We estimated the costs and health effects of treating stage I, II, III, and IV breast cancer individually, of treating all stages, and of introducing an extensive cancer control program (treating all stages plus early stage diagnosis) in three epidemiologically different world regions--Africa, North America, and Asia. We developed a mathematical simulation model of breast cancer using the stage distribution and case fatality rates in the presence and absence of treatment as predictors of survival. Outcome measures were life-years adjusted for disability (DALYs), costs (in 2000 U.S. dollars) of treatment and follow-up, and cost-effectiveness ratios (CERs; in dollars per DALY averted). Sensitivity analyses were performed to determine the robustness of the results. Treating patients with stage I breast cancer resulted in 23.41, 12.25, and 19.25 DALYs averted per patient in Africa, North America, and Asia, respectively. The corresponding average CERs compared with no intervention were 78 U.S. dollars , 1,960 U.S. dollars, and 62 U.S. dollars per DALY averted. The number of DALYs averted per patient decreased with stage; the value was lowest for stage IV treatment (0.18-0.19), with average CERs of 4,986 U.S. dollars in Africa, 70,380 U.S. dollars in North America, and 3,510 U.S. dollars per DALY averted in Asia. An extensive breast cancer program resulted in 16.14, 12.91, and 12.58 DALYs averted per patient and average CERs of 75 U.S. dollars, 915 U.S. dollars, and 75 U.S. dollars per DALY averted. Outcomes were most sensitive to case fatality rates for untreated patients, but varying model assumptions did not change the conclusions. These findings suggest that treating stage I disease and introducing an extensive breast cancer program are the most cost-effective breast cancer interventions.
The introduction of PBMs that are specific to a certain disease may have the merit of sensitivity to disease-specific effects of interventions. That gain, however, is traded off to the loss of comparability of utility values and, in some cases, insensitivity to side effects and comorbidity. The use of a CS-PBM for cost-utility analysis is warranted only under strict conditions.
Purpose Since 2000, many new treatment options have become available for relapsed and/or refractory multiple myeloma (R/R MM) after a long period in which dexamethasone and melphalan had been the standard treatment. Direct comparisons of these novel treatments, however, are lacking. This makes it extremely difficult to evaluate the relative added value of each new treatment. Our aim was to synthesize all efficacy evidence, enabling a comparison of all current treatments for R/R MM. Methods We performed a systematic literature review to identify all publicly available phase III randomized controlled trial evidence. We searched Embase, MEDLINE, MEDLINE In-Process, Cochrane Central Register of Controlled Clinical Trials, and the Web site www.ClinicalTrials.gov . In addition, two trials presented at two international hematology congresses (ie, ASCO 2016 and European Hematology Association 2016) were added to include the most recent evidence. In total, 17 randomized controlled trials were identified, including 18 treatment options. The evidence was synthesized using a conventional network meta-analysis. To include all treatments within one network, two treatment options were combined: (1) bortezomib monotherapy and bortezomib plus dexamethasone, and (2) thalidomide monotherapy and thalidomide plus dexamethasone. Results The combination of daratumumab, lenalidomide, and dexamethasone was identified as the best treatment. It was most favorable in terms of (1) hazard ratio for progression-free survival (0.13; 95% credible interval, 0.09 to 0.19), and (2) probability of being best (99% of the simulations). This treatment combination reduced the risk of progression or death by 87% versus dexamethasone, 81% versus bortezomib plus dexamethasone, and 63% versus lenalidomide plus dexamethasone. Conclusion Our network meta-analysis provides a complete overview of the relative efficacy of all available treatments for R/R MM. Until additional data from randomized studies are available, on the basis of this analysis, the combination of daratumumab, lenalidomide, and dexamethasone seems to be the best treatment option.
Background. Responses on condition-specific instruments can be mapped on the EQ-5D to estimate utility values for economic evaluation. Mapping functions differ in predictive quality, and not all condition-specific measures are suitable for estimating EQ-5D utilities. We mapped QLQ-C30, HAQ, and MSIS-29 on the EQ-5D and compared the quality of the mapping functions with statistical and clinical indicators. Methods. We used 4 data sets that included both the EQ-5D and a condition-specific measure to develop ordinary least squares regression equations. For the QLQ-C30, we used a multiple myeloma data set and a non-Hodgkin lymphoma one. An early arthritis cohort was used for the HAQ, and a cohort of patients with relapsing remitting or secondary progressive multiple sclerosis was used for the MSIS-29. We assessed the predictive quality of the mapping functions with the root mean square error (RMSE) and mean absolute error (MAE) and the ability to discriminate among relevant clinical subgroups. Pearson correlations between the condition-specific measures
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