4.7

81 ratings

•

21 reviews

Johns Hopkins University

Об этом курсе

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

Section

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

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

Section

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

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

Homework 1A16m

Homework 1B22m

Homework 1C10m

Homework 1D10m

Homework 1E10m

Homework 1F10m

Homework 1G14m

Module 1 Quiz: Covers Lectures 1-324m

Section

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

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

Section

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

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

Homework 2A22m

Homework 2B6m

Homework 2C4m

Homework 2D8m

Module 2 Quiz: Covers Lectures 1-524m

Section

This module extends linear and logistic methods to allow for the inclusion of multiple predictors in a single regression model....

8 videos (Total 172 min), 2 readings

Lecture 6b: Multiple Linear Regression: Some Examples 25m

Lecture 6c: Multiple Linear Regression: Basics of Model Selection and Estimating Outcomes 19m

Lecture 6d: Multiple Linear Regression: Some Examples from the Literature 23m

Lecture 7 Introduction: Multiple Logistic Regression1m

Lecture 7a: Multiple Logistic Regression: Some Examples 30m

Lecture 7b: Basics of Model Selection and Estimating Outcomes 22m

Lecture 7c: Some Examples from the Literature 33m

Learning Objectives, Lecture 610m

Learning Objectives, Lecture 710m

Section

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 (Total 160 min), 4 readings, 5 quizzes

Lecture 8a: Multiple Cox PH Regression: Some Examples 18m

Lecture 8b: Multiple Cox Regression: Basics of Model Selection and Estimating Outcomes 22m

Lecture 8c: Multiple Cox Regression: Some Examples from the Literature 28m

Lecture 9 Introduction: Investigating Effect Modification and Non-Linear Relationships with Multiple Regression2m

Lecture 9a: Effect Modification and Non-Linear Associations: Regression Based Approaches 30m

Lecture 9b: Examples of Interaction Terms from Published Research 26m

Lecture 9c: Non-Linear Relationships with Continuous Predictors in Regression: The Spline Approach 28m

Learning Objectives, Lecture 810m

Learning Objectives, Lecture 910m

Supporting Information for Homework 310m

Quiz 3 Solutions10m

Homework 3A12m

Homework 3B14m

Homework 3C14m

Homework 3D14m

Module 3 Quiz: Covers Lectures 1-824m

Section

...

4 videos (Total 73 min), 3 readings, 4 quizzes

Lecture 10a: Propensity Scores: Definition and Adjustment 26m

Lecture 10b: More Examples of Propensity Score Adjustment 18m

Lecture 10c: Propensity Score Matching 23m

Supporting Information for Homework 410m

Quiz 4 Solutions10m

Learning Objectives, Lecture 1010m

Homework 4A12m

Homework 4B8m

Homework 4C6m

Module 4 Quiz: Covers Lectures 1-1038m

4.7

got a tangible career benefit from this course

got a pay increase or promotion

By MJ•Jun 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 XP•Jan 8th 2017

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

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

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