Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Cope S

PROVIDER: S-EPMC7480667 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to-or even incompatible with-the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions, which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

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