Validating the assumptions of population adjustment: application of multilevel network meta-regression to a network of treatments for plaque psoriasis

Background: Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest, but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from one or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This paper aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population.

Methods: We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for three external target populations represented by a registry and two cohort studies. We examine residual heterogeneity and inconsistency, and relax the shared effect modifier assumption for each covariate in turn.

Results: Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid.

Conclusions: ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesises evidence from IPD and AgD studies in networks of any size whilst avoiding aggregation bias and non-collapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making.