Comparison of Methods for Estimating Therapy Effects by Indirect Comparisons: A Simulation Study

Dorothea Weber, Katrin Jensen and Meinhard Kieser
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Dorothea Weber: Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Baden-Württemberg, Germany
Katrin Jensen: Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Baden-Württemberg, Germany
Meinhard Kieser: Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Baden-Württemberg, Germany

Medical Decision Making, 2020, vol. 40, issue 5, 644-654

Abstract: Objective . In evidence synthesis, therapeutic options have to be compared despite the lack of head-to-head trials. Indirect comparisons are then widely used, although little is known about their performance in situations where cross-trial differences or effect modification are present. Methods . We contrast the matching adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and the method of Bucher using a simulation study. The different methods are evaluated according to their power and type I error rate as well as with respect to the coverage, bias, and the root mean squared error (RMSE) of the effect estimate for practically relevant scenarios using binary and time-to-event endpoints. In addition, we investigate how the power planned for the head-to-head trials influences the actual power of the indirect comparison. Results . Indirect comparisons are considerably underpowered. None of the methods had substantially superior performance. In situations without cross-trial differences and effect modification, MAIC and Bucher led to similar results, while Bucher has the advantage of preserving the within-study randomization. MAIC and STC could enhance power in some scenarios but at the cost of a potential type I error inflation. Adjusting MAIC and STC for confounders that did not modify the effect led to higher bias and RMSE. Conclusion . The choice of effect modifiers in MAIC and STC influences the precision of the indirect comparison. Therefore, a careful selection of effect modifiers is warranted. In addition, missed differences between trials may lead to low power and partly high bias for all considered methods, and thus, results of indirect comparisons should be interpreted with caution.

Keywords: evidence synthesis; Bucher; MAIC; population adjustment; anchored indirect comparison (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:5:p:644-654

DOI: 10.1177/0272989X20929309

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