Course Level: Beginner
Format Requirements: Didactic lectures and interactive discussion of theory, potential confounders and limitations, and statistical methods, using case study examples from published medical literature, and presentation of applications using Meta-Analyst, an open-source and free meta-analysis program developed by the authors and their colleagues.
Background: Meta-analysis is a formal, systematic method to synthesize the results of independent studies, considering and integrating the combined weight of evidence to determine the effect of an intervention or the strength of an association. Meta-analysis is being used increasingly in the medical and health sciences to augment traditional methods of narrative research to inform and guide practice and policy, in areas as disparate as estimating the effectiveness of mammography in detection of breast cancer and the consistency of gene-disease association studies.
Description and Objectives: Course Description: This workshop will provide a historical perspective of meta-analysis, and discuss methodological issues such as various types of bias and heterogeneity on the conduct and interpretation of meta-analyses. There will be extensive discussion of the appropriateness and use of statistical methods for combining data across studies, including nonparametric and parametric models; effect sizes for proportions, fixed versus random effects, regression and ANOVA models; multivariate models for proportions and standardized mean differences, treatment of zero cells, models with missing data, and special methods and issues in genetic applications.
The objectives are to:
- Understand the potential value of and theory underlying the conduct of meta-analysis of independent studies.
- Understand conditions under which meta-analyses can be performed and common factors that limit or confound the meta-analysis conduct and interpretation.
- Learn and understand a range of statistical methods for analyzing and interpreting meta-analysis studies.