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Background: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare., Purpose: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA., Data Sources: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking., Study Selection: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation., Data Extraction: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods., Data Synthesis: Across 18 methods identified from 22 studies, themes included use of informative prior(s) ( n  = 5), piecewise ( n  = 7), and general population adjustment ( n  = 9), plus a variety of "other" ( n  = 8) approaches. Most methods were applied in cancer populations ( n  = 13). No studies compared or validated their method against another method that also incorporated external evidence., Limitations: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review., Conclusions: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

Background: It is increasingly common for two or more treatments for cancer to be combined as a single regimen. Determining value and appropriate payment for such regimens can be challenging. This study discusses these challenges, and possible solutions., Methods: Stakeholders from around the world attended a 2-day workshop, supported by a background paper. This study captures key outcomes from the discussion, but is not a consensus statement., Results: Workshop attendees agreed that combining on-patent treatments can result in affordability and value for money challenges that delay or deny patient access to clinically effective treatments in many health systems. Options for addressing these challenges include: (i) Increasing the value of combination therapies through improved clinical development; (ii) Willingness to pay more for combinations than for single drugs offering similar benefit, or; (iii) Aligning the cost of constituent therapies with their value within a regimen. Workshop attendees felt that (i) and (iii) merited further discussion, whereas (ii) was unlikely to be justifiable. Views differed on the feasibility of (i). Key to (iii) would be systems allowing different prices to apply to different uses of a drug., Conclusions: Common ground was identified on immediate actions to improve access to combination regimens. These include an exploration of the legal challenges associated with price negotiations, and ensuring that pricing systems can support implementation of negotiated prices for specific uses. Improvements to clinical development and trial design should be pursued in the medium and longer term.

Objectives: This research aims to explore how often the National Institute for Health and Care Excellence (NICE) uses immature overall survival data to inform reimbursement decisions on cancer treatments, and the implications of this for resource allocation decisions., Methods: NICE cancer technology appraisals published between 2015 and 2017 were reviewed to determine the prevalence of using immature survival data. A case study was used to demonstrate the potential impact of basing decisions on immature data. The economic model submitted by the company was reconstructed and was populated first using survival data available at the time of the appraisal, and then using data from an updated data cut published after the appraisal concluded. The incremental cost-effectiveness ratios (ICERs) obtained using the different data cuts were compared. Probabilistic sensitivity analysis was undertaken and expected value of perfect information estimated., Results: Forty-one percent of NICE cancer technology appraisals used immature data to inform reimbursement decisions. In the case study, NICE gave a positive recommendation for a limited patient subgroup, with ICERs too high in the complete patient population. ICERs were dramatically lower when the final data cut was used, irrespective of the parametric model used to model survival. Probabilistic sensitivity analysis and expected value of perfect information may not have fully characterized uncertainty, because as they did not account for structural uncertainty., Conclusion: Analyses of cancer treatments using immature survival data may result in incorrect estimates of survival benefit and cost-effectiveness, potentially leading to inappropriate funding decisions. This research highlights the importance of revisiting past decisions when updated data cuts become available., (Copyright © 2020 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Background: It is often important to extrapolate survival estimates beyond the limited follow-up times of clinical trials. Extrapolated survival estimates can be highly sensitive to model choice; thus, appropriate model selection is crucial. Flexible parametric spline models have been suggested as an alternative to standard parametric models; however, their ability to extrapolate is not well understood., Aim: To determine how well standard parametric and flexible parametric spline models predict survival when fitted to registry cohorts with artificially right-censored follow-up times., Methods: Adults with advanced breast, colorectal, small cell lung, non-small cell lung, or pancreatic cancer with a potential follow-up time of 10 y were selected from the SEER 1973-2015 registry data set. Patients were classified into 15 cohorts by cancer and age group at diagnosis (18-59, 60-69, 70+ y). Follow-up times for each cohort were right censored at 20%, 35%, and 50% survival. Standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, generalized gamma) and spline models (proportional hazards, proportional odds, normal/probit) were fitted to the 10-y data set and the 3 right-censored data sets. Predicted 10-y restricted mean survival time and percentage surviving at 10 y were compared with the observed values., Results: Across all data sets, the spline odds and spline normal models most frequently gave accurate predictions of 10-y survival outcomes. Visually, spline models tended to demonstrate better fit to the observed hazard functions than standard parametric models, both in the censored and 10-y data., Conclusions: In these cohorts, where there was little uncertainty in the observed data, the spline models performed well when extrapolating beyond the observed data. Spline models should be routinely included in the set of models that are fitted when extrapolating cancer survival data.

Objectives: To systematically review the quality of reporting on the application of switching adjustment approaches in published oncology trials and industry submissions to the National Institute for Health and Care Excellence Although methods such as the rank preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) have been developed to address treatment switching, the approaches are not widely accepted within health technology assessment. This limited acceptance may partly be a consequence of poor reporting on their application., Methods: Published trials and industry submissions were obtained from searches of PubMed and nice.org.uk, respectively. The quality of reporting in these studies was judged against a checklist of reporting recommendations, which was developed by the authors based on detailed considerations of the methods., Results: Thirteen published trials and 8 submissions to nice.org.uk satisfied inclusion criteria. The quality of reporting around the implementation of the RPSFTM and IPCW methods was generally poor. Few studies stated whether the adjustment approach was prespecified, more than a third failed to provide any justification for the chosen method, and nearly half neglected to perform sensitivity analyses. Further, it was often unclear how the RPSFTM and IPCW methods were implemented., Conclusions: Inadequate reporting on the application of switching adjustment methods increases uncertainty around results, which may contribute to the limited acceptance of these methods by decision makers. The proposed reporting recommendations aim to support the improved interpretation of analyses undertaken to adjust for treatment switching., (Copyright © 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Objectives: Treatment switching occurs when patients in a randomized clinical trial switch from the treatment initially assigned to them to another treatment, typically from the control to experimental treatment. This study discusses the issues this raises and possible approaches to addressing them in trials of cancer drugs., Methods: Stakeholders from around the world were invited to a 1.5-day Workshop in Adelaide, Australia. This study attempts to capture the key points from the discussion and the perspectives of the various stakeholder groups, but is not a formal consensus statement., Results: Treatment switching raises challenging ethical issues with arguments for and against allowing it. It is increasingly common in cancer drug trials and presents challenges for the interpretation of results by regulators, clinicians, patients, and payers. Proposals are offered for good practice in the design, management, and analysis of trials and wider development programs for cancer drugs in which treatment switching has occurred or is likely to. Recommendations are also offered for further action to improve understanding of the importance and challenges of treatment switching and to promote agreement between key stakeholders on guidelines and other steps to address these challenges., Conclusions: The handling of treatment switching in trials is of concern to all stakeholders. On the basis of the discussions at the Adelaide International Workshop, there would appear to be common ground on approaches to addressing treatment switching in cancer trials and scope for the development of formal guidelines to inform the work of regulators, payers, industry, trial designers and other stakeholders.