Statistics in Medicine
This course aims to provide a firm grounding in the foundations of probability and statistics. Specific topics include:
- Describing data (types of data, data visualization, descriptive statistics)
- Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values)
- 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, students will learn how to read, interpret, and critically evaluate the statistics in medical studies.
The course also prepares students 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.
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
Students 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 students who need a refresher on these topics.
Kristin Sainani (née Cobb), associate professor, Stanford University
Joshua Wallach, current PhD student, Epidemiology and Clinical Research, Stanford University
Mike McAuliffe, Instructional Technologist in EdTech, IRT, Stanford University School of Medicine