On the double-robustness and semiparametric efficiency of matching-adjusted indirect comparisons.
Res Synth Methods
; 14(3): 438-442, 2023 May.
Article
en En
| MEDLINE
| ID: mdl-36537355
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Puntaje de Propensión
Tipo de estudio:
Health_technology_assessment
/
Prognostic_studies
Idioma:
En
Revista:
Res Synth Methods
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos