Value Health. 2024 Apr 26:S1098-3015(24)02349-0. doi: 10.1016/j.jval.2024.04.017. Online ahead of print.
ABSTRACT
OBJECTIVES: Multi-level network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data (AD) from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code.
METHODS: The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple class exposed relapsed/refractory multiple myeloma in terms of overall survival (OS). Single-arm clinical trials and real-world data were naively combined to create an AD artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM verus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory (TCR) status, and age and were compared via the leave-one-out information criterion (LOOIC). We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population.
RESULTS: The Weibull ML-NMR model had the lowest LOOIC. Ide-cel was more efficacious than Sd, BM, and CC in terms of OS. Effect modifiers had minimal impact on the model and only TCR was a prognostic factor.
CONCLUSIONS: We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments.
PMID:38679290 | DOI:10.1016/j.jval.2024.04.017