Statistics in Medicine
ABOUT THIS COURSE
Welcome to the self-paced version of Statistics in Medicine! The course is organized into 9 learning units that contain videos, quizzes, and a homework assignment. You may complete these at any pace. At the end of the course, there is a multiple-choice final exam. Once you have completed the course, if you have earned at least 60% on the graded assignments, you will be able to request a Statement of Accomplishment (users who score 90% or higher will earn a Statement of Accomplishment with Distinction). Course enrollment and the materials and assignments will be open until at least June, 2018. If access to the course materials will be turned off at any point, notification will go out to all course participants.
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 unit 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.
Unit 1 – Descriptive statistics and looking at data
Unit 2 – Review of study designs; measures of disease risk and association
Unit 3 – Probability, Bayes’ Rule, Diagnostic Testing
Unit 4 – Probability distributions
Unit 5 – Statistical inference (confidence intervals and hypothesis testing)
Unit 6 – P-value pitfalls; types I and type II error; statistical power; overview of statistical tests
Unit 7 – Tests for comparing groups (unadjusted); introduction to survival analysis
Unit 8 – Regression analysis; linear correlation and regression
Unit 9 – Logistic regression and Cox regression
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, and can also be found here.
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