A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis

Background: Stratified medicine seeks to identify patients most likely to respond to treatment. Individual participant data (IPD) network meta-analysis (NMA) models have greater power than individual trials to identify treatment-covariate interactions (TCI). TCI contain “within” and “across” trial interactions, where the across trial interaction is more susceptible to confounding and ecological bias. Methods: We considered a network of IPD from 37 trials (5922 patients) for cervical cancer (2394 events), where previous research identified disease stage as a potential interaction covariate. We compare two models for NMA with TCI: (i) two effects separating within and across trial interactions and (ii) a single effect combining within and across trial interactions. We argue for a visual assessment of consistency of within and across trial interactions and consider more detailed aspects of interaction modelling, e.g. common vs trial-specific effects of the covariate. This leads us to propose a practical framework for IPD NMA with TCI. Results: Following our framework, there was no evidence in the cervical cancer network for a treatment-stage interaction based on the within trial interaction. The NMA provided additional power for an across trial interaction over and above the pairwise evidence. Following our proposed framework, the within and across trial interactions should not be combined. Conclusion: Across trial interactions are susceptible to confounding and ecological bias. It is important to separate the sources of evidence to check their consistency and identify which sources of evidence are driving the conclusion. Our framework provides practical guidance for researchers, reducing the risk of unduly optimistic interpretation of TCI.