Abstract
Aims
To present alternative approaches related to both structural assumptions and data sources for the development of a decision analytic model for evaluating the cost-effectiveness of adjuvant nivolumab compared with surveillance in patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection.
Methods and results
Alternative approaches related to both structural assumptions and data sources are presented to address challenges and data gaps, as well as discussion of strengths and limitations of each approach. Specifically, challenges and considerations related to the following are presented: (1) selection of a modeling approach (partitioned survival model or state transition model) given the available evidence, (2) choice of health state structure (three- or four-state) to model disease progression and subsequent therapy, (3) modeling of outcomes from subsequent therapy using tunnel states to account for time-dependent transition probabilities or absorbing health states with one-off costs and outcomes applied, and (4) methods for modeling health-state transitions in a setting where treatment has curative intent and available survival data are immature.
Conclusions
Multiple considerations must be taken into account when developing an economic model for new, emerging oncology treatments in early lines of therapy, all of which can affect the model’s overall ability to estimate (quality-adjusted) survival benefits over a lifetime horizon. This paper identifies a series of key structural and analytic considerations regarding modeling of nivolumab treatment in the adjuvant MIUC setting. Several alternative approaches with regard to structure and data have been included in a flexible cost-effectiveness model so the impact of the alternative approaches on model results can be explored. The impact of these alternative approaches on cost-effectiveness results are presented in a companion article. Our findings may also help inform the development of future models for other treatments and settings in early-stage cancer.
Transparency
Declaration of funding
This study was sponsored by Bristol Myers Squibb.
Declaration of financial/other interests
ST, MK, and MYP are employees of Bristol Myers Squibb (BMS); TP was an employee of BMS at the time of the study. THB, CK, and FK are employees of RTI Health Solutions, which received payment from BMS for contracted analyses. SP received personal payment from BMS for attendance at an advisory board meeting.
Author contributions
All authors contributed to the study design. ST and THB developed the first draft of the manuscript. All authors reviewed and contributed critical revisions to the manuscript. All authors agree to be held accountable for the manuscript content and approve the final version for publication.
Acknowledgements
Bristol Myers Squibb (Princeton, NJ, USA) and Ono Pharmaceutical Company Ltd. (Osaka, Japan). Editorial assistance was provided by Parexel, funded by Bristol Myers Squibb.
Data availability statement
Data are available upon reasonable request. Bristol Myers Squibb’s policy on data sharing may be found online at https://www.bms.com/researchers-and-partners/independent-research/data-sharing-request-process.html.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Previous presentation
A version of this analysis was previously presented at Virtual ISPOR, May 15-18, 2022.