A key question for meta-analysis is to reliably assess whether treatment effects vary across different participant subgroups (a so-called interaction). In addition, estimates of subgroup effects (effect of a treatment within specific covariate subgroups) is key information for clinical decision making to target treatments appropriately – which estimation of an interaction effect itself does not provide.

In this Cochrane Learning Live webinar, a new framework for estimating interactions and subgroup effects in aggregate data meta-analysis was presented. The presenter described the steps involved and applied the methods to two examples taken from previously published meta-analyses, in which detailed aggregate data were available.

The webinar was aimed at those with some statistical knowledge, but not necessarily statisticians only. This includes review authors who are considering carrying out a subgroup analysis in their review or who have already carried one out. Statistical concepts were covered, but were not the sole focus of the talk.

The content of the webinar is based on an Open Access paper: Godolphin, PJ, White, IR, Tierney, JF, Fisher, DJ. Estimating interactions and subgroup-specific treatment effects in meta-analysis without aggregation bias: A within-trial framework. Res Syn Meth. 2023; 14(1): 68-78. doi:10.1002/jrsm.1590

The session was delivered in January 2024 and below you will find the videos from the webinar, together with the accompanying slides to download [PDF].

Part 1: Presentation
Part 2: Questions & answers

Presenter Bio

Dr Peter Godolphin is a statistician working in meta-analysis, both on applied projects and meta-analysis methodology. He has carried out meta-analyses predominately in COVID-19 and advanced prostate cancer, using both aggregate data and individual participant data and his main methodological focus is statistical methods for interactions (subgroup analysis).