Critical Appraisal of Published Indirect Comparisons and Network Meta-Analyses of Competing Interventions for Multiple Myeloma.

Value in Health(2020)

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摘要
Objectives: In the field of relapsed or refractory multiple myeloma (RRMM), between-trial or indirect comparisons are required to estimate relative treatment effects between competing interventions based on the available evidence. Two approaches are frequently used in RRMM: network meta-analysis (NMA) and unanchored matching-adjusted indirect comparison (MAIC). The objective of the current study was to evaluate the relevance and credibility of published NMA and unanchored MAIC studies aiming to estimate the comparative efficacy of treatment options for RRMM. Methods: Twelve relevant studies were identified in the published literature (n = 7) and from health technology assessment agencies (n = 5). Data from trials were extracted to identify between-trial differences that may have biased results. Credibility of the performed analyses and relevance of the research questions were critically appraised using the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) checklist and feedback based on consultations with clinical experts. Results: The identified studies concerned NMAs of randomized controlled trials (RCTs; n = 7), unanchored MAICs (n = 4), or both types of analyses (n = 1). According to clinical expert consultation, the majority of the identified NMAs did not consider differences in prior therapies or treatment duration across the RCTs included in the analyses, thereby compromising the relevance. Conclusion: Based on the results and feedback from clinicians, the majority of NMAs did not consider prior treatment history or treatment duration, which resulted in nonrelevant comparisons. Furthermore, it may have compromised the credibility of the estimates owing to differences in effect-modifiers between the different trials. Pairwise comparisons by means of unanchored MAICs require clear justification given the reliance on non-randomized comparisons.
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