2C**Practice Quiz:**Homework 2D**Reading:**Quiz 2 Solutions

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

**Reading:**Learning Objectives, Lecture 6**Video:**Lecture 6a: An Overview of Multiple Regression for Estimation, Adjustment and Basic Prediction and Multiple Linear Regression**Video:**Lecture 6b: Multiple Linear Regression: Some Examples**Video:**Lecture 6c: Multiple Linear Regression: Basics of Model Selection and Estimating Outcomes**Video:**Lecture 6d: Multiple Linear Regression: Some Examples from the Literature**Reading:**Learning Objectives, Lecture 7**Video:**Lecture 7 Introduction: Multiple Logistic Regression**Video:**Lecture 7a: Multiple Logistic Regression: Some Examples**Video:**Lecture 7b: Basics of Model Selection and Estimating Outcomes**Video:**Lecture 7c: Some Examples from the Literature

WEEK 6

Module 3B: More 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

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

WEEK 7

Module 4: Additional Topics in Regression

4 videos, 3 readings, 3 practice quizzes

**Video:**Lecture 10 Introduction: Propensity Scores: Another Approach to Estimating Adjusted Associations**Video:**Lecture 10a: Propensity Scores: Definition and Adjustment**Video:**Lecture 10b: More Examples of Propensity Score Adjustment**Video:**Lecture 10c: Propensity Score Matching**Reading:**Supporting Information for Homework 4**Practice Quiz:**Homework 4A**Practice Quiz:**Homework 4B**Practice Quiz:**Homework 4C**Reading:**Quiz 4 Solutions**Reading:**Learning Objectives, Lecture 10

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Rated 4.7 out of 5 of 74 ratings

DG

Great course

MD

I learned a lot, and it was wonderfully taught!

S

Wonderful course!

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