Elsevier

Value in Health

Volume 15, Issue 6, September–October 2012, Pages 940-947
Value in Health

Original research
Comparative effectiveness research/health technology assessment
Matching-Adjusted Indirect Comparisons: A New Tool for Timely Comparative Effectiveness Research

https://doi.org/10.1016/j.jval.2012.05.004Get rights and content
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Abstract

Objective

In the absence of head-to-head randomized trials, indirect comparisons of treatments across separate trials can be performed. However, these analyses may be biased by cross-trial differences in patient populations, sensitivity to modeling assumptions, and differences in the definitions of outcome measures. The objective of this study was to demonstrate how incorporating individual patient data (IPD) from trials of one treatment into indirect comparisons can address several limitations that arise in analyses based only on aggregate data.

Methods

Matching-adjusted indirect comparisons (MAICs) use IPD from trials of one treatment to match baseline summary statistics reported from trials of another treatment. After matching, by using an approach similar to propensity score weighting, treatment outcomes are compared across balanced trial populations. This method is illustrated by reviewing published MAICs in different therapeutic areas. A novel analysis in attention deficit/hyperactivity disorder further demonstrates the applicability of the method. The strengths and limitations of MAICs are discussed in comparison to those of indirect comparisons that use only published aggregate data.

Results

Example applications were selected to illustrate how indirect comparisons based only on aggregate data can be limited by cross-trial differences in patient populations, differences in the definitions of outcome measures, and sensitivity to modeling assumptions. The use of IPD and MAIC is shown to address these limitations in the selected examples by reducing or removing the observed cross-trial differences. An important assumption of MAIC, as in any comparison of nonrandomized treatment groups, is that there are no unobserved cross-trial differences that could confound the comparison of outcomes.

Conclusions

Indirect treatment comparisons can be limited by cross-trial differences. By combining IPD with published aggregate data, MAIC can reduce observed cross-trial differences and provide decision makers with timely comparative evidence.

Keywords

comparative effectiveness
individual patient data
matching-adjusted indirect comparison

Cited by (0)

We declare the following potential conflicts of interest: Vanja Sikirica, M. Haim Erder, and Paul S. Hodgkins are employees of Shire Pharmaceuticals, Inc., and hold stock options in Shire Pharmaceuticals, Inc. James E. Signorovitch, Jipan Xie, Mei Lu, Keith A. Betts, and Eric Q. Wu are employees of Analysis Group, Inc., a consultant for Shire Pharmaceuticals, Inc.