This module builds on the principles of systematic review and meta-analysis covered in the core MSc modules to provide you with the statistical knowledge and tools to perform meta-analyses based on data from clinical trials. We will cover the statistical principles of meta-analysis, including fixed-effect and random-effects models, and you will utilise these models to meta-analyse continuous, binary and time-to-event data. You will learn how to assess and investigate heterogeneity as well as exploring the impact that bias can have in a meta-analysis. In addition, we will cover three advanced topics: patient-level interactions, network meta-analysis and individual participant data meta-analysis.   

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

Perform fixed-effect and random-effects meta-analyses, interpret results, and understand the different model assumptions
	Assess the degree of heterogeneity in published data and suitably explore reasons for this
	Know how bias can impact a meta-analysis and assess risk of bias in a meta-analysis
	Understand the difference between a multivariate meta-analysis and a univariate meta-analysis and be able to estimate within-trial interactions using suitable methods
	Understand the difference between individual participant data (IPD) and aggregate data, and know how to conduct a meta-analysis using IPD
	Combine direct and indirect evidence on treatment comparisons using network meta-analysis, and interpret results
	Fit a variety of meta-analysis models in Stata, producing forest plots and other graphical summaries
This module is compulsory for students on the Statistics for Clinical Trials route of the MSc Clinical Trials.

Schmid CH, Stijnen T, White IR. Handbook of Meta‐Analysis. Boca Raton, FL: Chapman and Hall/CRC Press; 2020 (available UCL library)

Salanti G, Higgins JPT, Ades AE, Ioannidis JPA. ‘Evaluation of networks of randomized trials’. Statistical Methods in Medical Research 17: 279-301; 2008.

Riley, RD, Debray, TP, Fisher, D, et al. ‘Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning’. Statistics in Medicine. 2020; 39: 2115– 2137

Fisher DJ, Carpenter JR, Morris TP, Freeman SC, Tierney JF. ‘Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?’ BMJ 2017; 356 :j573 doi:10.1136/bmj.j573

Burke DL, Ensor J, Riley RD. ‘Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ’. Stat Med. 2017 Feb 28;36(5):855-875. doi: 10.1002/sim.7141.

Morris, TP, Fisher, DJ, Kenward, MG, Carpenter, JR. ‘Meta‐analysis of Gaussian individual patient data: Two‐stage or not two‐stage?’ Statistics in Medicine. 2018; 37: 1419– 1438. https://doi.org/10.1002/sim.7589