Meta-Analysis for Public Health and Biomedical Research Using R
Boston University School of Public Health - Summer Institute
Meta-Analysis is the gold standard statistical approach to combine the results of multiple studies and to examine sources of heterogeneity and potential biases. This program will review fixed-effect and random-effects models that underlie the combination of study results in meta-analysis; the use of study-level predictors in meta-regression; assessment of small-study effects and related reporting biases; and sensitivity analyses to bias. Examples will cover meta-analysis of randomized trials and of observational studies. Throughout the program, participants will apply each model by using the R software.
Participants will learn to:
- Utilize the fixed-effect and random-effects methods of combining effect sizes;
- Describe different ways to measure between-study heterogeneity;
- Describe the strengths and weaknesses of random-effects as compared to fixed effect meta-analysis;
- Assess the potential impact of small-study effects and related reporting biases on a combined effect size estimate;
- Perform meta-regression modeling and describe the limitations of meta-regression
The target audience includes biostatisticians, data analysts, and quantitative researchers from academia, the pharmaceutical industry, and other government institutions.
Basic knowledge of study design and regression modeling and basic working knowledge of R are necessary to be successful in the program. Participants must bring a laptop to the class sessions.