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# Statistics in Medicine

4940
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## Overview

This course aims to provide a firm grounding in the foundations of probability and statistics. Specific topics include:

1. Describing data (types of data, data visualization, descriptive statistics)
2. Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values)
3. Specific statistical tests (ttest, ANOVA, linear correlation, non-parametric tests, relative risks, Chi-square test, exact tests, linear regression, logistic regression, survival analysis; how to choose the right statistical test)

The course focuses on real examples from the medical literature and popular press. Each week starts with "teasers," such as: Should I be worried about lead in lipstick? Should I play the lottery when the jackpot reaches half-a-billion dollars? Does eating red meat increase my risk of being in a traffic accident? We will work our way back from the news coverage to the original study and then to the underlying data. In the process, participants will learn how to read, interpret, and critically evaluate the statistics in medical studies.

The course also prepares participants to be able to analyze their own data, guiding them on how to choose the correct statistical test and how to avoid common statistical pitfalls. Optional modules cover advanced math topics and basic data analysis in R.

PREREQUISITES

There are no prerequisites for this course.

Participants will need to be familiar with a few basic math tools: summation sign, factorial, natural log, exponential, and the equation of a line; a brief tutorial is available on the course website for participants who need a refresher on these topics.

## Syllabus

Week 1 - Descriptive statistics and looking at data
Week 2 - Review of study designs; measures of disease risk and association
Week 3 - Probability, Bayes' Rule, Diagnostic Testing
Week 4 - Probability distributions
Week 5 - Statistical inference (confidence intervals and hypothesis testing)
Week 6 - P-value pitfalls; types I and type II error; statistical power; overview of statistical tests
Week 7 - Tests for comparing groups (unadjusted); introduction to survival analysis
Week 8 - Regression analysis; linear correlation and regression
Week 9 - Logistic regression and Cox regression

#### Taught by

Michael McAuliffe, Rajhansa Sridhara, Michael Hurley and Kristin Sainani

## Reviews for Stanford OpenEdx's Statistics in Medicine 4.5 Based on 6 reviews

• 5 stars 83%
• 4 star 0%
• 3 star 0%
• 2 star 17%
• 1 star 0%

Did you take this course? Share your experience with other students.

• 1
Anonymous
5.0 4 years ago
completed this course.
Outstanding class. Kristin Sainani knows statistics, how to teach and loves both. Examples are kept general enough for everyone but close enough to medicine to keep everyone engaged.

Highly recommended class!
4 people found
Anonymous
5.0 4 years ago
completed this course.
Prof. Sainani is the best! You will have to work at it, but the material will really hold you in good stead for future use and advanced classes.
4 people found
Facundo S
2.0 4 weeks ago
completed this course, spending 1 hours a week on it and found the course difficulty to be very easy.
WHERE IS THE COURSE??? CAN'T FIND IT ANYWHERE, WHEN I CLICK GO TO CLASS IT JUST GET ME TO THE SYLLABUS
Anonymous
5.0 3 years ago
completed this course.
2 people found
Jee-young H
5.0 3 years ago
completed this course.
1 person found