Authors' objectives: Background: Multiple myeloma is the second most common type of blood cancer, with approximately 450 new cases diagnosed annually in Norway. The median age at diagnosis is approximately 70 years, and incidence is rare among individuals under age 30. Multiple myeloma affects plasma cells in the bone marrow. Because there is currently no cure for multiple myeloma, the goal of treatment is to achieve as strong a response as possible without unacceptable side effects, and to maintain the patient’s quality of life at as high a level as possible throughout treatment. Objective: To determine the clinical efficacy, safety, and cost-effectiveness of disease modifying treatments for relapsed and/or refractory multiple myeloma (RRMM) in a Norwegian context.

Authors' results and conclusions: Efficacy and safety: We included in total 72 articles from 50 RCTs that studied the effects of various treatment regimens containing one to three disease modifying drugs. We performed component network meta-analyses on up to 34 randomised, controlled trials, comprising 12 873 randomized patients, and 31 treatments. The radar plots below illustrate the overall safety and efficacy of each treatment regimen across all outcomes. Each individual radar plot presents the available P-scores for the different outcomes, for each treatment regimen, as a polygon (shaded). A Pscore expresses the mean extent of certainty that a given treatment regimen is superior to all other regimens included in the underlying meta-analysis (i.e., with respect to a single outcome such as overall survival). They are informally interpreted as the probability that a treatment is “best”. A treatment with a higher P-score (closer to100%) could be interpreted to be superior (i.e., longer survival, better quality of life, fewer severe adverse events, longer progression-free survival or fewer discontinuations due to adverse events) to a treatment with a lower P-score (closer to 0%). In the radar plots, treatment regimens with polygons with larger areas tend to be superior to those with smaller areas. However, this interpretation can be misleading because data was not available for all treatments and outcomes: it is therefore possible for a highly effective treatment to have a polygon with small area due to a lack of data. When comparing results for different treatment regimens, one should be careful not to interpret effect based solely on polygon area. We inferred that there is not a single treatment regimen that is superior with respect to all outcomes. Radar plots for the double combination [P + d] exemplify treatment regimens that have polygon with large area. This would indicate better efficacy and safety than treatment regimens with smaller area polygons, e.g., [DR + d]. However, when looking closer at the individual P-scores, we find that [P + d] have lower P-score for overall survival than [DR + d]. As such, we would expect longer survival by treatment with [DR + d] than [P + d]. While radar plots may be useful for understanding tradeoffs between efficacy and safety, they should not be interpreted in isolation. Furthermore, the radar plots do not reflect assessments of the certainty of evidence or results of the health economic analysis. We assessed the certainty of evidence (GRADE) for one treatment being better than another, to be mainly low or very low, with a few exceptions. The six triplet combinations [EP + d], [IsP + d], [DK + d], [KR + d], [DR + d] and [DV + d] are examples of treatment regimens relevant for non-refractory patients2 that have clearly favorable hazard ratios for overall survival, that also are ranked highly with respect to other outcomes. Health economic evaluation: We report the cost-effectiveness results for treatments in each reference group that were not dominated by another treatment (the sums given as X because of Norwegian confidentiality law). A treatment is considered dominated if it has a higher total cost and lower health effect than another treatment. In the [R + d] group, [R + d] had costs of NOK X, with 2.90 QALYs gained. Only two other treatments were not dominated by other treatments: [IR + d] had costs of NOK X, 3.82 QALYs, and an ICER of NOK X compared to [R + d]. [DR + d] had costs of NOK X, 4.31 QALYs, and an ICER of NOK X compared to [IR + d]. In the [V + d] group, [V + d] had costs of NOK X and 2.24 QALYs. [DV + d] was the only other treatment that was not dominated by other treatments, with costs of NOK X, 3.63 QALYs, and an ICER of NOK X compared to [V + d]. In the [P + d] group, [P + d] had costs of X, and 0.81 QALYS. [EP + d] was the only other treatment that was not dominated by other treatments, with costs of NOK X, 1.39 QALYs, and an ICER of NOK X compared to [P + d]. In addition to providing cost-effectiveness results, the health economic analysis provided estimates of average absolute shortfall for treatments in each of the reference groups. The values were similar across groups and ranged from 12.46 to 14.95 lost healthy life-years.

