A worked example of matching-adjusted indirect comparison using Stata

Matching-adjusted indirect comparison is a comparative effectiveness research methodology that leverages individual level data and aggregate results when head-to-head randomized trials are not available or feasible. MAIC is growing in popularity partly because of the high costs of randomized trials and because of interest on the part of regulators for more safety and effectiveness evidence. Since the seminal papers describing the theory and application of MAIC were published just over a decade ago, the literature on how to apply this method as well as demonstrations of its applications has grown quickly. The National Institute for Health Care Excellence (NICE) in the UK released a technical document in 2016 that described MAIC best practices and provided sample R code for an example analysis. As the method has become more popular, references to the use of Stata for statistical analysis are appearing in publications yet very little documentation or code is available. We present the NICE technical documentation worked example using Stata in parallel to the original example in R and highlight the efficiencies and potential challenges of both programs.