Introduction Nivolumab demonstrated significant recurrence-free survival (RFS) gains versus ipilimumab in the Check-Mate-238 trial, whereas the CA184-029 trial showed superior RFS gains for ipilimumab versus placebo. No head-to-head trial data were available to compare the efficacy of nivolumab to that of observation, so indirect treatment comparisons were required. Additionally, overall survival (OS) data were not available from CheckMate-238, and the clinical pathway for melanoma has changed significantly over the last decade. Four modelling options were developed using different methods and evidence sources to estimate OS and the impact of nivolumab on predicted life-years in the adjuvant setting; however, this article focuses on two primary methods. Methods RFS for nivolumab and observation were informed by a patient-level data meta-regression. The first model was a partitioned survival model, where the parametric OS curve for observation was derived from CA184-029 and nivolumab OS was based on a surrogacy relationship between RFS and OS specific to adjuvant melanoma. The other option used a state-transition model to estimate post-recurrence survival using different data sources. Results The modelling options estimated different OS for both nivolumab and observation but demonstrated at least a 32% increase in life-years gained for nivolumab versus observation. Conclusion This analysis demonstrated the difficulties in modelling within the adjuvant setting. Each model produced different survival projections, showing the need to explore different techniques to address the extent of uncertainty. This also highlighted the importance of understanding the impact of RFS in the long term in a setting where the aim of treatment is to remain disease free.
Objectives: To evaluate the cost-effectiveness of treating relapsed/refractory multiple myeloma (RRMM) with carfilzomib, lenalidomide plus dexamethasone (KRd) compared to lenalidomide plus dexamethasone (Rd) and with carfilzomib plus dexamethasone (Kd) versus bortezomib plus dexamethasone (Vd) from a Colombian third-party payer perspective. Methods: An economic model was developed to estimate the treatment acquisition costs relative to treatment outcomes for KRd versus Rd and Kd versus Vd in the treatment of RRMM. Efficacy data were based on the ASPIRE and ENDEAVOR head-to-head pivotal trials comparing the therapies of interest. Only treatment acquisition costs were considered and were obtained from local price databases and expressed in local currency. Results were expressed as incremental cost per incremental month of duration of response (DOR), progression free survival (PFS) and overall survival respectively. A threshold of 3 times GDP per capita (63M COP) was considered to assess the comparative cost-effectiveness of each intervention. A scenario considering no drug wastage was also explored. Results: Both combinations with carfilzomib (KRd and Kd) were cost-effective (,3 times GDP per capita) compared to Rd and Vd respectively. For Kd versus Vd, ICERs ranged between 37.19M COP and 53.33M COP, with median DOR being the scenario with the lowest ICER, followed by median PFS. For KRd versus Rd, median PFS provided the most cost-effective scenario followed by median OS, within a 52.95M to 62.26M COP ICER range. When no drug wastage was considered, ICERs were lower by 19% across all outcomes compared to the base case analysis. Conclusions: Carfilzomib is likely to be a cost-effective treatment option for RRMM patients from a third-party payer perspective in Colombia, attributable to its demonstrated superior clinical profile. Further analysis including other direct medical costs, discounts and survival estimate variations is warranted to assess additional foreseeable gains/savings.
Introduction: Health economics models are typically built in Microsoft Excel ® owing to its wide familiarity, accessibility and perceived transparency. However, given the increasingly rapid and analytically complex decision-making needs of both the pharmaceutical industry and the field of health economics and outcomes research (HEOR), the demands of cost-effectiveness analyses may be better met by the programming language R.Objective: This case study provides an explicit comparison between Excel and R for contemporary cost-effectiveness analysis.
Methods:We constructed duplicate cost-effectiveness models using Excel and R (with a user interface built using the Shiny package) to address a hypothetical case study typical of contemporary health technology assessment.
Results:We compared R and Excel versions of the same model design to determine the advantages and limitations of the modelling platforms in terms of (i) analytical capability, (ii) data safety, (iii) building considerations, (iv) usability for technical and non-technical users and (v) model adaptability.
Conclusions:The findings of this explicit comparison are used to produce recommendations for when R might be more suitable than Excel in contemporary cost-effectiveness analyses. We conclude that selection of appropriate modelling software needs to consider case-by-case modelling requirements, particularly (i) intended audience, (ii) complexity of analysis, (iii) nature and frequency of updates and (iv) anticipated model run-time.
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