Systematic reviews and meta-analysis of individual participant data (IPD)

''It is the platinum standard of meta-analysis.''

Systematic reviews and meta-analyses are an important cornerstone of contemporary evidence-based medicine. The large majority summarize published aggregate data, but it is increasingly common that individual participant data (IPD) are obtained from primary studies. As a result, new opportunities arise and more advanced statistical methods are needed to properly analyze the available data. In this course, we discuss how a meta-analysis involving IPD may help to identify sources of heterogeneous treatment effects, to investigate the accuracy of diagnostic tests, to develop clinical prediction models and to externally validate such models. We place particular emphasis on statistical methods for dealing with between-study heterogeneity, and discuss how to interpret corresponding results.

Entry requirements: 

In this course, we expect participants to have a basic knowledge about the principles of intervention research, diagnostic research, prognostic research, systematic reviews and meta-analysis. Furthermore, basic knowledge of R is helpful (but not required).

Start date(s): 
26 June 2023
Time investment: 
Five full working days
University Medical Center Utrecht
Faculty of Medicine
€ 890
Extra information: 

The course consists of plenary presentations, small-group discussions, reading assignments, and computer exercises. The course ends with an exam. 

At the end of the course, you will be able to: 

  • Explain the rationale for performing an individual participant data meta-analysis (IPD-MA)
  • Understand the advantages, limitations and key characteristics of IPD-MA in intervention, diagnostic and prognostic research
  • Understand the relevance of between-study heterogeneity, and be familiar with statistical methods for investigating and reporting this.
  • Be familiar with statistical methods for summarizing relative treatment effects and exploring the presence of treatment-covariate interaction
  • Be familiar with statistical methods for developing and validating clinical prediction models using IPD from multiple studies or settings
  • Be familiar with statistical methods for investigating and comparing diagnostic test accuracy using IPD
  • Interpret and critically appraise the results from an IPD-MA


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