Four Aspects Affecting Health Economic Decision Models and Their Validation.

BackgroundA trade-off exists between building confidence in health-economic (HE) decision models and the use of scarce resources. We aimed to create a practical tool providing model users with a structured view into the validation status of HE decision models, to address this trade-off.MethodsA Delphi panel was organized, and was completed by a workshop during an international conference. The proposed tool was constructed iteratively based on comments from, and the discussion amongst, panellists. During the Delphi process, comments were solicited on the importance and feasibility of possible validation techniques for modellers, their relevance for decision makers, and the overall structure and formulation in the tool.ResultsThe panel consisted of 47 experts in HE modelling and HE decision making from various professional and international backgrounds. In addition, 50 discussants actively engaged in the discussion at the conference workshop and returned 19 questionnaires with additional comments. The final version consists of 13 items covering all relevant aspects of HE decision models: the conceptual model, the input data, the implemented software program, and the model outcomes.ConclusionsAssessment of the Validation Status of Health-Economic decision models (AdViSHE) is a validation-assessment tool in which model developers report in a systematic way both on validation efforts performed and on their outcomes. Subsequently, model users can establish whether confidence in the model is justified or whether additional validation efforts should be undertaken. In this way, AdViSHE enhances transparency of the validation status of HE models and supports efficient model validation.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-015-0327-2) contains supplementary material, which is available to authorized users.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

Evaluations of healthcare interventions, e.g. new drugs or other new treatment strategies, commonly include a cost-effectiveness analysis (CEA) that is based on the application of health economic (HE) models. As end users, patients are important stakeholders regarding the outcomes of CEAs, yet their knowledge of HE model development and application, or their involvement therein, is absent. This paper considers possible benefits and risks of patient involvement in HE model development and application for modellers and patients. An exploratory review of the literature has been performed on stakeholder-involved modelling in various disciplines. In addition, Dutch patient experts have been interviewed about their experience in, and opinion about, the application of HE models. Patients have little to no knowledge of HE models and are seldom involved in HE model development and application. Benefits of becoming involved would include a greater understanding and possible acceptance by patients of HE model application, improved model validation, and a more direct infusion of patient expertise. Risks would include patient bias and increased costs of modelling. Patient involvement in HE modelling seems to carry several benefits as well as risks. We claim that the benefits may outweigh the risks and that patients should become involved.

MS3 Reproduction of Published Health Economic Obesity Models: Assessment of Facilitators, Hurdles and Barriers

PRM121 - Assessment of Validation of Health-Economics Decision Models In Intervention Studies of Seasonal Influenza and Breast Cancer

PHP106 - Health Economic Analyses in Medical Nutrition: A Systematic Literature Review

