Dear Editor,
We read with interest the study by Encinas et al. exploring the cost-effectiveness of elranatamab versus teclistamab for the treatment of triple-class exposed relapsed/refractory multiple myeloma in Spain [ 1 ].
In this letter, we respectfully highlight several methodological concerns that may have considerably impacted the study’s conclusions regarding the cost-effectiveness of elranatmab, by potentially overestimating relative efficacy of elranatamab, while considerably underestimating its costs.
Relative Efficacy of Elranatamab versus Teclistamab
Uncertainty in Treatment Comparison
The cost-effectiveness analysis uses an unanchored matching-adjusted indirect treatment comparison (MAIC) between elranatamab and teclistamab [ 2 ]. There are several reasons why this indirect treatment comparison may be biased.
First, differences in the timing of the clinical studies for elranatamab and teclistamab may inflate the perceived efficacy of elranatamab. The enrollment and treatment initiation period for MajesTEC-1 (teclistamab) coincided with the peak of the coronavirus disease 2019 (COVID-19) pandemic, prior to vaccine availability, while MagnetisMM-3 (elranatamab) started approximately 1 year later. In MajesTEC-1, 10.9% of patients died due to COVID-19 [ 3 ], compared with 1.6% in MagnetisMM-3 [ 4 ]. After censoring COVID-19 deaths, teclistamab’s median progression-free survival (PFS) and overall survival (OS) improved by +3.8 months and +6.4 months, respectively [ 3 ]. This was not considered in the indirect treatment comparison that forms the basis of the cost-effectiveness analysis and may bias the results in favor of elranatamab. To provide insight into the magnitude of this bias, the authors could have provided a sensitivity analysis adjusting for COVID-19 deaths.
Additionally, the elranatamab PFS curve shows a high early (24-month) censoring percentage (22%) [ 5 ]. Since clinical cutoffs occurred at least 2 years after the last patient received their initial dose, these censorings did not result from follow-up duration but likely from high elranatamab discontinuation owing to adverse events (13.8%). As older and frailer patients in MagnetisMM-3 were more prone to discontinue treatment owing to adverse events [ 6 ], this is likely considerably overestimating elranatamab PFS [ 7 ]. Authors could have qualitatively acknowledged this. Additionally, they could have performed an estimation of potential PFS overestimation because of toxicity-driven early censoring [ 8 ] and considered this in their indirect comparison as a sensitivity analysis.
Finally, while observed outcomes were similar, MAIC weighting had a significant positive impact on elranatamab outcomes. This is in contrast with another recent publication, where outcomes for both treatments before and after weighting were similar [ 9 ]. This introduces considerable uncertainty around the reported MAIC results, which form the basis of the cost-effectiveness analysis.
Presentation of distribution of weights and sensitivity analyses related to the inclusion of matching covariates would have provided more insight into uncertainty surrounding the MAIC estimates.
Progression-Free Survival and Overall Survival Extrapolations
Cost-effectiveness analyses aim to capture costs and effects of therapies over a lifetime horizon. As clinical trials generally do not have data over a lifetime follow-up, extrapolations of observed PFS and OS data are needed. In this case, the authors used Weibull distributions to extrapolate elranatamab PFS and OS, as shown in Fig. 1 of the original paper, on the basis of Akaike information criterion (AIC)/Bayesian information criterion (BIC) and visual inspection. These curves cross early in the extrapolated period, after approximately 32 months. Consequently, after 32 months, there are no patients progressing anymore; they either die without disease progression, or they remain progression-free and alive. The elranatamab National Institute for Health and Care Excellence (NICE) evaluation report already stated that this is not in line with clinical expert expectations, violates key assumptions behind partitioned survival modeling, and results in overestimated quality-adjusted life year (QALY) gains and underestimated subsequent therapy costs [ 10 ]. Selection of clinically plausible extrapolations not leading to crossing of PFS and OS, with scenario analyses using various distributions, would have increased the credibility of the cost-effectiveness analysis.
For teclistamab, PFS and OS were modeled by applying hazard ratios (HRs) derived from the MAIC to the observed MagnetisMM-3 data. However, as previously noted, these HRs are highly uncertain and may be biased in favor of elaranatamab for the reasons explained above. This questions the validity of this approach. It would have been more accurate to use the counterfactual curves from the MAIC for the extrapolation of the long-term PFS and OS for teclistamab, as this would better reflect the observed shape of the teclistamab curves.
