OBJECTIVES: Matching-adjusted indirect comparison (MAIC) uses individual patient data from trials for one comparator to adjust for cross-trial differences from trials of another comparator to yield a relative treatment effect across balanced trial populations. MAIC is also a preferred approach over network meta-analyses since it can accommodate biases in cross-trial patient populations, differences in outcome measures definitions and robustness to modeling specifications. We aimed to review the trends in the use of this methodology in published literature and NICE Technology Assessments (TAs). METHODS: A systematic review was conducted in EMBASE and MEDLINE to identify publications using ‘matching-adjusted indirect comparison’ and ‘MAIC’ keywords for years 2010 through current. The NICE website was also queried for published TAs using MAICs from 2000 to current. Methodological and review articles were excluded. RESULTS: Thirty-two publications were identified in the literature employing MAICs, five in 2011, four in 2012, four in 2013, five in 2014, 13 in 2015 and one in 2016. Additionally, four NICE TAs employed MAIC to support submission, one in 2012, one in 2014 and two in 2015. Majority of the publications were in oncology, specifically for advanced, relapsed/ refractory and poor prognosis cancer subtypes. The methodology was also frequently used to assess relative treatment effect in country-specific sub-populations. Among the four NICE TAs employing MAICs, three were single TAs and all three drugs received marketing authorization.  The fourth TA, which was a multiple TA, did not have favorable outcome for one drug. CONCLUSIONS: An increased trend was found in the use of MAIC in published literature as well as NICE TA submissions.  This methodology was also well received by NICE TAs. Overall, MAICs can provide comparative evidence to enable informed policy decisions.