Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: Individual patient-level covariates versus aggregate trial-level covariates

摘要

Mixed treatment comparison (MTC) meta-analysis allows several treatments to be compared in a single analysis while utilising direct and indirect evidence. Treatment by covariate interactions can be included in MTC models to explore how the covariate modifies the treatment effects. If interactions exist, the assumptions underlying MTCs may be invalidated. For conventional pair-wise meta-analysis, important benefits regarding the investigation of such interactions, gained from using individual patient data (IPD) rather than aggregate data (AD), have been described. We aim to compare IPD MTC models including patient-level covariates with AD MTC models including study-level covariates. IPD and AD random-effects MTC models for dichotomous outcomes are specified. Three assumptions are made regarding the interactions (i.e. independent, exchangeable and common interactions). The models are applied to a dataset to compare four drugs for treating malaria (i.e. amodiaquine-artesunate, dihydroartemisinin-piperaquine (DHAPQ), artemether-lumefantrine and chlorproguanil-dapsone plus artesunate) using the outcome unadjusted treatment success at day 28. The treatment effects and regression coefficients for interactions from the IPD models were more precise than those from AD models. Using IPD, assuming independent or exchangeable interactions, the regression coefficient for chlorproguanil-dapsone plus artesunate versus DHAPQ was statistically significant and assuming common interactions, the common coefficient was significant; whereas using AD, no coefficients were significant. Using IPD, DHAPQ was the best drug; whereas using AD, the best drug varied. Using AD models, there was no evidence that the consistency assumption was invalid; whereas, the assumption was questionable based on the IPD models. The AD analyses were misleading.