RESUMO
BACKGROUND: We conducted a cost-effectiveness analysis of brentuximab vedotin for the treatment of relapsed and refractory Hodgkin lymphoma (hl) in the post-autologous stem-cell transplantation (asct) failure period, from the perspective of the Canadian health care payer. METHODS: We developed a decision-analytic model to simulate lifetime costs and benefits of brentuximab vedotin compared with best supportive care for the treatment of patients with hl after failure of asct. Administrative data from Ontario were used to set the model parameters. RESULTS: In the base case, treatment with brentuximab vedotin resulted in incremental quality-adjusted life-years (qalys) of 0.544 and an incremental cost of $89,366 per patient, corresponding to an incremental cost-effectiveness ratio (icer) of $164,248 per qaly gained. The icer was sensitive to the cost of brentuximab vedotin, the hazard ratio used to assess the efficacy of brentuximab vedotin treatment, and health state utilities. CONCLUSIONS: In light of the available information, brentuximab vedotin has an icer exceeding $100,000 per qaly gained, which is a level often classified as having "weak evidence for adoption and appropriate utilization" in Canada. However, it is worth noting that provincial cancer agencies take into account not only the costs and associated icer, but also other factors such as a lack of alternative treatment options and the clinical benefits of expensive cancer drugs. Pricing arrangements should be negotiated, and risk-sharing agreements or patient access schemes should be explored.
RESUMO
AIMS: We analysed the radiotherapy planning process at the London Regional Cancer Program to determine the bottlenecks and to quantify the effect of specific resource levels with the goal of reducing waiting times. MATERIALS AND METHODS: We developed a discrete-event simulation model of a patient's journey from the point of referral to a radiation oncologist to the start of radiotherapy, considering the sequential steps and resources of the treatment planning process. We measured the effect of several resource changes on the ready-to-treat to treatment (RTTT) waiting time and on the percentage treated within a 14 calendar day target. RESULTS: Increasing the number of dosimetrists by one reduced the mean RTTT by 6.55%, leading to 84.92% of patients being treated within the 14 calendar day target. Adding one more oncologist decreased the mean RTTT from 10.83 to 10.55 days, whereas a 15% increase in arriving patients increased the waiting time by 22.53%. The model was relatively robust to the changes in quantity of other resources. CONCLUSIONS: Our model identified sensitive and non-sensitive system parameters. A similar approach could be applied by other cancer programmes, using their respective data and individualised adjustments, which may be beneficial in making the most effective use of limited resources.
