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Logistic Regression and Prediction for Health Data

Logistic Regression and Prediction for Health Data

本课程是 Data Science for Health Research 专项课程的一部分

位教师：Philip S. Boonstra

包含在中

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3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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

您将获得的技能

您将学习的工具

R Programming

要了解的详细信息

可分享的证书

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作业

7 项作业

授课语言：英语（English）

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本课程是 Data Science for Health Research 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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获得对主题或工具的基础理解
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该课程共有3个模块

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.

单元详情

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

涵盖的内容

11个视频8篇阅读材料2个作业3个讨论话题

11个视频总计80分钟

Data Science for Health Research: Specialization Introduction6分钟
How and When Binary Outcomes Can Arise7分钟
A Need for Models Beyond Linear Regression4分钟
Binary Outcomes, Comparing Between Two Groups (Part 1)10分钟
Binary Outcomes, Comparing Between Two groups (part 2)6分钟
Binary Outcomes, Comparing Between Two groups (part 3)2分钟
Guided Practice: Z-Test15分钟
Guided Practice: Fisher's Exact Test13分钟
Analyzing a Binary Outcome and Binary Exposure with the Odds Ratio6分钟
Interpreting the Odds Ratio5分钟
2x2 Example: The WCGS Cardiovascular Study6分钟

8篇阅读材料总计73分钟

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

2个作业总计90分钟

Module 1 Quiz60分钟
1.2 Practice Quiz30分钟

3个讨论话题总计30分钟

Meet Your Fellow Global Classmates10分钟
1.2 Discussion Prompt10分钟
End of Module 1 Discussion Prompt10分钟

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.

涵盖的内容

11个视频2篇阅读材料3个作业

11个视频总计80分钟

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

2篇阅读材料总计20分钟

2.1 Independent Guide10分钟
2.2 Independent Guide10分钟

3个作业总计90分钟

Module 2 Quiz30分钟
2.1 Practice Quiz30分钟
2.2 Practice Quiz30分钟

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

涵盖的内容

16个视频3篇阅读材料2个作业1个讨论话题

16个视频总计120分钟

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