Stanford University is coming up with an online course on Statistics in Medicine. This course focuses on providing a firm base regarding the foundations of probability and statistics.
Topics that are included in this course are:
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-Describing data (types of data, data visualization, and 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).
Through this course students will be able to analyze their own data, will learn 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.
On scoring 60% marks students will pass this course and those who obtain 90% marks will receive a certificate with distinction.
MedStats will start from 24 June, 2014.
Duration of the Course
Students are required to contribute 8 to 12 hours of work per week.
This course will teach students about statistics in medicine.
Students will learn from the real examples of medical literature and popular press. Teasers will be shown in the starting of every week. Through this process students will learn how to read, interpret, and critically evaluate the statistics in medical studies.
-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 those who are familiar with few mathematical tools like summation sign, factorial, natural log, exponential and the equation of a line can apply for this course.
About the Instructor
She is a clinical assistant professor at Stanford University. She received her MS in statistics and her PhD in epidemiology from Stanford University.
Michael Hurley (TA)
He completed his Bachelor’s degree in Materials Science and Engineering from MIT in 2010, and a Masters degree in Clinical Epidemiology at Stanford University in 2012. He is currently a first year medical student at Stanford.
Rajhansa Sridhara (TA)
He completed his Bachelors and Masters in Aerospace Engineering from the Indian Institute of Technology, Bombay in 2011. In Stanford, Rajhansa is a tutor for learning-disabled students through the Office of Accessible Education.
Michael McAuliffe (Instructional Technologist)
He is an Instructional Technologist in EdTech, IRT for the Stanford University School of Medicine.
Follow the Stanford University’s page for more information