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December 2020 The statistical performance of matching-adjusted indirect comparisons: Estimating treatment effects with aggregate external control data
David Cheng, Rajeev Ayyagari, James Signorovitch
Ann. Appl. Stat. 14(4): 1806-1833 (December 2020). DOI: 10.1214/20-AOAS1359

Abstract

Indirect comparisons of treatment-specific outcomes across separate studies often inform decision making in the absence of head-to-head randomized comparisons. Differences in baseline characteristics between study populations may introduce confounding bias in such comparisons. Matching-adjusted indirect comparison (MAIC) (Pharmacoeconomics 28 (2010) 935–945) has been used to adjust for differences in observed baseline covariates when the individual patient-level data (IPD) are available for only one study and aggregate data (AGD) are available for the other study. The approach weights outcomes from the IPD using estimates of trial selection odds that balance baseline covariates between the IPD and AGD. With the increasing use of MAIC, there is a need for formal assessments of its statistical properties. In this paper we formulate identification assumptions for causal estimands that justify MAIC estimators. We then examine large sample properties and evaluate strategies for estimating standard errors without the full IPD from both studies. The finite-sample bias of MAIC and the performance of confidence intervals based on different standard error estimators are evaluated through simulations. The method is illustrated through an example comparing placebo arm and natural history outcomes in Duchenne muscular dystrophy.

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David Cheng. Rajeev Ayyagari. James Signorovitch. "The statistical performance of matching-adjusted indirect comparisons: Estimating treatment effects with aggregate external control data." Ann. Appl. Stat. 14 (4) 1806 - 1833, December 2020. https://doi.org/10.1214/20-AOAS1359

Information

Received: 1 August 2019; Revised: 1 April 2020; Published: December 2020
First available in Project Euclid: 19 December 2020

MathSciNet: MR4194249
Digital Object Identifier: 10.1214/20-AOAS1359

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 4 • December 2020
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