Learning Outcomes | Meta-Analysis Workshops

Overview		Agenda		Learning Outcomes		Who Should Attend		Testimonials

About the Workshop » Learning Outcomes

Download a PDF

After Day-1, you should understand:

What a meta-analysis is, and how to perform one
The key differences between meta-analysis and narrative reviews
That the goal of a meta-analysis is to synthesize, and not simply combine, effect sizes
The difference between fixed-effect and random-effects models
How to use a forest-plot to understand and report a meta-analysis
How to use CMA to compute effect sizes, perform a simple analysis, and create forest plots
Common mistakes in meta-analysis, and how to avoid them

Mistakes in choosing between fixed-effect and random-effects models
Mistakes in understanding why a meta-analysis appears to conflict with a clinical trial
Mistakes in using Vote-counting
Mistakes in the goals of meta-analysis

How to quantify and interpret heterogeneity

After Day-2, you should understand:

How to compare the effect size in subgroups of studies
How to use regression to assess the relationship between covariates and effect size
Common mistakes in meta-analysis, and how to avoid them
- Mistakes in interpreting indices of heterogeneity
- Mistakes in choosing between fixed-effect and random-effects models for subgroups-analysis and meta-regression

After Day-3, you should understand:

How to work with studies that report effects for two (or more) independent subgroups
How to work with studies that report effects for two (or more) outcomes or time-points
How to work with studies that compare two (or more) treatments to a common control group
How to decide whether or not it makes sense to perform a meta-analysis
How to assess the potential impact of publication bias
How to perform a meta-analysis using studies that employed different designs (matched groups vs. independent groups), formats (some reported means, others reported t-tests) or outcomes (some worked with means, others with risks).
How to use CMA to perform all of these analyses
Common mistakes in meta-analysis, and how to avoid them
- Mistakes in working with multiple outcomes from the same sample
- Mistakes in interpreting publication bias

« Previous Back to Top Next »

Testimonials

Excellent introduction to the many features of meta-analysis.

Stephen J. Ganocy, PhD, Dept. of Psychiatry, Case Western Reserve University

CMA is an incredible software package for the novice and experienced researcher alike. It offers a sophisticated array of analytical techniques, yet retains the flexibility to adapt to your specific application. The interface is intuitive and user-friendly, and frees the user to focus on the analysis rather than the software. I use CMA in my graduate biostatistics course and recommend it to all of my faculty colleagues as well. The software is a joy to use!

Dr. Michael W. Hubble, Associate Professor, Graduate and Distance Program, Director, Emergency Medical Care Program, Western Carolina University, Cullowhee, NC

The meta-analysis program is easy to use and provides me with information that would be hard to get by any other means. I analyze a lot of data and, believe it or not, use the meta-analysis program every day. I am not a statistician, but the results I produce have routinely been verified by statistical consultants costing large amounts for their confirmatory analyses. Without verification, I have used the results to make important decisions regarding a potential billion dollar a year product. At night I wonder, "What would I have done without this great program.

Louis Fabre MD, PhD, Chairman, Fabre-Kramer Pharmaceuticals Inc.

Let me know when workshops are added: