In health technology assessment, existing covariate adjustment methods-such as network meta regression, matching-adjusted indirect comparison, and simulated treatment comparison often implicitly rely on the shared effect modification (SEM) assumption. When this assumption does not hold, systematic bias may arise. Network meta interpolation (NMI), by contrast, does not depend on the SEM assumption. Instead, it leverages the correlation structure of covariates, applying best linear unbiased prediction to impute unreported subgroup data, which are then used for regression analysis to obtain indirect comparison results. In this study, we conducted Monte Carlo simulations to evaluate the performance of NMI and network meta-analysis across three scenarios. The results showed that, regardless of whether the SEM assumption held, NMI produced estimates that were closer to the true values and confidence intervals with higher coverage. This method therefore offers an important complement for addressing covariate imbalance and non-shared effect modification in HTA, though caution is warranted in interpreting results due to the risks of multiple comparisons and inflated type I error.
With the increase in the number of single-arm clinical trials and lack of head-to-head clinical studies, the application of unadjusted indirect comparisons and network meta-analysis methods has been limited. Matching-adjusted indirect comparison (MAIC) is an alternative method to fully utilize individual patient data from one study and balance potential bias caused by baseline characteristics differences in different trials through propensity score matching with aggregated data reported in other studies, and complete the comparison of the efficacy between target interventions. This study introduced the concept and principles of MAIC. In addition, we demonstrated how to use the anchored MAIC method based on R language for survival data, which has been widely used in anti-cancer drug evaluation. This study aimed to provide an alternative method to inform evidence-based decisions.
