Details

Autor(en) / Beteiligte

• Jiang, Tammy
• Youn, Bora
• Paradis, Angela D.
• Beckerman, Rachel
• Barnieh, Lianne
• Johnson, Nicole B.

Titel

A Critical Appraisal of Matching-Adjusted Indirect Comparisons in Spinal Muscular Atrophy

Ist Teil von

• Advances in therapy, 2023-07, Vol.40 (7), p.2985-3005

Ort / Verlag

Cheshire: Springer Healthcare

Erscheinungsjahr

2023

Quelle

SpringerLink

Beschreibungen/Notizen

• In the absence of head-to-head trials, indirect treatment comparisons (ITCs) are often used to compare the efficacy of different therapies to support decision-making. Matching-adjusted indirect comparison (MAIC), a type of ITC, is increasingly used to compare treatment efficacy when individual patient data are available from one trial and only aggregate data are available from the other trial. This paper examines the conduct and reporting of MAICs to compare treatments for spinal muscular atrophy (SMA), a rare neuromuscular disease. A literature search identified three studies comparing approved treatments for SMA including nusinersen, risdiplam, and onasemnogene abeparvovec. The quality of the MAICs was assessed on the basis of the following principles consolidated from published MAIC best practices: (1) justification for the use of MAIC is clearly stated, (2) the included trials with respect to study population and design are comparable, (3) all known confounders and effect modifiers are identified a priori and accounted for in the analysis, (4) outcomes should be similar in definition and assessment, (5) baseline characteristics are reported before and after adjustment, along with weights, and (6) key details of a MAIC are reported. In the three MAIC publications in SMA to date, the quality of analysis and reporting varied greatly. Various sources of bias in the MAICs were identified, including lack of control for key confounders and effect modifiers, inconsistency in outcome definitions across trials, imbalances in important baseline characteristics after weighting, and lack of reporting key elements. These findings highlight the importance of evaluating MAICs according to best practices when assessing the conduct and reporting of MAICs.