A Systematic Review of Modeling Approaches to Evaluate Treatments for Relapsed Refractory Multiple Myeloma: Critical Review and Considerations for Future Health Economic Models.

Background and objective: Multiple myeloma is a rare incurable hematological cancer in which most patients relapse or become refractory to treatment. This systematic literature review aimed to critically review the existing economic models used in economic evaluations of systemic treatments for relapsed/refractory multiple myeloma and to summarize how the models addressed differences in the line of therapy and exposure to prior treatment.

Methods: Following a pre-approved protocol, literature searches were conducted on 17 February, 2023, in relevant databases for models published since 2014. Additionally, key health technology assessment agency websites were manually searched for models published as part of submission dossiers since 2018. Reported information related to model conceptualization, structure, uncertainty, validation, and transparency were extracted into a pre-defined extraction sheet.

Results: In total, 49 models assessing a wide range of interventions across multiple lines of therapy were included. Only five models specific to heavily pre-treated patients and/or those who were refractory to multiple treatment classes were identified. Most models followed a conventional simple methodology, such as partitioned survival (n = 28) or Markov models (n = 9). All included models evaluated specific interventions rather than the whole treatment sequence. Where subsequent therapies were included in the model, these were generally only considered from a cost and resource use perspective. The models generally used overall and progression-free survival as model inputs, although data were often immature. Sensitivity analyses were frequently reported (n = 41) whereas validation was only considered in less than half (n = 19) of the models.

Conclusions: Published economic models in relapsed/refractory multiple myeloma rarely followed an individual patient approach, mainly owing to the higher need for complex data assumptions compared with simpler modeling approaches. As many patients experience disease progression on multiple treatment lines, there is a growing need for modeling complex treatment strategies, leading to more sophisticated approaches in the future. Maintaining transparency, high reporting standards, and thorough analyses of uncertainty are crucial to support these advancements.