Logistic Regression and Prediction for Health Data

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Logistic Regression and Prediction for Health Data

This course is part of Data Science for Health Research Specialization

Instructors: Philip S. Boonstra

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Understand how binary outcomes arise and know the difference between prevalence, risk ratios, and odds ratios
Use logistic regression to estimate and interpret the association between one or more predictors and a binary outcome
Understand the principles for using logistic regression to make predictions and assessing the quality of those predictions

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Data Science for Health Research Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become familiar with fundamental tests for two-group comparisons and statistical inference plus prediction more broadly using logistic regression. They will understand the connection between prevalence, risk ratios, and odds ratios. By the end of this course, learners will be able to understand how binary outcomes arise, how to use R to compare proportions between two groups, how to fit logistic regressions in R, how to make predictions using logistic regression, and how to assess the quality of these predictions. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.

This module introduces you to binary outcomes, including how they arise, how to calculate proportions, and how to compare proportions between two groups.

What's included

11 videos8 readings2 assignments3 discussion prompts

11 videos Total 80 minutes

Data Science for Health Research: Specialization Introduction 6 minutes
How and When Binary Outcomes Can Arise 7 minutes
A Need for Models Beyond Linear Regression 4 minutes
Binary Outcomes, Comparing Between Two Groups (Part 1) 10 minutes
Binary Outcomes, Comparing Between Two groups (part 2) 6 minutes
Binary Outcomes, Comparing Between Two groups (part 3) 2 minutes
Guided Practice: Z-Test 15 minutes
Guided Practice: Fisher's Exact Test 13 minutes
Analyzing a Binary Outcome and Binary Exposure with the Odds Ratio 6 minutes
Interpreting the Odds Ratio 5 minutes
2x2 Example: The WCGS Cardiovascular Study 6 minutes

8 readings Total 73 minutes

Meet Your Instructors 3 minutes
Welcome & Course Syllabus 10 minutes
Pre-Course Survey 10 minutes
Introduction To and How To Use Independent Guides 10 minutes
Introduction to the BPUrban Data 10 minutes
1.2 Independent Guide 10 minutes
1.2 Discussion Prompt Suggested Answer 10 minutes
End of Module 1 Discussion Prompt Suggested Answer 10 minutes

2 assignments Total 90 minutes

1.2 Practice Quiz 30 minutes
Module 1 Quiz 60 minutes

3 discussion prompts Total 30 minutes

Meet Your Fellow Global Classmates 10 minutes
1.2 Discussion Prompt 10 minutes
End of Module 1 Discussion Prompt 10 minutes

In this module, you will be introduced to the ubiquitous logistic regression, one of the most common tools for measuring the association between one or more predictors and a binary outcome.

What's included

11 videos2 readings3 assignments

11 videos Total 80 minutes

Limitations of the 2x2 Table Analysis 2 minutes
Logistic Regression: A First Look 7 minutes
Visualizing and Interpreting a Logistic Regression 9 minutes
Revising the 2x2 Example: WCGS Cardiovascular Study 6 minutes
Guided practice: Fitting a Simple Logistic Regression Against One Variable 11 minutes
Extending the WCGS Cardiovascular Model with Multivariable Logistic Regression 7 minutes
Prediction with Multivariable Logistic Regression 5 minutes
Logistic Regression: A Recap and Review 7 minutes
Guided Practice: Fitting a Logistic Regression Against More Than One Variable 7 minutes
Guided Practice: Calculating Predicted Probabilities 11 minutes
Guided Practice: Visualizing a Fitted Logistic Regression Model 7 minutes

2 readings Total 20 minutes

2.1 Independent Guide 10 minutes
2.2 Independent Guide 10 minutes

3 assignments Total 90 minutes

2.1 Practice Quiz 30 minutes
2.2 Practice Quiz 30 minutes
Module 2 Quiz 30 minutes

This module introduces you to tools for assessing the quality of a fitted logistic regression model.

What's included

16 videos3 readings2 assignments1 discussion prompt

16 videos Total 120 minutes

Why Do We Need to Assess Predictions? 4 minutes
Extracting Probabilities from a Logistic Regression 4 minutes
How Do We Determine if Predicted Probabilities are "Good"? 4 minutes
Model Calibration 9 minutes
Hosmer-Lemeshow Test 6 minutes
Model Discrimination 10 minutes
Changing the Cutpoint Changes Sensitivity and Specificity 4 minutes
Receiver Operating Characteristic (ROC) Curve 6 minutes
Area Under the ROC Curve (AUC) 5 minutes
AUC Example: Risk of Coronary Heart Disease 4 minutes
Brier Score 3 minutes
Cross Validation 6 minutes
Guided Practice: Assessing the Predictive Ability of Logistic Regression Models 19 minutes
Guided Practice: ROC and AUC 11 minutes
Guided Practice: Brier Score 10 minutes
Case Study: Treatment of Testicular Cancer 17 minutes

3 readings Total 25 minutes

3.3 Independent Guide 10 minutes
End of Module 3 Discussion Prompt Suggested Answer 10 minutes
Post-Course Survey 5 minutes

2 assignments Total 60 minutes

3.3 Practice Quiz 30 minutes
Module 3 Quiz 30 minutes

1 discussion prompt Total 10 minutes

End of Module 3 Discussion Prompt 10 minutes

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Instructors

Philip S. Boonstra

University of Michigan

4 Courses 4,269 learners

Offered by

University of Michigan

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