Agenda » Learning objectives
Course learning objectives
Basic course
The objective of this course is to introduce students to the logic of a basic meta-analysis and teach them how to perform a meta-analysis, critique a meta-analysis, and avoid common mistakes in meta-analysis. By the end of the course students will understand
- The goals of a meta-analysis
- How to choose a statistical model
- How to choose an effect-size index
- How to enter data for a simple meta-analysis
- How to estimate the mean effect size
- How to quantify and understand heterogeneity in effects
- How to report the results of the analysis
- How to create forest plots
- How to create plots that show the distribution of true effects
- How to avoid common mistakes in all these areas
Advanced course
The objective of this course is to teach students advanced issues in meta-analysis. By the end of the course the student will understand
- How to use subgroup analyses to compare the impact of a treatment in sets of studies that enrolled different populations or employed different variants of an intervention (analogous to ANOVA in a primary study)
- How to use meta-regression to assess the unique impact of continuous or categorical covariates on the effect size (analogous to multiple-regression in a primary study)
- How to assess the potential impact of publication bias on the analysis
- What to do when there are only a small number of studies in the analysis
- How to avoid common mistakes in all these areas
Testimonials
"The course has enlightened me a lot about meta-analysis. The way I look at a meta-analysis is completely different as this workshop has changed my perspective about meta-analysis completely. It is a kind of an eye opener for me. The course helped me understand the concepts of meta-analysis very clearly."
Rama Krishna Guggilla - Medical University of Bialystok - Los Angeles 2019
"Thank you very much for the wonderful workshop at Kent State University. I really enjoyed it. I particularly like the way you organize the course, starting with the concept, then applications and examples, and finally common mistakes."
Jingzhen (Ginger) Yang, PhD, MPH - Associate Professor, Department of Social and Behavioral Sciences, College of Public Health, Kent State University