Addressing challenges with Matching-Adjusted Indirect Comparisons to demonstrate the comparative effectiveness of...

Matching Adjusted Indirect Comparison (MAIC) is a statistical method used to adjust for potential biases when comparing treatment effects between separate data sources, with aggregate data in one arm, and individual patients data in the other. However, acceptance of MAIC in health technology assessment (HTA) is challenging because of the numerous biases that can affect the estimates of treatment effects - especially with small sample sizes, increasing the risk of convergence issues. We suggest statistical approaches to address some of the challenges in supporting evidence from MAICs, applied to a case study.

The proposed approaches were illustrated with a case study comparing an integrated analysis of three single-arm trials of entrectinib with the French standard of care using the Epidemio-Strategy and Medical Economics (ESME) Lung Cancer Data Platform, in metastatic ROS1-positive Non-Small Cell Lung Cancer (NSCLC) patients. To obtain convergent models with balanced treatment arms, a transparent predefined workflow for variable selection in the propensity score model, with multiple imputation of missing data, was used. To assess robustness, multiple sensitivity analyses were conducted, including Quantitative Bias Analyses (QBA) for unmeasured confounders (E-value, bias plot), and for missing at random assumption (tipping-point analysis).

The proposed workflow was successful in generating satisfactory models for all sub-populations, that is, without convergence problems and with effectively balanced key covariates between treatment arms. It also gave an indication of the number of models tested. Sensitivity analyses confirmed the robustness of the results, including to unmeasured confounders. The QBA performed on the missing data allowed to exclude the potential impact of the missing data on the estimate of comparative effectiveness, even though approximately half of the ECOG Performance Status data were missing.

To the best of our knowledge, we present the first in-depth application of QBA in the context of MAIC. Despite the real-world data limitations, with this MAIC, we show that it is possible to confirm the robustness of the results by using appropriate statistical methods.

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