Implementing Multilevel Network Meta-Regression for Time-To-Event Outcomes: A Case Study in Relapsed Refractory Multiple Myeloma

• Published in: Value in health 2024-08, Vol.27 (8), p.1012-1020
• Main Authors: Maciel, Dylan, Jansen, Jeroen P., Klijn, Sven L., Towle, Kevin, Dhanda, Devender, Malcolm, Bill, Cope, Shannon
• Format: Article
• Language: English
• Subjects: aggregate data; Antineoplastic Combined Chemotherapy Protocols - therapeutic use; Dexamethasone - administration & dosage; Dexamethasone - therapeutic use; Humans; individual patient data; Multiple Myeloma - drug therapy; Multiple Myeloma - mortality; network meta-regression; Randomized Controlled Trials as Topic; time-to-event outcomes; Treatment Outcome
• ISSN: 1098-3015; 1524-4733; 1524-4733
• DOI: 10.1016/j.jval.2024.04.017

Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data 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. 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. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus 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 status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. 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. •Multilevel network meta-regression (ML-NMR) can leverage individual patient data and aggregate data when comparing multiple treatments in a network of any size, while adjusting for differences in populations between trials.•We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code for implementation.•ML-NMR reflects an extension of standard network meta-analysis framework to help address challenges of existing alternative indirect treatment methods, which can be applied to time-to-event outcomes to compare multiple direct treatment comparisons simultaneously in health technology assessments.