• Conduct systematic reviews and meta-analyses using academic databases and interpret and report results
• Define strategies for critically appraising the literature
• Describe economic evaluation methods
• Describe how decisions can be made using clinical practice guidelines

Overview of Systematic Reviews

• Knowledge synthesis and the knowledge-to-action process
• Benefits and limitations of systematic reviews
• Steps of a systematic review and levels of evidence

Finding and Managing the Evidence from the Biomedical Literature

• How to search published and unpublished materials on topic of interest in a systematic and repeatable manner

Intervention Reviews Methodology

• How to select and appraise studies
• Strategies for collecting and analyzing data
• How to report and update systematic reviews

Meta-Analysis of Clinical Trials: Direct Comparisons

• Qualitative and quantitative synthesis
• Models to use during data analysis
• Heterogeneity
• Reporting methods for meta-analyses

Introduction to Meta-Analysis: Indirect Comparisons

• Indirect comparisons
• Evidence networks
• Effect modifiers and how they are a source of bias

Meta-Analysis of Non-Randomized Studies

• Risks of bias in non-randomized studies
• Meta-regression as a tool to reduce risk of bias
• Caveats of meta-regression
• Reporting meta-analyses of non-randomized studies

Diagnostic Test Evaluation

• Purposes for medical testing
• Precision and accuracy
• Measures of validity for diagnostic/screening tests
• Likelihood ratio for analysis of test performance
• ROC curves for analysis of test performance

Meta-Synthesis

• Methods for collecting and synthesizing data
• Reporting of meta-synthesis findings

Clinical Practice Guidelines

• Process of guideline development
• How evidence is evaluated
• How clinical practice guidelines are evaluated and revised

Economic Evaluation

• Clinical decision analysis and economic evaluation
• Costs and methods for economic evaluation
• Cost-effectiveness and cost analysis
• Steps for economic evaluation

Decision Analysis for Outcomes Research

• Decision analysis and modeling
• Markov modeling
• Capturing of uncertainty in models
• Examples of decision modeling in cancer outcomes research