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About the Workshop  »  Learning Outcomes

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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

After Day-2, you should understand:

  • How to quantify and interpret heterogeneity
  • 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


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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.

 
 
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