Randomized controlled trials (RCTs) are currently the gold standard for the treatment effect comparisons; however, it is sometimes not feasible to conduct an RCT due to ethical and economic reasons. In the absence of evidence for head-to-head RCT direct comparison, the indirect comparison technique is an effective and resource-saving alternative. Matching-adjusted indirect comparison (MAIC) is an attractive method in the field of population-adjusted indirect comparisons between two trials. It can adjust for between-trial imbalances in the distribution of observed covariates by weighting the available individual patient data of the studied intervention and then match the aggregated data of the controlled intervention. Subsequently, the treatment effect comparison can be evaluated through the post-matched population. Although MAIC is gaining increasing attention in clinical research, especially in the evaluation of new drugs, efforts are still largely required for knowledge dissemination in China. In this paper, we briefly introduced the concepts, research value and examples, and pros and cons of MAIC.
When there is a lack of head-to-head randomized controlled trials between two interventions of interest, indirect comparison methods can be employed to estimate their relative treatment effects. Matching-adjusted indirect comparison (MAIC) is a population-adjusted indirect comparison method that utilizes a weighting approach. Unanchored MAIC is particularly applicable in scenarios where a common control group between the two interventions is not available. This article introduces the background and mathematical theory of unanchored MAIC, along with a demonstration of the operational steps and interpretation of results through an application example.
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.
Objective To provide methodological guidance for the application of matching-adjusted indirect comparison (MAIC). Methods The methodology literature on MAIC was examined to clarify key steps and methodological points, and MAIC application literature in the non-small cell lung cancer field published after January 2016 was systematically reviewed to compare and analyze the current status and problems of MAIC. Results MAIC consisted of five key steps: data source and sample selection, matching variable screening, individual weight calculation, matching validity evaluation, and relative efficacy calculation. The systematic review revealed that studies primarily employed literature reviews to screen data sources, used statistical analysis and other scientific methods to screen matching variables, employed software for individual weight calculation, evaluated matching validity by reporting effective sample size (ESS), calculated relative efficacy using Cox, logistic, and other models, conducted sensitivity analyses to evaluate the uncertainty caused by different data sources and matching variable combinations, and the studies demonstrated good overall reporting standardization but significant differences in particular aspects. Concerning the connection between MAIC and pharmacoeconomic research, studies included mainly used target drugs as the reference group of survival data extrapolation, and proportional hazards (PH) assumptions were considered when utilizing hazard ratios (HR) in extrapolation. Conclusion There are some deficiencies in the method application and reporting standards of MAIC research, such as lack of explanation of data source selection criteria and matching variable screening criteria, insufficient reporting of weight distribution, and inadequate consideration of PH assumptions. It is recommended that future MAIC research systematically screen data sources and report covariate distribution evaluation, covariate status evaluation, weight distribution, uncertainty measurement, etc. Additionally, considering PH assumptions after calculating HR is suggested.