- From www.edx.org
CERTaIN: Knowledge Synthesis: Systematic Reviews and Clinical Decision Making
- Self-paced
- Free Access
- Fee-based Certificate
- 3 Sequences
- Introductive Level
Course details
Syllabus
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- How to search published and unpublished materials on topic of interest in a systematic and repeatable manner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
Prerequisite
Introduction course (CERTaIN.1x) recommended, but not required.
Instructors
Maria E. Suarez-Almazor, MD, PhD
Barnts Family Distinguished Professor
The University of Texas MD Anderson Cancer Center
Maria A. Lopez-Olivo, MD, PhD
Assistant Professor
The University of Texas MD Anderson Cancer Center
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