The Methods Group regularly holds training workshops on the use of IPD at Cochrane Colloquia and UK & Ireland-based Contributors Meetings


Details of any training workshops to be run at forthcoming Cochrane Colloquia will appear here, when available.

For details of past training workshops, please click on the following links:
For details of guidance and slide presentations on the use of IPD and FAQs, please visit the resources page

**Upcoming training (2 courses) **

"Statistical Methods for Meta-analysis of Individual Participant Data"

 24th - 26th April 2019

Led by Prof Richard D. Riley, Dr Joie Ensor and Dr Kym Snell

Research Institute for Primary Care and Health Sciences, KEELE UNIVERSITY




This three-day statistical course provides a detailed foundation of the methods and principles for meta-analysis when IPD (Individual Participant Data) are available from multiple related studies. The course considers continuous, binary and time-to-event outcomes, and covers a variety of modelling options, including fixed effect and random effects. Days 1 and 2 mainly focus on the synthesis of IPD from randomised trials of interventions, where the aim is to summarise a treatment effect or to examine treatment-covariate interactions. We outline how to use either a two-stage framework (day 1) or a one-stage framework (day 2) for the meta-analysis, and compare their pros and cons. Day 3 focuses on novel extensions including multivariate and network meta-analysis of IPD to incorporate correlated and indirect evidence (e.g. from multiple outcomes or multiple treatment comparisons). Special topics will also be covered, including: (i) IPD meta-analysis to identify prognostic/risk factors; (ii) IPD meta-analysis of test accuracy studies; (iii) estimating the power of a planned IPD meta-analysis; and (iv) dealing with unavailable IPD. The course consists of a mixture of lectures and practical sessions to reinforce the underlying statistical concepts. Participants can choose either Stata or R for the practicals. The key messages are illustrated with real examples throughout the course.


  • Understand the difference between IPD and aggregate data, and the rationale for an IPD meta-analysis of randomised trials
  • Recognise the challenges of setting up an IPD meta-analysis, but also the many potential advantages
  • Know how to conduct one-stage and two-stage fixed effect and random effects IPD meta-analyses
  • Understand how to model, explain and interpret heterogeneity between studies
  • Understand when and why one-stage and two-stage methods may differ
  • Appreciate how to derive percentage study weights in two-stage and one-stage IPD meta-analysis models
  • Recognise why it is essential to account for the clustering of participants within studies in an IPD meta-analysis
  • Know how to write-down and fit fundamental IPD meta-analysis models for continuous, binary and time-to-event outcomes
  • Understand how to estimate patient-level effect modifiers (treatment-covariate interactions, predictive markers) in an IPD meta-analysis, and why these are important for stratified medicine
  • Know the meaning of the terms publication bias, availability bias, and selection bias, and how to examine them
  • Understand evidence synthesis models for combining IPD studies with aggregate data from non-IPD studies
  • Understand meta-analysis models for identifying risk or prognostic factors using IPD from observational studies
  • Understand the difference between univariate and multivariate meta-analysis models
  • Recognise why multivariate methods are important for evidence synthesis of multiple outcomes
  • Appreciate the potential benefits of IPD for network meta-analysis of multiple treatments
  • Understand how IPD facilitates multivariate meta-analysis by deriving within-study correlations via bootstrapping
  • Appreciate the importance of multiple imputation and how it may be undertaken in an IPD meta-analysis
  • Recognise the importance of the PRISMA-IPD reporting guidelines
  • Recognise possible options for calculating the power of an IPD meta-analysis, in advance of collecting IPD
  • Gain experience at fitting key IPD meta-analysis models in the Stata software, through practical sessions including: one-stage IPD meta-analysis approaches; two-stage IPD meta-analysis approaches; estimation of treatment-covariate interactions; multivariate and network meta-analysis using IPD


The course is aimed at individuals that want to learn how to plan and undertake an IPD meta-analysis. We recommend that participants have a background in statistics as the course assumes a good understanding of core statistical principles and topics, such as regression methods (such as linear, logistic, and Cox), parameter estimation and interpreting software output. A familiarity with traditional aggregate data (non-IPD) meta-analysis methods is advantageous, though not essential. We also recommend that participants are familiar with Stata or R, although the practicals will not require individuals to write their own code. Participants must bring their own laptop with R installed. or with Stata 13 or above installed.


Registration for this course is £550 for students, £695 for public sector and £895 for private sector. The cost includes all sessions, lunch on all days, a pub meal on the evening of the 24th and a Gala Dinner on the evening of the 25th.


"Systematic Reviews and Meta-analysis of Individual Participant Data"

 25th - 29th June 2018


Julius Center for Health Sciences and Primary Care, UMC Utrecht

Coordinator: Thomas Debray



*This course is run annually*


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 can be conducted to investigate the comparative efficacy between different interventions, 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. The course consists of plenary presentations, small-group discussions, reading assignments, and computer exercises.


At the end of the course, the student 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

Prerequisite knowledge

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. Some basic knowledge of R is helpful (but not required).

Course format: Lectures, small group discussions, computer exercises, self-study

Assessment: 80% attendance and exam

Number of participants: 20

Fee: 830

To register for the course please visit: