Meta-analysis

2x1

MSc in Evidence-Based Healthcare Fostering lifelong learning and critical thinking about evidence for improved health

Basic and advanced statistical methods for meta-analysis

The Meta-analysis short course for health professionals is designed to provide an overview of different meta-analysis methods and common problems encountered with extracting data. Basic and advanced methods which can be used to combine data from various study types will be covered using Review Manager and Stata software.

Topics covered will include standard methods for intervention comparisons, approaches which can be used for combining different summary measures, subgroup analyses and methods to investigate heterogeneity, as well as advanced methods for diagnostic accuracy, individual patient data and network meta-analysis.

The last date for receipt of complete applications is 5pm Friday 24th February 2017. Regrettably, late applications cannot be accepted.

The overall aims of this module are to enable students to:

  • Be able to plan, carry out and interpret meta-analysis of different study designs for questions in evidence-based healthcare.
  • Extract data in different formats and deal with missing data
  • Use three different software packages (Stata, Review Manager and R) to perform meta-analysis
  • Use methods to explore heterogeneity and appropriately use fixed and random effects, subgroup analysis, sensitivity analysis and meta-regression
  • Carry out and interpret cumulative meta-analysis, diagnostic accuracy meta-analysis and network meta-analysis.
  • Understand the advantages and limitations of an individual patient data meta-analysis

The course will cover the following topics:

  • Introduction to different meta-analysis methods and the advantages or disadvantages of each
  • Common problems encountered with data extraction
  • Heterogeneity, fixed and random effects, meta-regression, unit of analysis, follow-up and cross-over studies
  • Approaches to meta-analysis of different study designs
  • Cumulative meta-analysis
  • Diagnostic accuracy meta-analysis in Stata and RevMan
  • Introduction to meta-analysis using ‘R’ statistical package
  • Network meta-analysis
  • Introduction to individual patient data meta-analysis

For further information, please visit https://www.conted.ox.ac.uk/courses/meta-analysis.