Healthcare decision-making requires reliable estimates of relative treatment effects. In an ideal scenario, these are provided by high-quality randomised controlled trials (RCTs) comparing the treatments of interest, in a relevant target population. However, it is often the case that head-to-head RCTs are not available between all relevant treatments. Instead, standard network meta-analysis and indirect comparison methods can be used to estimate relative treatment effects between treatments of interest by combining aggregate data from multiple studies, assuming that any variables that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods aim to relax this assumption by adjusting for differences in effect modifiers using available individual patient data from one or more trials.

In this talk, I will give an overview of different population adjustment methods including Matching Adjusted Indirect Comparison, Simulated Treatment Comparison, and a new approach, Multilevel Network Meta-Regression. These methods will be illustrated using an applied example, and I will discuss the results of an extensive simulation study designed to assess the performance of the methods in a range of realistic scenarios under various failures of assumptions.

This is a free event, which will be taking place online via Zoom. To register your interest in attending this talk please visit: oxford.onlinesurveys.ac.uk/herc-webinar-dphillippo-bristol