Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you'll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.
本课程是 Biostatistics in Public Health 专项课程 专项课程的一部分
Multiple Regression Analysis in Public Health
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课程信息
19,447 次近期查看
您将学到的内容有
Practice multiple regression methods to determine relationships between an outcome and multiple predictors
Use the Spline approach for non-linear relationships with continuous predictors
Perform calculations with multiple predictor variables
您将获得的技能
effect modifcationProportional Hazards ModelRegression AnalysisSpline approach
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 4 小时
An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression
Within this module, an overview of multiple regression will be provided. Additionally, examples and applications will be examined. A practice quiz is provided to test your knowledge before completing the graded quiz.
9 个视频 （总计 177 分钟）, 2 个阅读材料, 2 个测验
9 个视频
Multiple Linear Regression: Some Examples23分钟
Multiple Linear Regression: Basics of Model Estimation, and Handling Uncertainty in the Resulting Estimates14分钟
Estimate Group Means and Mean Differences for Groups Who Differ in More than One Predictor with Multiple Linear Regression9分钟
The “Linearity” Assumption and Estimating Amount of Variability Explained by Multiple Predictors21分钟
Examples from the Literature21分钟
Investigating Effect Modification with Multiple Linear Regression (Forthcoming)20分钟
Investigating Effect Modification with Multiple Linear Regression—More Examples, For Those Interested18分钟
Additional Examples of Multiple Linear Regression34分钟
2 个阅读材料
Solutions to Practice Quiz on Multiple Linear Regression10分钟
Solutions to Summative Quiz 110分钟
2 个练习
Practice Quiz: An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression30分钟
An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression30分钟
周
2完成时间为 4 小时
Multiple Logistic Regression
Module two covers examples of multiple logistic regression, basics of model estimates, and a discussion of effect modification. In addition to lectures, you will also be completing a practice quiz and graded quiz.
8 个视频 （总计 173 分钟）, 2 个阅读材料, 2 个测验
8 个视频
Multiple Logistic Regression: Basics of Model Estimation, and Handling Uncertainty in the Resulting Estimates18分钟
Estimating Group Odds and Proportions, and Odds Ratios for Groups Who Differ in More than One Predictor with Multiple Linear Regression24分钟
The “Linearity” Assumption and A Brief Note about Prediction with Multiple Logistic Regression27分钟
Examples from the Literature26分钟
Investigating Effect Modification with Multiple Logistic Regression, Part 118分钟
Investigating Effect Modification with Multiple Logistic Regression, Part 210分钟
Additional Examples21分钟
2 个阅读材料
Solutions to Practice Quiz Items 1-910分钟
Solutions to Quiz on Logistic Regression10分钟
2 个练习
Practice Quiz: Multiple Logistic Regression30分钟
Multiple Logistic Regression30分钟
周
3完成时间为 3 小时
Multiple Cox Regression
The last module for this class focuses on multiple Cox regression, the “Linearity” assumption, examples, and applications. You will complete a practice quiz, graded quiz, and project.
5 个视频 （总计 95 分钟）, 2 个阅读材料, 2 个测验
5 个视频
Some Examples17分钟
Basics of Model Estimation and Handling Uncertainty in the Resulting Estimates18分钟
Estimating Hazard Ratios for Groups who Differ in more than one Predictor with Multiple Cox Regression12分钟
The "Linearity" Assumption and Prediction with Multiple Cox Regression21分钟
Examples from the Literature25分钟
2 个阅读材料
Soluitons To Multiple Cox Regression Practice Quiz10分钟
Solutions: Summative Quiz (Multiple Cox Regression)10分钟
2 个练习
Practice Quiz: Multiple Cox Regression30分钟
Multiple Cox Regression, and Course Concepts30分钟
周
4完成时间为 2 小时
Course Project
During this module, you get the chance to demonstrate what you've learned by putting yourself in the shoes of biostatistical consultant on two different studies, one about self-administration of injectable contraception and one about medical appointment scheduling in Brazil. The two research teams have asked you to help them interpret previously published results in order to inform the planning of their own studies. If you've already taken other courses in this specialization, then this scenario will be familiar.
1 个阅读材料, 1 个测验
1 个阅读材料
Biostatistical Consulting Project10分钟
1 个练习
Project2小时
4.8
9 个审阅来自Multiple Regression Analysis in Public Health 的热门评论
The course is well structured. The numerous examples and practice questions help the student apply the concepts learned. I love the fact that almost every question was from a real article.
The course was five stars, there were some quiz issues and a few numerical issues, but I learned a lot and appreciate the time and effort put into the content. Thank you!
讲师
John McGready, PhD, MS
Associate Scientist, BiostatisticsBloomberg School of Public Health
关于 约翰霍普金斯大学
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.
关于 Biostatistics in Public Health 专项课程
This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.
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