Elranatamab Costs Are Considerably Underestimated
Duration of Treatment Extrapolations
While efficacy was modeled using PFS and OS, treatment costs were modeled using the duration of treatment (DoT) data. Exponential distributions were applied to the median values reported in the individual clinical trials (5.6 months for elranatamab and 8.5 months for teclistamab). Exponential distributions rely on the assumption that the probability that a patient discontinues treatment remains constant over time. Supplementary Fig. S1 of the original publication shows the observed elranatamab DoT data and the exponential distribution. It clearly shows that the discontinuation rate in MagnetisMM-3 is high in the beginning and decreases over time. In other words, the assumption of constant hazard behind the exponential extrapolation does not hold. It also clearly demonstrates that using the exponential distribution results in a severe underestimation of treatment costs. The selection of an extrapolation that better fits the observed treatment duration would have more accurately reflected real elranatamab treatment costs.
Unlike PFS and OS, for modeling teclistamab DoT, no adjustment was performed, despite publicly available data [ 11 ]. As adjustment improved the relative efficacy of elranatamab versus teclistamab, adjusting DoT would likely have increased elranatamab treatment duration relative to teclistamab as well. Therefore, not adjusting the DoT curve underestimates elranatamab costs relative to teclistamab.
Dose Switching and Relative Dose Intensity
The analysis considered patients switching to less frequent dosing schedules. While for teclistamab the percentage of switchers (38%) observed in the MajesTEC-1 study [ 12 ] was assumed, for elranatamab, 100% of patients were assumed to switch, instead of the 47.2% reported in MagnetisMM-3 [ 4 ], thereby grossly underestimating elranatamab costs. No justification was provided.
Relative dose intensity (RDI) is one of the key incremental cost-effectiveness ratio (ICER) drivers in this cost-effectiveness study. RDIs of 78.35% for elranatamab and 93.35% for teclistamab were considered to account for dose reductions owing to adverse events. Adverse events led to elranatamab dose reductions in 28.5% of patients [ 4 ], despite the study protocol [ 13 ] and summary of product characteristics (SmPC) [ 14 ] specifically not recommending reductions in the fixed treatment dose. Since elranatamab is administered at a fixed dose, reducing the dose does not lower the cost. Therefore, treatment costs should not be adjusted by an RDI percentage that includes dose reductions. Furthermore, it is not clear if the RDI definition was consistent between the studies and how dosing schedule changes were handled in this percentage.
Subsequent Therapy Costs
Subsequent therapy costs after elranatamab were likely to be considerably underestimated in the presented cost-effectiveness analysis. Subsequent therapy is only costed for a proportion of patients who are entering the postprogression state in the model. Because of early crossing of the extrapolated PFS and OS curves after approximately 32 months, no patients progress on elranatamab anymore, despite > 30% of patients still being progression-free and alive in the model. This is not only clinically implausible, but it also means that these patients will never receive subsequent therapy in the model. This issue could have been prevented by selecting clinically plausible, noncrossing distributions to extrapolate elranatamab PFS and OS.
Conclusions
The published cost-effectiveness analysis may have considerably overestimated the relative efficacy of elranatamab versus teclistamab, while significantly underestimating the costs associated with elranatamab treatment owing to several methodological flaws. Most of these flaws could have been prevented by making more clinically and statistically plausible assumptions and by providing a range of sensitivity or scenario analyses. Therefore, the conclusions drawn in the paper are highly questionable and should be interpreted cautiously.
Author Contributions
All authors contributed to the writing and critical review of this letter. The first draft was written by Kirsten J.M. van Nimwegen. Mustafa Oguz, Raúl Morano, and Joris Diels critically reviewed, provided suggestions for improvement, and contributed to the writing of the letter, both on previous versions as well as the revised manuscript. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this letter and have given their approval for this version to be published. Multiple rounds of review of this letter were provided by Mary Slavcev, BScPhm, Eric Ammann, PhD, and Dr. Eunju Todd.
Funding
No funding or sponsorship was received for the publication of this article.
Declarations
Conflict of interest
Kirsten J.M. van Nimwegen, Mustafa Oguz, Raúl Morana, and Joris Diels work for Johnson & Johnson. The authors report no other conflicts of interest in this communication.
Ethics
This letter is a response to a previously conducted cost-effectiveness analysis and does not contain any new studies within human participants or animals performed by any of the authors.
References
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