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Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research.

James E Signorovitch¹, Vanja Sikirica, M Haim Erder

¹Analysis Group, Inc., Boston, MA 02199, USA. jsignorovitch@analysisgroup.com

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research

|September 25, 2012

Summary

Indirect treatment comparisons using aggregate data can be biased. Incorporating individual patient data (IPD) with matching-adjusted indirect comparisons (MAICs) reduces cross-trial differences, providing reliable comparative evidence when head-to-head trials are unavailable.

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Area of Science:

Health Economics and Outcomes Research
Biostatistics
Clinical Trial Design

Background:

Head-to-head randomized trials are the gold standard for treatment comparison but are not always available.
Indirect treatment comparisons using aggregate data are susceptible to biases from cross-trial differences in patient populations, modeling assumptions, and outcome definitions.

Purpose of the Study:

To demonstrate how incorporating individual patient data (IPD) into indirect comparisons can overcome limitations inherent in aggregate data analyses.
To introduce and illustrate the application of matching-adjusted indirect comparisons (MAICs).

Main Methods:

Matching-adjusted indirect comparisons (MAICs) utilize IPD from one treatment arm to match baseline characteristics of another treatment arm.
Propensity score-like weighting is employed to balance trial populations before comparing treatment outcomes.
The method's application is demonstrated through a review of existing MAICs and a novel analysis in attention deficit/hyperactivity disorder.

Main Results:

MAICs effectively address limitations of aggregate data indirect comparisons by reducing observed cross-trial differences in patient populations and outcome measures.
The method enhances the reliability of comparative treatment evidence, as illustrated in various therapeutic areas and a specific ADHD analysis.
A key assumption is the absence of unobserved confounders between non-randomized treatment groups.

Conclusions:

Matching-adjusted indirect comparisons (MAICs) offer a robust approach to mitigate biases in indirect treatment comparisons.
By integrating IPD with aggregate data, MAICs provide timely and more reliable comparative evidence for decision-making when direct trial data is lacking.