Is the Use of Unanchored Matching-Adjusted Indirect Comparison Always Superior to Naïve Indirect Comparison on Survival Outcomes? A Simulation Study

Ying Liu, Xiaoning He, Jia Liu and Jing Wu ()
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Ying Liu: Tianjin University
Xiaoning He: Tianjin University
Jia Liu: Tianjin University
Jing Wu: Tianjin University

Applied Health Economics and Health Policy, 2025, vol. 23, issue 4, No 11, 693-704

Abstract: Abstract Objective To compare the performance of matching-adjusted indirect comparison (MAIC) and naïve indirect comparison (NIC) under a wide range of data scenarios on survival outcome. Methods A simulation study included 729 (36) single-arm trial data scenarios, which were created by performing a three-level full factorial arrangement of six situational variables, including individual patient data (IPD) sample size, aggregate data (AgD) sample size, covariate strength, covariate correlation, covariate overlap, and relative treatment effect. In each scenario, 1000 repetitions of simulated datasets were generated using the Monte Carlo approach. MAIC and NIC methods were used to estimate the relative treatment effect of each simulated dataset. The performance was evaluated in terms of bias, empirical standard error (ESE), mean squared error (MSE), and confidence interval coverage, respectively. Results MAIC yielded relatively unbiased estimates of relative treatment effect compared with NIC in most scenarios, with better coverage and MSE but higher ESE. None of the situational variables had a significant impact on the bias and coverage of MAIC. However, increasing IPD sample size and covariate overlap significantly reduced the ESE and MSE of MAIC. In scenarios with low covariate overlap and high covariate strength, the bias of MAIC was larger and even greater than that of NIC. Conclusions The performance of MAIC consistently demonstrates advantage over NIC across various scenarios. MAIC often provides more unbiased estimates and achieves confidence interval coverage close to nominal values compared with NIC. While MAIC may exhibit higher ESE in specific scenarios, this additional uncertainty can offer a more accurate reflection of variability, enhancing the robustness of the results. Researchers should thoroughly comprehend the influencing factors and interactions affecting the performance of these methods and judiciously apply research findings.

Date: 2025
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DOI: 10.1007/s40258-025-00952-1

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