Background New immuno-oncology (I-O) therapies that harness the immune system to fight cancer call for a reexamination of the traditional parametric techniques used to model survival from clinical trial data. More flexible approaches are needed to capture the characteristic I-O pattern of delayed treatment effects and, for a subset of patients, the plateau of long-term survival. Objectives Using a systematic approach to data management and analysis, the study assessed the applicability of traditional and flexible approaches and, as a test case of flexible methods, investigated the suitability of restricted cubic splines (RCS) to model progression-free survival (PFS) in I-O therapy. Methods The goodness of fit of each survival function was tested on data from the CheckMate 067 trial of monotherapy versus combination therapy (nivolumab/ipilimumab) in metastatic melanoma using visual inspection and statistical tests. Extrapolations were validated using long-term data for ipilimumab. Results Modelled PFS estimates using traditional methods did not provide a good fit to the Kaplan-Meier (K-M) curve. RCS estimates fit the K-M curves well, particularly for the plateau phase. RCS with six knots provided the best overall fit, but RCS with one knot performed best at the plateau phase and was preferred on the grounds of parsimony. Conclusions RCS models represent a valuable addition to the range of flexible approaches available to model survival when assessing the effectiveness and cost-effectiveness of I-O therapy. A systematic approach to data analysis is recommended to compare the suitability of different approaches for different diseases and treatment regimens. Key Points for Decision MakersThe use of traditional parametric survival functions can underestimate survival with immuno-oncology (I-O) therapies, primarily when a plateau of long term survival is observed, and therefore give a misleading estimate of life expectancy.Flexible models including restricted cubic splines (RCS) can provide a good fit to trial data and valid extrapolations of clinical trial endpoints, as demonstrated by the case study of progression free survival in I-O treatment of melanoma.Methods including the RCS-based approaches can be considered an option for survival analysis by health technology assessment bodies when considering effectiveness and cost-effectiveness.

Background: Existing economic model frameworks may not adequately capture the atypical treatment response patterns in immuno-oncology (I-O) compared with conventional therapies and thus may fail to represent the full clinical value associated with disease dynamics and improved survival. Objective: A cost-effectiveness analysis (CEA) of the I-O Regimen (nivolumab/ipilimumab) versus ipilimumab alone in advanced melanoma was carried out by applying a 5-state partitioned survival model (PSM) as a case study, to explore the I-O treatment response and clinical outcomes. The findings were compared with those of a conventional 3-state PSM. Materials and Methods: The case study extends the conventional 3-state PSM, by separating the pre-progression state into non-responders and responders, and the post-progression state into normal and I-O progression to account for delayed treatment effects preceding clinical response. Model states were populated using patient-level data (where possible), mapping from the best overall response (BOR), and survival analysis with flexible and traditional parametric methods. Survival functions were applied to progression-free survival (PFS) and overall survival (OS) endpoints across treatment arms using the 4-year follow-up data (data available at the time of the research; since then 5-year follow-up data have been published) from the CheckMate 067 trial. Information on BOR was used as a means of differentiating the I-O treatment response in addition to the outcomes of progression-free and progressed disease. A UK National Health Service and personal social services (NHS/PSS) perspective over a lifetime horizon was used with outcomes discounted at 3.5% annually. Results: The 5-state PSM generated an increase in quality adjusted life years (QALYs) in both treatment arms and gave a more granular description of patients' health profiles compared with the traditional 3-state PSM. The incremental QALY increased by 13% (from 2.62 to 2.95 QALYs) and the incremental cost decreased by 12% (£29,125 to £25,678) with the 5-state model. In both models, the Regimen had an incremental cost-effectiveness ratio (ICER) relative to ipilimumab alone within the lower bound of the National Institute for Health and Care Excellence (NICE) reference range (£20,000 per QALY gained). Conclusion: A 5-state economic model, incorporating relevant I-O health states, can be more informative to gain insight into treatment response and progression differences that are not commonly captured in existing economic models. Clinical trial endpoints, including those relating to treatment response, which are not directly reported in ongoing I-O trials, can be mapped on to the proposed modelled health states (although assumptions are required to do so). Improvements in reporting treatment response in future I-O clinical trials could help to further validate and improve the proposed model framework.

Background: The benefits of preventive interventions lack comprehensive evaluation in standard health technology assessments (HTA), particularly for rare and transmissible diseases. Objective: To identify possible considerations for future HTA using analogies between the treatment and prevention of rare diseases. Study design: An Expert panel meeting assessed whether one HTA assessment framework can be applied to assess both rare disease treatments and preventive interventions. Experts also evaluated the range of value elements currently included in HTAs and their applicability to rare, transmissible, and/or preventable diseases. Results: A broad range of value should be considered when assessing rare, transmissible disease prevention. Although standard HTA can be applied to transmissible diseases, the risk of local outbreaks and the need for large-scale prevention programs suggest a modified assessment framework, capable of incorporating prevention-specific value elements in HTAs. A ‘Rule of Prevention’ framework was proposed to allow broader value considerations anchored to severity, equity, and prevention benefits in decision-making for preventive interventions for rare transmissible diseases. Conclusion: The proposed prevention framework introduces an explicit initial approach to consistently assess rare transmissible diseases, and to incorporate the broader value of preventive interventions compared with treatment.