Full course description
Level: Intermediate
Intermediate courses focus on applying a new analytic tool or framework within the topic area.
Track: Analyst track
Courses targeted to analysts focus learning new approaches for managing and analyzing data in statistical software such as R or SAS, or learning version control procedures using git.
Intended Audience:
This course is targeted to those who have introductory-level training in epidemiology and biostatistics as well as experience using logistic regression, but it is open to all regardless of previous experience.
Overview:
This course provides an overview of how to evaluate the effect of a hypothetical intervention on outcomes using observational data. Participants will review key epidemiologic concepts and analyses including directed acyclic graphs (DAGs), counterfactuals, measures of association, and linear and logistic regression before learning the steps of marginal standardization, including coding example problems in R. There is an emphasis on how to apply these tools to answer real-world questions, such as:
- Estimating the effect of different durations of daily exercise on the probability of being overweight among a cohort of adults using observational data
- Estimating the effect of increasing vaccine uptake from 60% to 80% on rates of disease.
Time commitment: This 4.5 hour course is a combination of lecture and coding in R, including a 1-hour self-directed coding activity in R.
Certificate: Upon completion of all videos, quizzes and activities, participants will be awarded a certificate of completion.