Authors' recommendations: Efficacy and safety: We included in total 72 articles from 50 RCTs that studied the effects of various treatment regimens containing one to three disease modifying drugs. We performed component network meta-analyses on up to 34 randomised, controlled trials, comprising 12 873 randomized patients, and 31 treatments. The radar plots below illustrate the overall safety and efficacy of each treatment regimen across all outcomes. Each individual radar plot presents the available P-scores for the different outcomes, for each treatment regimen, as a polygon (shaded). A Pscore expresses the mean extent of certainty that a given treatment regimen is superior to all other regimens included in the underlying meta-analysis (i.e., with respect to a single outcome such as overall survival). They are informally interpreted as the probability that a treatment is “best”. A treatment with a higher P-score (closer to100%) could be interpreted to be superior (i.e., longer survival, better quality of life, fewer severe adverse events, longer progression-free survival or fewer discontinuations due to adverse events) to a treatment with a lower P-score (closer to 0%). In the radar plots, treatment regimens with polygons with larger areas tend to be superior to those with smaller areas. However, this interpretation can be misleading because data was not available for all treatments and outcomes: it is therefore possible for a highly effective treatment to have a polygon with small area due to a lack of data. When comparing results for different treatment regimens, one should be careful not to interpret effect based solely on polygon area. We inferred that there is not a single treatment regimen that is superior with respect to all outcomes. Radar plots for the double combination [P + d] exemplify treatment regimens that have polygon with large area. This would indicate better efficacy and safety than treatment regimens with smaller area polygons, e.g., [DR + d]. However, when looking closer at the individual P-scores, we find that [P + d] have lower P-score for overall survival than [DR + d]. As such, we would expect longer survival by treatment with [DR + d] than [P + d]. While radar plots may be useful for understanding tradeoffs between efficacy and safety, they should not be interpreted in isolation. Furthermore, the radar plots do not reflect assessments of the certainty of evidence or results of the health economic analysis. We assessed the certainty of evidence (GRADE) for one treatment being better than another, to be mainly low or very low, with a few exceptions. The six triplet combinations [EP + d], [IsP + d], [DK + d], [KR + d], [DR + d] and [DV + d] are examples of treatment regimens relevant for non-refractory patients2 that have clearly favorable hazard ratios for overall survival, that also are ranked highly with respect to other outcomes. Health economic evaluation: We report the cost-effectiveness results for treatments in each reference group that were not dominated by another treatment (the sums given as X because of Norwegian confidentiality law). A treatment is considered dominated if it has a higher total cost and lower health effect than another treatment. In the [R + d] group, [R + d] had costs of NOK X, with 2.90 QALYs gained. Only two other treatments were not dominated by other treatments: [IR + d] had costs of NOK X, 3.82 QALYs, and an ICER of NOK X compared to [R + d]. [DR + d] had costs of NOK X, 4.31 QALYs, and an ICER of NOK X compared to [IR + d]. In the [V + d] group, [V + d] had costs of NOK X and 2.24 QALYs. [DV + d] was the only other treatment that was not dominated by other treatments, with costs of NOK X, 3.63 QALYs, and an ICER of NOK X compared to [V + d]. In the [P + d] group, [P + d] had costs of X, and 0.81 QALYS. [EP + d] was the only other treatment that was not dominated by other treatments, with costs of NOK X, 1.39 QALYs, and an ICER of NOK X compared to [P + d]. In addition to providing cost-effectiveness results, the health economic analysis provided estimates of average absolute shortfall for treatments in each of the reference groups. The values were similar across groups and ranged from 12.46 to 14.95 lost healthy life-years.