Haemochromatosis is one of the most common autosomal recessive disorders amongst populations of northern European ancestry. It is a condition characterised by iron overload, which, if untreated, contributes to morbidity and mortality. As diagnosis is often delayed owing to the non-specific nature of early symptoms, population screening programs have been suggested to reduce the consequential burden of disease. To date, a paucity of robust health economic evidence regarding haemochromatosis screening has been cited as a key barrier to the establishment of such programs internationally. This thesis investigates and generates health economic evidence for haemochromatosis and related interventions, with a focus on population screening programs. Chapter 1 presents a general overview of haemochromatosis and health economic concepts pertinent to this thesis. Chapter 2 presents a systematic review of all health economic evidence regarding haemochromatosis screening and related interventions. Most of the evidence identified in the review related to population screening programs. The economic methodologies employed and the quality of epidemiologic evidence incorporated into these models were flawed in most studies, reducing their validity and generalizability. The gaps in current knowledge that were identified in this review guided the subsequent direction of this thesis. The systematic review did not identify any robust health state utility value data for haemochromatosis. Whilst four studies included in the review incorporated utility values into cost-effectiveness models, these were set at unrealistically high levels, with no description of the source of these provided. Chapter 3 aimed to robustly quantify the quality of life burden associated with haemochromatosis by measuring health state utility values. A national, online survey of people with haemochromatosis was conducted, which allowed for calculation of utilities for different categories of severity of haemochromatosis. These utility values, which provide insight into the quality of life impacts of haemochromatosis, were incorporated into the population screening model. In the absence of published literature quantifying the economic burden associated with haemochromatosis, a national cost of illness study was undertaken, and is reported in Chapter 4. Costs were estimated from the patient, government and societal perspectives, with societal costs extrapolated to the Australian population. These costs are the first to be published that quantify the economic burden of haemochromatosis. Whilst Chapters 3 and 4 provide information on the size of the burden associated with haemochromatosis, Chapter 6 identifies strategies to address this. Chapter 5 provides a detailed description of the construction and validation of the health economic model for screening for haemochromatosis. A state-transition Markov model using probabilistic decision analysis was constructed and validated for the Australian adult population of northern European ancestry. Chapter 6 presents the results generated from the health economics model. The target populations were males aged 30 years and females aged 45 years, both of northern European ancestry, and neonates irrespective of ancestry. Three population screening strategies (genotyping with a blood sample, genotyping with a buccal sample, sequential screening with two consecutive iron studies and confirmatory HFE genotyping) were modelled for adults and one for neonates (HFE genotyping). The current status quo was the comparator, which comprises a combination of cascade and opportunistic screening. From the government perspective, genotyping with a blood sample was the most cost-effective approach for males; for females, sequential screening was considered to be the most cost-effective. For male and female neonates, screening was associated with cost savings and increased effectiveness, thereby dominating the current status quo. This thesis presents a range of studies that were conducted to address the substantial knowledge gaps identified in the systematic review. Both the health state utility data and cost of illness data can be used to populate future health economic models regarding haemochromatosis interventions. The results of the modelling work provide medical and reimbursement decision-makers with robust data when considering future resource allocation decisions pertaining to screening for haemochromatosis.

Background: Requiring access to sensitive data can be a significant obstacle for the development of health models in the Health Economics & Outcomes Research (HEOR) setting. We demonstrate how health economic evaluation can be conducted with minimal transfer of data between parties, while automating reporting as new information becomes available.Methods: We developed an automated analysis and reporting pipeline for health economic modelling and made the source code openly available on a GitHub repository. The pipeline consists of three parts: An economic model is constructed by theconsultant using pseudo data. On the data-owner side, an application programming interface (API) is hosted on a server. This API hosts all sensitive data, so that data does not have to be provided to theconsultant. An automated workflow is created, which calls the API, retrieves results, and generates a report.Results: The application of modern data science tools and practices allows analyses of data without the need for direct access – negating the need to send sensitive data. In addition, the entire workflow can be largely automated: the analysis can be scheduled to run at defined time points (e.g. monthly), or when triggered by an event (e.g. an update to the underlying data or model code); results can be generated automatically and then be exported into a report. Documents no longer need to be revised manually.Conclusions: This example demonstrates that it is possible, within a HEOR setting, to separate the health economic model from the data, and automate the main steps of the analysis pipeline.

PNS66 NON-INFERIORITY TRIALS IN HEALTH TECHNOLOGY ASSESSMENT - DO THEY NECESSITATE A COST-MINIMISATION APPROACH?

With their article, Grutters et al raise an important question: What do successful health technology assessments (HTAs) look like, and what is their real-world utility in decision-making? While many HTAs are published in peer-reviewed journals, many are considered proprietary and their attributes remain confidential, limiting researchers’ ability to answer these questions. Models for economic evaluations like cost-effectiveness analyses (CEAs) synthesize a wide range of evidence, are often statistically and mathematically sophisticated, and require untestable assumptions. As such, there is nearly universal agreement among researchers that enhancing transparency is an important issue in health economic modeling. However, the definition of transparency and guidelines for its implementation vary. Model registration combined with a linked database of model-based economic evaluations has been proposed as a solution, whereby registered models and their accompanying technical and nontechnical documentation are sourced into a single publicly-available repository, ideally in a standardized format to ensure consistent and complete representation of features, code, data sources, results, validation exercises, and policy recommendations. When such a repository is ultimately created, modelers will not have to reinvent the wheel for every new drug launched or new treatment pathway. These more open and transparent approaches will have substantial implications for model accuracy, reliability, and validity, improving trust and acceptance by healthcare decision-makers.

