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Logistic Regression and Prediction for Health Data

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

91%

of learners achieved a positive career outcome

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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.

Learn new concepts from industry experts
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.

Module details

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 videosTotal 80 minutes

Data Science for Health Research: Specialization Introduction6 minutes
How and When Binary Outcomes Can Arise7 minutes
A Need for Models Beyond Linear Regression4 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-Test15 minutes
Guided Practice: Fisher's Exact Test13 minutes
Analyzing a Binary Outcome and Binary Exposure with the Odds Ratio6 minutes
Interpreting the Odds Ratio5 minutes
2x2 Example: The WCGS Cardiovascular Study6 minutes

8 readingsTotal 73 minutes

Meet Your Instructors3 minutes
Welcome & Course Syllabus10 minutes
Pre-Course Survey10 minutes
Introduction To and How To Use Independent Guides 10 minutes
Introduction to the BPUrban Data10 minutes
1.2 Independent Guide10 minutes
1.2 Discussion Prompt Suggested Answer10 minutes
End of Module 1 Discussion Prompt Suggested Answer10 minutes

2 assignmentsTotal 90 minutes

1.2 Practice Quiz30 minutes
Module 1 Quiz60 minutes

3 discussion promptsTotal 30 minutes

Meet Your Fellow Global Classmates10 minutes
1.2 Discussion Prompt10 minutes
End of Module 1 Discussion Prompt10 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 videosTotal 80 minutes

Limitations of the 2x2 Table Analysis2 minutes
Logistic Regression: A First Look7 minutes
Visualizing and Interpreting a Logistic Regression9 minutes
Revising the 2x2 Example: WCGS Cardiovascular Study6 minutes
Guided practice: Fitting a Simple Logistic Regression Against One Variable11 minutes
Extending the WCGS Cardiovascular Model with Multivariable Logistic Regression7 minutes
Prediction with Multivariable Logistic Regression5 minutes
Logistic Regression: A Recap and Review7 minutes
Guided Practice: Fitting a Logistic Regression Against More Than One Variable7 minutes
Guided Practice: Calculating Predicted Probabilities11 minutes
Guided Practice: Visualizing a Fitted Logistic Regression Model7 minutes

2 readingsTotal 20 minutes

2.1 Independent Guide10 minutes
2.2 Independent Guide10 minutes

3 assignmentsTotal 90 minutes

2.1 Practice Quiz30 minutes
2.2 Practice Quiz30 minutes
Module 2 Quiz30 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 videosTotal 120 minutes

Why Do We Need to Assess Predictions? 4 minutes
Extracting Probabilities from a Logistic Regression4 minutes
How Do We Determine if Predicted Probabilities are "Good"?4 minutes
Model Calibration9 minutes
Hosmer-Lemeshow Test6 minutes
Model Discrimination10 minutes
Changing the Cutpoint Changes Sensitivity and Specificity4 minutes
Receiver Operating Characteristic (ROC) Curve6 minutes
Area Under the ROC Curve (AUC)5 minutes
AUC Example: Risk of Coronary Heart Disease4 minutes
Brier Score3 minutes
Cross Validation6 minutes
Guided Practice: Assessing the Predictive Ability of Logistic Regression Models19 minutes
Guided Practice: ROC and AUC11 minutes
Guided Practice: Brier Score10 minutes
Case Study: Treatment of Testicular Cancer17 minutes