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Parametric models are routinely used to estimate the benefit of cancer drugs beyond trial follow-up. The advent of immune checkpoint inhibitors has challenged this paradigm, and emerging evidence suggests that more flexible survival models, which can better capture the shapes of complex hazard functions, might be needed for these interventions. Nevertheless, there is a need for an algorithm to help analysts decide whether flexible models are required and, if so, which should be chosen for testing. This position article has been produced to bridge this gap.A virtual advisory board comprising 7 international experts with in-depth knowledge of survival analysis and health technology assessment was held in summer 2021. The experts discussed 24 questions across 6 topics: the current survival model selection procedure, data maturity, heterogeneity of treatment effect, cure and mortality, external evidence, and additions to existing guidelines. Their responses culminated in an algorithm to inform selection of flexible survival models.The algorithm consists of 8 steps and 4 questions. Key elements include the systematic identification of relevant external data, using clinical expert input at multiple points in the selection process, considering the future and the observed hazard functions, assessing the potential for long-term survivorship, and presenting results from all plausible models.This algorithm provides a systematic, evidence-based approach to justify the selection of survival extrapolation models for cancer immunotherapies. If followed, it should reduce the risk of selecting inappropriate models, partially addressing a key area of uncertainty in the economic evaluation of these agents.

Background: The value of evidence about the performance of a technology and the value of access to a technology are central to policy decisions regarding coverage with, without, or only in research and managed entry (or risk-sharing) agreements. Objectives: We aim to outline the key principles of what assessments are needed to inform "only in research" (OIR) or "approval with research" (AWR) recommendations, in addition to approval or rejection. Methods: We developed a comprehensive algorithm to inform the sequence of assessments and judgments that lead to different types of guidance: OIR, AWR, Approve, or Reject. This algorithm identifies the order in which assessments might be made, how similar guidance might be arrived at through different combinations of considerations, and when guidance might change. Results: The key principles are whether the technology is expected to be cost-effective; whether the technology has significant irrecoverable costs; whether additional research is needed; whether research is possible with approval and whether there are opportunity costs that once committed by approval cannot be recovered; and whether there are effective price reductions. Determining expected cost-effectiveness is only a first step. In addition to AWR for technologies expected to be cost-effective and OIR for those not expected to be cost-effective, there are other important circumstances when OIR should be considered. Conclusions: These principles demonstrate that cost-effectiveness is a necessary but not sufficient condition for approval. Even when research is possible with approval, OIR may be appropriate when a technology is expected to be cost-effective due to significant irrecoverable costs. The Medical Research Council and the National Institute for Health Research Health Technology Assessment (NIHR HTA) program (project no. 06/90/99).

Background The key principles regarding what assessments lead to different types of guidance about the use of health technologies (Only in Research, Approval with Research, Approve, or Reject) provide an explicit and transparent framework for technology appraisal. Objective We aim to demonstrate how these principles and assessments can be applied in practice through the use of a seven-point checklist of assessment. Methods The value of access to a technology and the value of additional evidence are explored through the application of the checklist to the case studies of enhanced external counterpulsation for chronic stable angina and clopidogrel for the management of patients with non–ST-segment elevation acute coronary syndromes. Results The case studies demonstrate the importance of considering 1) the expected cost-effectiveness and population net health effects; 2) the need for evidence and whether the type of research required can be conducted once a technology is approved for widespread use; 3) whether there are sources of uncertainty that cannot be resolved by research but only over time; and 4) whether there are significant (opportunity) costs that once committed by approval cannot be recovered. Conclusions The checklist demonstrates that cost-effectiveness is a necessary but not sufficient condition for approval. Only in Research may be appropriate when a technology is expected to be cost-effective due to significant irrecoverable costs. It is only approval that can be ruled out if a technology is not expected to be cost-effective. Lack of cost-effectiveness is not a necessary or sufficient condition for rejection.

