On the Double-Robustness and Semiparametric Efficiency of Matching-Adjusted Indirect Comparisons

Autor/inn/en	Cheng, David; Tchetgen, Eric Tchetgen; Signorovitch, James
Titel	On the Double-Robustness and Semiparametric Efficiency of Matching-Adjusted Indirect Comparisons
Quelle	In: Research Synthesis Methods, 14 (2023) 3, S.438-442 (5 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Cheng, David)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1616
Schlagwörter	Comparative Analysis; Robustness (Statistics); Intervention; Patients; Medical Research; Outcomes of Treatment; Probability; Simulation + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Patient; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Simulation program; Simulationsprogramm
Abstract	Matching-adjusted indirect comparison (MAIC) enables indirect comparisons of interventions across separate studies when individual patient-level data (IPD) are available for only one study. Due to its similarity with propensity score weighting, it has been speculated that MAIC can be combined with outcome regression models in the spirit of augmented inverse probability weighting estimators to improve robustness and efficiency. We show that MAIC enjoys intrinsic double-robustness and semiparametric efficiency properties for estimating the average treatment effect on the treated in the limited IPD setting without explicit augmentation. A connection between MAIC and the method of simulated treatment comparisons is highlighted. These results clarify conditions under which MAIC is consistent and efficient, informing appropriate application and interpretation of MAIC analyses. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01