PCN104 The Appropriate Health Economic Modeling Approach in Cancer Screening Tests

Economic evaluations are used to identify which health treatments or preventions offer the most effective use of resources, or value for money. This is achieved by identifying, measuring and valuing the inputs and outcomes of alternative interventions. These evaluations are often conducted alongside clinical trials; however, these trials may end before the outcomes of economic interest have been observed and measured. An alternative to within trial economic evaluation is to use decision modelling, which can model the cost-effectiveness of interventions over an extended time period. This paper aims to provide an overview for clinicians of the different modelling techniques used within health economic evaluations and to introduce methods for critical appraisal. The most common modelling approaches, and their associated strengths and weaknesses, were discussed. Alongside this, practical examples specific to dermatology were given. These examples include studies where the model chosen or the methods used may not have been the most appropriate. Methods for critical appraisal were also highlighted. Common modelling approaches include Decision Trees, Markov Cohort, extensions to the Markov model (Monte Carlo Simulation) and Discrete Event Simulation models. Items of the Philips Checklist were discussed in the context of performing critical appraisal. Health economic decision models are multi-faceted and can often be complex. Full critical appraisal requires clinicians' unique knowledge, which is complementary to the knowledge of health economists.

HTA49 Determining Appropriate Health Economic Modeling Approaches in Fabry Disease

BackgroundThe evidence base informing economic evaluation models is rarely derived from a single source. Researchers are typically expected to identify and combine available data to inform the estimation of model parameters for a particular decision problem. The absence of clear guidelines on what data can be used and how to effectively synthesize this evidence base under different scenarios inevitably leads to different approaches being used by different modelers. ObjectivesThe aim of this article is to produce a taxonomy that can help modelers identify the most appropriate methods to use when synthesizing the available data for a given model parameter. MethodsThis article developed a taxonomy based on possible scenarios faced by the analyst when dealing with the available evidence. While mainly focusing on clinical effectiveness parameters, this article also discusses strategies relevant to other key input parameters in any economic model (i.e., disease natural history, resource use/costs, and preferences). ResultsThe taxonomy categorizes the evidence base for health economic modeling according to whether 1) single or multiple data sources are available, 2) individual or aggregate data are available (or both), or 3) individual or multiple decision model parameters are to be estimated from the data. References to examples of the key methodological developments for each entry in the taxonomy together with citations to where such methods have been used in practice are provided throughout. ConclusionsThe use of the taxonomy developed in this article hopes to improve the quality of the synthesis of evidence informing decision models by bringing to the attention of health economics modelers recent methodological developments in this field.

Health economic evaluation is a framework for the comparative analysis of the incremental health gains and costs associated with competing decision alternatives. The process of developing health economic models is usually complex, financially expensive and time-consuming. For these reasons, model development is sometimes based on previous model-based analyses; this endeavour is usually referred to as model replication. Such model replication activity may involve the comprehensive reproduction of an existing model or 'borrowing' all or part of a previously developed model structure. Generally speaking, the replication of an existing model may require substantially less effort than developing a new de novo model by bypassing, or undertaking in only a perfunctory manner, certain aspects of model development such as the development of a complete conceptual model and/or comprehensive literature searching for model parameters. A further motivation for model replication may be to draw on the credibility or prestige of previous analyses that have been published and/or used to inform decision making. The acceptability and appropriateness of replicating models depends on the decision-making context: there exists a trade-off between the 'savings' afforded by model replication and the potential 'costs' associated with reduced model credibility due to the omission of certain stages of model development. This paper provides an overview of the different levels of, and motivations for, replicating health economic models, and discusses the advantages, disadvantages and caveats associated with this type of modelling activity. Irrespective of whether replicated models should be considered appropriate or not, complete replicability is generally accepted as a desirable property of health economic models, as reflected in critical appraisal checklists and good practice guidelines. To this end, the feasibility of comprehensive model replication is explored empirically across a small number of recent case studies. Recommendations are put forward for improving reporting standards to enhance comprehensive model replicability.