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, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. 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 logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.
Overview
Syllabus
- Simple Regression Methods
- Module one covers simple regression, the four different types of regression, commonalities between them, and simple linear aggression. Before completing the graded quiz, you can test your knowledge with the practice quiz.
- Simple Logistic Regression
- Within module two, we will look at logistic regression, create confidence intervals, and estimate p-values. You will have the opportunity to test your knowledge in both a practice quiz and a graded quiz.
- Simple Cox Proportional Hazards Regression
- Module three focuses on Cox regression with different predictors. You will have the opportunity to test your knowledge first with the practice quiz and, then, with the graded quiz.
- Confounding, Adjustment, and Effect Modification
- Within module four, you will look at confounding and adjustment, and unadjusted and adjusted association estimates. Additionally, you will learn about effect modification. Similar to previous modules, you will first take a practice quiz before completing the graded quiz.
- 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.
Taught by
John McGready, PhD, MS
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5.0 rating, based on 1 Class Central review
4.7 rating at Coursera based on 372 ratings
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I would consider this an intuitive introduction to linear regression as a powerful statistical tool. The use of data from real studies is also a nice touch.
