Identifying who benefits most from treatments: estimating interactions and subgroup effects in aggregate data meta-analysis

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 will be presented. The presenter will describe the steps involved and apply the methods to two examples taken from previously published meta-analyses, in which detailed aggregate data were available.

The webinar is 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 will be covered, but they will not be the sole focus of the talk.

The format of the webinar will be 40 minute presentation with 20 minutes for questions and answers. 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

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 my main methodological focus is statistical methods for interactions (subgroup analysis).

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Monday, 29th January 2024 14:00 UTC [check the time in your timezone] SIGN UP HERE

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