A Review of Recent Decision-Analytic Models Used to Evaluate the Economic Value of Cancer Treatments

Abstract

Cost-effectiveness analysis provides information on the potential value of new cancer treatments, which is particularly pertinent for decision makers as demand for treatment grows while healthcare budgets remain fixed. A range of decision-analytic modelling approaches can be used to estimate cost effectiveness. This study summarises the key modelling approaches considered in oncology, alongside their advantages and limitations. A review was conducted to identify single technology appraisals (STAs) submitted to the National Institute for Health and Care Excellence (NICE) and published papers reporting full economic evaluations of cancer treatments published within the last 5 years. The review was supplemented with the existing methods literature discussing cancer modelling. In total, 100 NICE STAs and 124 published studies were included. Partitioned-survival analysis (n = 54) and discrete-time state transition structures (n = 41) were the main structures submitted to NICE. Conversely, the published studies reported greater use of discrete-time state transition models (n = 102). Limited justification of model structure was provided by authors, despite an awareness in the existing literature that the model structure should be considered thoroughly and can greatly influence cost-effectiveness results. Justification for the choice of model structure was limited and studies would be improved with a thorough rationale for this choice. The strengths and weaknesses of each approach should be considered by future researchers. Alternative methods (such as multi-state modelling) are likely to be utilised more frequently in the future, and so justification of these more advanced methods is paramount to their acceptability to inform healthcare decision making.Electronic supplementary materialThe online version of this article (10.1007/s40258-019-00513-3) contains supplementary material, which is available to authorized users.

A comparison of partitioned survival analysis and state transition multi-state modelling approaches using a case study in oncology

“…The PartSA is the most commonly applied model structure in oncology within the UK. However, the limitations associated with this structure are often not acknowledged or explored thoroughly 8 . Use of MSMs in HTA are less common, a recent example considering the structural link between progression and death through MSM is described in the UK HTA NICE submission TA587 19 .…”

“…The PartSA was characterized by three health states (pre-progression, progressed disease and death); this structure is the most commonly seen in cancer submissions to NICE in the UK 8,9 . State membership was determined by two independent survival curves (PFS and OS) that allow sub-division (or "partitioning") of the OS curve.…”

“…Typically, a CEA involves the development of an economic model to synthesize the available evidence concerning costs and effects in order to compare alternative treatment strategies. The most common model structures constructed in the field of oncology are partitioned survival analyses (PartSA) and state transition models (STMs), which are frequently based on three health states relevant to cancer: pre-progression, progressed disease and death 8,9 .…”

“…PFS and OS outcomes are often readily available from the literature and are widely understood by clinicians and other stakeholders. This makes the PartSA a practical model choice, which is likely to be one reason that this structure was found to be the most common structure applied for cancer treatments in health technology assessments (HTAs) submitted to the National Institute for Health and Care Excellence (NICE)the HTA body for England and Wales 8,9 .…”

“…A review of published CEAs in cancer found that despite the modelling structures available, typically only one is presented with limited explanation as to the justification and validation of this choice 8 . This is despite guidance from the Decision Support Unit (DSU) supporting NICE in UK HTAs stating that "state transition modelling should be used alongside the PartSA approach to assist in verifying the plausibility of PartSA's extrapolations and to address uncertainties in the extrapolation period" 9 .…”