An Oncology Simulation Model to Estimate 10-Year Progression-Free Survival and Stem Cell Transplantation for Frontline, Stage III or IV Classical Hodgkin Lymphoma Based on the 5-Year Update of the ECHELON-1 Trial: A United States Perspective
Abstract:Objectives
Doxorubicin, bleomycin, vinblastine and dacarbazine (ABVD) is the most common frontline (1L) regimen for patients with stage III or IV classical Hodgkin lymphoma (cHL), but about 30% of patients with stage III or IV cHL have refractory or relapsed disease after ABVD treatment. Based on the 5-year update of the ECHELON-1 trial, patients on 1L brentuximab vedotin, doxorubicin, vinblastine and dacarbazine (A+AVD) continued to demonstrate a robust and durable progression-free survival (PF… Show more
“…The OSM framework was utilized because it offers a robust methodological structure for generating estimates of population-level patient survival in oncology and may be utilized to benchmark outcomes from trials of different design such as JAVELIN and other 1L therapies ( Figure 1 ). In addition, the OSM framework was previously used to estimate disease prevalence in breast cancer; 8 prevalence and survival outcomes in bladder cancer; 9 progression-free survival (PFS), OS, and patient productivity losses in classical Hodgkin lymphoma; 10 , 11 and PFS and OS in T-cell lymphoma. 12 Figure 1 Comparison of the design of an oncology maintenance therapy trial with a traditional 1L oncology therapy trial.…”
First-line (1L) maintenance avelumab prolonged overall survival (OS) in patients with advanced urothelial carcinoma (aUC) in JAVELIN Bladder 100. OS was measured from maintenance initiation in patients with disease control following 1L platinum-based therapy (PBT). The OS impact of maintenance for the 1L PBT-treated population is unknown since it was not measured from 1L initiation, nor can it be benchmarked with other 1L therapies. To characterize the OS impact of maintenance avelumab, we used an oncology simulation model to estimate the OS of maintenance-eligible and -ineligible patients with aUC from 1L PBT initiation. Methods: We developed a simulated cohort of 1L PBT-treated patients with aUC, including those who did and did not receive maintenance avelumab. Eligibility was assessed at 5.6 months post 1L PBT initiation based on the JAVELIN trial design. Among the 1L-treated population, 58% (95% credible interval [CrI] 49-67%) were projected to be eligible (calculated from contemporary phase 3 trials); of those, 85% were assumed to receive maintenance. The model estimated median OS (mOS) among a maintenance-ineligible simulated cohort which when combined with the maintenance-eligible cohort yielded an estimated OS in the overall maintenanceintended population from 1L PBT initiation. Results: Approximately half of the modeled 1L PBT-treated population received maintenance. Estimated mOS was 10.1 months (95% CrI 7.5-13.5) for the maintenance-ineligible cohort, 29.3 months (95% CrI 24.8-33.9) for the maintenance-eligible, received maintenance cohort, and 15.9 months (95% CrI 13.2-19.1) in the overall maintenance-intended, 1L PBT-treated population, including those eligible and ineligible for maintenance.
Conclusion:The model shows that maintenance avelumab has a modest impact on OS in the overall 1L PBT-treated population of patients with aUC. While maintenance avelumab improves OS for eligible patients, a large proportion of the maintenance-intended population may not receive maintenance due to ineligibility or physician/patient choice.
