4.7
87 ratings
24 reviews
A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction....

100% online courses

Start instantly and learn at your own schedule.

Suggested: 8 weeks of study, 2-3 hours/week

Approx. 21 hours to complete

English

Subtitles: English

Skills you will gain

Regression AnalysisPropensity Score MatchingStatisticsInteraction (Statistics)

100% online courses

Start instantly and learn at your own schedule.

Suggested: 8 weeks of study, 2-3 hours/week

Approx. 21 hours to complete

English

Subtitles: English

1

Section
4 hours to complete

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 (Total 203 min), 3 readings
11 videos
Lecture 1a: Simple Regression: An Overview17m
Lecture 1b: Simple Linear Regression with a Binary (or Nominal Categorical) Predictor 21m
Lecture 1c: Simple Linear Regression with a Continuous Predictor 30m
Lecture 1d: Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates 22m
Lecture 1e: Measuring the Strength of a Linear Association 25m
Lecture 2 Introduction: Simple Logistic Regression1m
Lecture 2a: Simple Logistic Regression with a Binary (or Categorical) Predictor 24m
Lecture 2b: Simple Logistic Regression with a Continuous Predictor 24m
Lecture 2c: Simple Logistic Regression: Accounting for Uncertainty in the Estimates 19m
Lecture 2d: Estimating Risk and Functions of Risk from Logistic Regression Results 14m
Syllabus10m
Learning Objectives, Lecture 110m
Learning Objectives, Lecture 210m

2

Section
4 hours to complete

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 (Total 74 min), 3 readings, 8 quizzes
5 videos
Lecture 3a: Simple Cox Regression: The Concept of Proportional Hazards 19m
Lecture 3b: Simple Cox Regression with Binary or Categorical Predictors 11m
Lecture 3d: Accounting for Uncertainty in Slope Estimate and Translating Cox Regression Results to Predicted Survival Curves 19m
Lecture 3c: Simple Cox Regression with a Continuous Predictor 21m
Learning Objectives, Lecture 310m
Supporting Information for Homework 110m
Quiz 1 Solutions10m
8 practice exercises
Homework 1A16m
Homework 1B22m
Homework 1C10m
Homework 1D10m
Homework 1E10m
Homework 1F10m
Homework 1G14m
Module 1 Quiz: Covers Lectures 1-324m

3

Section
1 hour to complete

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 (Total 54 min), 1 reading
4 videos
Lecture 4a: Confounding: A Formal Definition and Some Examples 24m
Lecture 4b: Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding 17m
Lecture 4c: Adjusted Estimates: The General Idea Behind the Computations 10m
Learning Objectives, Lecture 410m

4

Section
3 hours to complete

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 (Total 65 min), 3 readings, 5 quizzes
4 videos
Lecture 5a: Effect Modification: Introduction with Some Examples 28m
Lecture 5b: Effect Modification: More Examples of Investigating Effect Modification 19m
Lecture 5c: Confounding versus Effect Modification: A Review 15m
Learning Objectives, Lecture 510m
Supporting Information for Homework 210m
Quiz 2 Solutions10m
5 practice exercises
Homework 2A22m
Homework 2B6m
Homework 2C4m
Homework 2D8m
Module 2 Quiz: Covers Lectures 1-524m
4.7

83%

got a tangible career benefit from this course

25%

got a pay increase or promotion

Top Reviews

By MJJun 8th 2017

Very well taught course. I learned valuable skills, and got a better understanding of how to interpret results, published in the literature.

By XPJan 8th 2017

Great course to improve your skills related to statistical data analysis focused on health domain

Instructor

Associate Scientist, Biostatistics
Bloomberg School of Public Health