This four-day workshop provides the knowledge and skills needed to start conducting and reporting the results of meta-analyses. An emphasis is placed on the two primary types of meta-analysis: fixed-effects versus random-effects models. Each topic will be introduced at the conceptual level, followed by worked examples and exercises so that participants will feel comfortable performing these analyses on their own. All sessions include time for an extensive discussion.

Topics Covered
Introduction to meta-analysis , Goals of a meta-analysis when effect-size is consistent across studies , Goals of a meta-analysis when effect-size varies across studies , Meta-Analysis where effect-size is consistent across studies (Dichotomous outcome) , Analysis where effect size is consistent across studies (Continuous outcome) , Goals of a meta-analysis when effect-size is consistent and when effect-size varies , Understanding the fixed-effect vs. the random-effects models , How to select and understand the effect-size index , Effect-sizes vs. p-values , What studies to include in the analysis , Heterogeneity in effects , The I-squared index , The prediction interval , The confidence interval , Common mistakes related to heterogeneity , How to quantify and report heterogeneity , The most common mistake in meta-analysis , Odds ratio , How to perform and report a meta-analysis , Dichotomous outcome , Incidence , Prevalence , Risk in one group , Correlation , A meta-analysis for Covid , Books , Papers , Websites , Model Assumptions , Calculating Fixed-Effect Models , Interpreting Results , Software Implementation , Examples , Overview of Meta-Analysis , Fixed-effect , Random-effects , Mean Difference , Tamiflu , Goals , Calculating Random-Effects Models , Definition of Heterogeneity , Sources of Heterogeneity , Statistical Tests , Interpreting Heterogeneity , Software Tools , Risk ratio , Statins , Writing Results , Formatting Reports , Common Pitfalls , Best Practices , Purpose of Subgroup Analyses , Identifying Subgroups , Conducting Analyses , Introduction to Meta-Regression , Model Building , Interpreting Coefficients , Understanding Outputs , Statistical Significance , Practical Significance , Graphical Representations , Reporting Standards , Definition of Publication Bias , Detecting Bias , Statistical Methods , Types of Biases , Detection Methods , Mitigation Strategies , Setting Up Models , Running Analyses , Interpreting Outputs , Saving Results , Data Import , Data Cleaning , Data Transformation , Handling Missing Data , Exporting Data , Setting Up Tests , Software Installation , Basic Navigation , Data Input , Initial Analyses , Saving Projects , Setting Up Subgroups , Introduction to IPD Meta-Analysis , Data Collection , Introduction to Network Meta-Analysis , Exercise using an analysis where the effect-size is consistent across studies , Introduction to meta-analysis and its importance in research , Heterogeneity , I-squared , Prediction intervals , Common Mistakes , Subgroups with incidence or prevalence , Subgroups in a Covid Analysis , Using subgroup analysis to explain heterogeneity in effects , Subgroups with correlations , Subgroups with binary outcomes , Meta-regression for correlations , Meta-regression for a covid analysis , Meta-regression for incidence or prevalence , Meta-regression for the risk ratio , Meta-regression using the standardized difference in means , Multiple outcomes based on same subjects , Multiple timepoints based on same subjects , Independent subgroups within a study , Publication bias for the standardized difference in means , Publication bias for correlations , Understanding publication bias , The small-study effect , Publication bias for the risk ratio