Objective To assess the cost-effectiveness of eplerenone versus spironolactone as an adjunctive therapy to standard care in patients with heart failure (HF) following a myocardial infarction (post-MI) from the perspective of the National Health Service in the United Kingdom. Methods A systematic review was conducted, and a Bayesian meta-regression approach was used to establish the relative effectiveness of eplerenone and spironolactone by using evidence from randomized controlled trials. A decision analytic model was developed to assess the costs and consequences associated with the primary outcome of the trials over a lifetime time horizon. Results The incremental cost-effectiveness ratio of eplerenone compared with that of standard care alone was £4457 and £7893 for each additional quality-adjusted life-year when 2-year and lifetime treatment duration was assumed, respectively. In both scenarios, spironolactone did not appear cost-effective compared with eplerenone. The results were sensitive to the higher relative effectiveness estimated for eplerenone compared with spironolactone from the meta-regression. When a class effect was assumed for the effect on mortality and hospitalizations, spironolactone emerged as the most cost-effective treatment. Conclusions Eplerenone appears more cost-effective than spironolactone for the treatment of post-MI HF. These findings, however, remain subject to important uncertainties regarding the effects of treatment on major clinical events. An adequately powered, well-conducted randomized controlled trial that directly compares spironolactone and eplerenone may be required to provide more robust evidence on the optimal management of post-MI HF. Despite these uncertainties, the use of an aldosterone antagonist was consistently demonstrated to be a highly cost-effective strategy for the management of post-MI HF in the National Health Service.

Background Elicitation can be used to characterize structural uncertainty within a decision analytic model. This allows the value of acquiring further evidence to resolve these uncertainties to be established. Aim This article demonstrated the use of expert elicitation for this purpose and also compared the elicited results with the results from alternative assumptions previously used to characterize the uncertainties. Materials and Methods Distributions for two unknown parameters were elicited. These were used within a model developed to assess the cost-effectiveness of infliximab and etanercept for the treatment of active psoriatic arthritis (PsA), compared with palliative care. The experts' distributions were synthesized using two approaches: linear pooling and random effects meta-analysis. Weighting of experts is also explored. Results The four methods produce broadly similar results, and in each, the choice of optimum strategy is between etanercept and palliative care (incremental cost-effective ratio for etanercept is between £29,021 and £39,259 per costs and quality adjusted life years). Decision uncertainty, at a £30,000 threshold, is high in all of the synthesis models thus generating high values of further research at between £141 and £634 million. In each model, the greatest value of further research was for the short-term effectiveness of treatment (£47–£406 million). Discussion Although the cost-effectiveness results do not differ substantially between the models using the elicited values and the original scenarios, there are some stark contrasts in terms of the values of further research generated. Conclusion Elicitation offers a feasible method to generate evidence for the missing information but there are a number of key issues for which further research is required.

Background The characterization of uncertainty is critical in cost-effectiveness analysis, particularly when considering whether additional evidence is needed. In addition to parameter and methodological uncertainty, there are other sources of uncertainty which include simplifications and scientific judgments that have to be made when constructing and interpreting a model of any sort. These have been classified in a number of different ways but can be referred to collectively as structural uncertainties. Materials and Methods Separate reviews were undertaken to identify what forms these other sources of uncertainty take and what other forms of potential methods to explicitly characterize these types of uncertainties in decision analytic models. These methods were demonstrated through application to four decision models each representing one of the four types of uncertainty. Results These sources of uncertainty fall into four general themes: 1) inclusion of relevant comparators; 2) inclusion of relevant events; 3) alternative statistical estimation methods; and 4) clinical uncertainty. Two methods to explicitly characterize such uncertainties were identified: model selection and model averaging. In addition, an alternative approach, adding uncertain parameters to represent the source of uncertainty was also considered. The applications demonstrate that cost-effectiveness may be sensitive to these uncertainties and the methods used to characterize them. The value of research was particularly sensitive to these uncertainties and the methods used to characterize it. It is therefore important, for decision-making purposes, to incorporate such uncertainties into the modeling process. Conclusion Only parameterizing the uncertainty directly in the model can inform the decision to conduct further research to resolve this source of uncertainty.

Objectives To compare four UK models evaluating the cost-effectiveness of interventions in coronary heart disease (CHD), exploring the relative importance of structure and inputs in accounting for differences, and the scope for consensus on structure and data. Methods We compared published cost-effectiveness results (incremental cost, quality-adjusted life year, and cost-effectiveness ratio) of three models conforming to the National Institute for Health and Clinical Excellence guidelines dealing with three interventions (statins, percutaneous coronary intervention, and clopidogrel) with a model developed in Southampton. Comparisons were made using three separate stages: 1) comparison of published results; 2) comparison of the results using the same data inputs wherever possible; and 3) an in-depth exploration of reasons for differences and the potential for consensus. Results Although published results differed by up to 73% (for statins), standardization of inputs (stage 2) narrowed these gaps. Greater understanding of the reasons for differences was achieved, but a consensus on preferred values for all data inputs was not reached. Conclusions We found that published guidance on methods was important to reduce variation in important model inputs. Although the comparison of models did not lead to consensus for all model inputs, it provided a better understanding of the reasons for these differences, and enhanced the transparency and credibility of all models. Similar comparisons would be aided by fuller publication of models, perhaps through detailed web appendices.