Authors' methods: Efficacy and safety: We have systematically collected and reviewed the evidence for clinical efficacy and safety for disease modifying treatments for relapsed and/or refractory multiple myeloma according to the PRISMA rules. We identified relevant publications from randomised, controlled trials (RCTs) through systematic reviews from our previous mapping review, as well as through systematic searches. The inclusion criteria were individuals over 18 years diagnosed with multiple myeloma, who either were refractory to at least one previous line of treatment or had experienced one or more relapses. The treatment (intervention) was any of the drugs listed below, alone or in combination with each other, and/or with a glucocorticosteroid such as dexamethasone, compared with any intervention-drug alone, or in combination with each other, or in combination with other drugs. The primary outcomes were overall survival, health related quality of life, and severe adverse events, with overall survival being our main primary outcome. Secondary outcomes were progression free survival, adverse events, and discontinuation due toadverse events. We used the critical appraisal of one systematic review from which we included several studies; two researchers critically appraised the remaining included studies. All outcomes were analysed by component network meta-analyses. We present results for the treatment regimens relevant for Norway, as determined by the Norwegian guideline for multiple myeloma, with results for all included treatment regimens in the appendix. We assessed the certainty of evidence for all outcomes using the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation), expressing the certainty as high, moderate, low, or very low, depending onthe level of confidence we have in the effect estimates. Health economic evaluation: We conducted a cost-utility analysis of 13 treatments for patients with relapsed and/or refractory multiple myeloma in which health effects were measured in quality-adjusted life-years (QALYs), costs in Norwegian kroner, and results were presented as incrementals cost-effectiveness ratios (ICERs). We chose to use a partitioned survival analysis model implemented in TreeAge to perform the analysis. Partitioned survival analyses are frequently used to model the cost-effectiveness of cancer treatments because Kaplan-Meier plots of overall and progression-free survival curves from clinical trials can be used to track patients through three health states: Progressionfree, Progressed, and Dead. Without access to patient level trial data it is not possible to generate well-fitted survival curves for each treatment in the model. Instead, we used a technique, common in cost-effectiveness analyses, in which a survival curve is generated for the comparator treatment in the analysis and the corresponding curves for interventions of interest are generated by applying the relevant hazard ratios from a meta-analysis to the comparator’s survival curve. As the hazard ratios for overall and progression-free survival were taken from the network meta-analysis in the clinical effect section of this report, there was not a “comparator” in the normal sense. In a network meta-analysis any treatment can be designated as a “reference treatment” since the matrix of results generated by the analysis provides hazard ratios for each intervention relative to all other interventions. Based on expert advice, we subdivided our economic model into three treatment groups, each based on one of three reference treatments: lenalidomide (Revlimid) + dexamethasone [R + d], bortezomib (Velcade) + dexamethasone [V + d], and pomalidomide + dexamethasone [P + d]. The [R + d] group included: [R + d], [DR + d], [RK + d], [ER + d], and [IR + d]. The [V + d] group included: [V + d], [DK + d], [K + d], [FV + d], and [DV + d]. The [P + d] group included: [P + d], [EP + d], and [IsP + d]. Costs for the analysis included: 1) cost of medications, 2) time costs for pharmacy and nursing staff for preparation and administration of medications given by injection or infusion, 3) time costs for doctor visits and tests at regular check-ups, and 4) patients’ travel and time costs associated with treatment. We were unable to include costs of severe adverse events, as they were not reported consistently in published trial results, but as these are quite small in relation to medication costs, they would not have resulted in meaningful changes in the results. To account for uncertainty associated with the variables included in the model (hazard ratios, utility values for capturing quality-of-life and treatment costs) we ran the model as a probabilistic sensitivity analysis with 10,000 random draw Monte Carlo iterations. The model also allowed us to calculate absolute shortfall, the variable used to determine the severity of a disease. We also conducted one-way sensitivity analysis to determine which variables had the largest impact on the results.

Project Status: Completed

Year Published: 2023

URL for published report: https://www.fhi.no/en/publ/2023/Treatments-for-relapsing-and-or-refractory-multiple-myeloma/

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: Norway

Organisation Name: Norwegian Institute of Public Health

Contact Address: P.O. Box 222 Skoyen, N-0123, Oslo