R for HTA 2022 Workshop - Erin Barker
Channel: R-HTA consortium
Date: 2022-06-01

Erin Barker

Title:
MAIC-ing it easy: R for matching-adjusted indirect comparisons

Abstract:
Background
Matching-adjusted indirect comparisons (MAICs) are becoming increasingly common, and necessary, in HTA submissions.  As a result, the National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) released a Technical Support Document (TSD) providing guidance on the reliability and use of MAICs in the context of NICE appraisals.

Analysis
The R package “maic” can be used to conduct the steps required for a MAIC.  This package includes functions that replicate the processes described in the NICE TSD 18.  In this example, we use the “maic” package, simulated individual patient data (IPD) and aggregate data from a published paper to perform a MAIC.  The analysis is anchored and considers a continuous outcome.  The steps include data preparation and cleaning, generating individual patient weights, weight diagnostics and calculating an adjusted outcome from the IPD.  Finally, the adjusted outcome is used to estimate the relative treatment effect.  Confidence intervals provide an indication of the degree of uncertainty of the estimate.  Considerations and limitations are acknowledged and discussed throughout.

Discussion
There are clear advantages of MAICs including the reduction of heterogeneity between trials and the ability to conduct both anchored and unanchored analysis.  However, there are also limitations to their applicability such as data availability (IPD and baseline variables), sufficient trial overlap and reduction of the effective sample size.  The process for conducting the analysis can be made easier using the “maic” package.  Nevertheless, the methods for performing MAICs are still evolving and are a key area for further research.