“…The OSM framework was utilized because it offers a robust methodological structure for generating estimates of population-level patient survival in oncology and may be utilized to benchmark outcomes from trials of different design such as JAVELIN and other 1L therapies ( Figure 1 ). In addition, the OSM framework was previously used to estimate disease prevalence in breast cancer; 8 prevalence and survival outcomes in bladder cancer; 9 progression-free survival (PFS), OS, and patient productivity losses in classical Hodgkin lymphoma; 10 , 11 and PFS and OS in T-cell lymphoma. 12 Figure 1 Comparison of the design of an oncology maintenance therapy trial with a traditional 1L oncology therapy trial.…”
First-line (1L) maintenance avelumab prolonged overall survival (OS) in patients with advanced urothelial carcinoma (aUC) in JAVELIN Bladder 100. OS was measured from maintenance initiation in patients with disease control following 1L platinum-based therapy (PBT). The OS impact of maintenance for the 1L PBT-treated population is unknown since it was not measured from 1L initiation, nor can it be benchmarked with other 1L therapies. To characterize the OS impact of maintenance avelumab, we used an oncology simulation model to estimate the OS of maintenance-eligible and -ineligible patients with aUC from 1L PBT initiation. Methods: We developed a simulated cohort of 1L PBT-treated patients with aUC, including those who did and did not receive maintenance avelumab. Eligibility was assessed at 5.6 months post 1L PBT initiation based on the JAVELIN trial design. Among the 1L-treated population, 58% (95% credible interval [CrI] 49-67%) were projected to be eligible (calculated from contemporary phase 3 trials); of those, 85% were assumed to receive maintenance. The model estimated median OS (mOS) among a maintenance-ineligible simulated cohort which when combined with the maintenance-eligible cohort yielded an estimated OS in the overall maintenanceintended population from 1L PBT initiation. Results: Approximately half of the modeled 1L PBT-treated population received maintenance. Estimated mOS was 10.1 months (95% CrI 7.5-13.5) for the maintenance-ineligible cohort, 29.3 months (95% CrI 24.8-33.9) for the maintenance-eligible, received maintenance cohort, and 15.9 months (95% CrI 13.2-19.1) in the overall maintenance-intended, 1L PBT-treated population, including those eligible and ineligible for maintenance.
Conclusion:The model shows that maintenance avelumab has a modest impact on OS in the overall 1L PBT-treated population of patients with aUC. While maintenance avelumab improves OS for eligible patients, a large proportion of the maintenance-intended population may not receive maintenance due to ineligibility or physician/patient choice.
“…15 In addition to these data gaps, aspects of BC progression and treatment which make it an ideal use case for the model include the multiple stages of disease and the dynamic, continuously evolving treatment landscape, permitting evaluation of the flexibility of the framework. The OSM has been used previously to estimate disease prevalence of patients undergoing treatment in Canada for this indication 18 and has also been used to estimate disease prevalence in breast cancer, 19 and PFS and OS in classical Hodgkin Lymphoma, 20 and peripheral T-Cell Lymphoma. 21 The framework was implemented in Microsoft Excel for transparency and consistency with contemporary best practices for modeling, although it can be implemented in any computational platform of choice (Excel, R, Python, C, etc).…”
Section: Bladder Cancer As a Case Study Applicationmentioning
We demonstrate a new model framework as an innovative approach to more accurately estimate and project prevalence and survival outcomes in oncology. Methods: We developed an oncology simulation model (OSM) framework that offers a customizable, dynamic simulation model to generate population-level, country-specific estimates of prevalence, incidence of patients progressing from earlier stages (progressionbased incidence), and survival in oncology. The framework, a continuous dynamic Markov cohort model, was implemented in Microsoft Excel. The simulation runs continuously through a prespecified calendar time range. Time-varying incidence, treatment patterns, treatment rates, and treatment pathways are specified by year to account for guideline-directed changes in standard of care and real-world trends, as well as newly approved clinical treatments. Patient cohorts transition between defined health states, with transitions informed by progression-free survival and overall survival as reported in published literature. Results: Model outputs include point prevalence and period prevalence, with options for highly granular prevalence predictions by disease stage, treatment pathway, or time of diagnosis. As a use case, we leveraged the OSM framework to estimate the prevalence of bladder cancer in the United States.
Conclusion:The OSM is a robust model that builds upon existing modeling practices to offer an innovative, transparent approach in estimating prevalence, progression-based incidence, and survival for oncologic conditions. The OSM combines and extends the capabilities of other common health-economic modeling approaches to provide a detailed and comprehensive modeling framework to estimate prevalence in oncology using simulation modeling and to assess the impacts of new treatments on prevalence over time.
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