Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--2

How Qualitative Methods Can be Used to Inform Model Development

Decision-analytic models play a key role in informing healthcare resource allocation decisions. However, there are ongoing concerns with the credibility of models. Modelling methods guidance can encourage good practice within model development, but its value is dependent on its ability to address the areas that modellers find most challenging. Further, it is important that modelling methods and related guidance are continually updated in light of any new approaches that could potentially enhance model credibility. The objective of this article was to highlight the ways in which qualitative methods have been used and recommended to inform decision-analytic model development and enhance modelling practices. With reference to the literature, the article discusses two key ways in which qualitative methods can be, and have been, applied. The first approach involves using qualitative methods to understand and inform general and future processes of model development, and the second, using qualitative techniques to directly inform the development of individual models. The literature suggests that qualitative methods can improve the validity and credibility of modelling processes by providing a means to understand existing modelling approaches that identifies where problems are occurring and further guidance is needed. It can also be applied within model development to facilitate the input of experts to structural development. We recommend that current and future model development would benefit from the greater integration of qualitative methods, specifically by studying 'real' modelling processes, and by developing recommendations around how qualitative methods can be adopted within everyday modelling practice.

Development of a Conceptual Model of Disease Progression for Use in Economic Modeling of Chronic Obstructive Pulmonary Disease

BACKGROUND

To develop and validate a new conceptual model (CM) of chronic obstructive pulmonary disease (COPD) for use in disease progression and economic modeling. The CM identifies and describes qualitative associations between disease attributes, progression and outcomes.

METHODS

A literature review was performed to identify any published CMs or literature reporting the impact and association of COPD disease attributes with outcomes. After critical analysis of the literature, a Steering Group of experts from the disciplines of health economics, epidemiology and clinical medicine was convened to develop a draft CM, which was refined using a Delphi process. The refined CM was validated by testing for associations between attributes using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE).

RESULTS

Disease progression attributes included in the final CM were history and occurrence of exacerbations, lung function, exercise capacity, signs and symptoms (cough, sputum, dyspnea), cardiovascular disease comorbidities, 'other' comorbidities (including depression), body composition (body mass index), fibrinogen as a biomarker, smoking and demographic characteristics (age, gender). Mortality and health-related quality of life were determined to be the most relevant final outcome measures for this model, intended to be the foundation of an economic model of COPD.

CONCLUSION

The CM is being used as the foundation for developing a new COPD model of disease progression and to provide a framework for the analysis of patient-level data. The CM is available as a reference for the implementation of further disease progression and economic models.

Simulation as an ethical imperative and epistemic responsibility for the implementation of medical guidelines in health care

Guidelines orient best practices in medicine, yet, in health care, many real world constraints limit their optimal realization. Since guideline implementation problems are not systematically anticipated, they will be discovered only post facto, in a learning curve period, while the already implemented guideline is tweaked, debugged and adapted. This learning process comes with costs to human health and quality of life. Despite such predictable hazard, the study and modeling of medical guideline implementation is still seldom pursued. In this article we argue that to systematically identify, predict and prevent medical guideline implementation errors is both an epistemic responsibility and an ethical imperative in health care, in order to properly provide beneficence, minimize or avoid harm, show respect for persons, and administer justice. Furthermore, we suggest that implementation knowledge is best achieved technically by providing simulation modeling studies to anticipate the realization of medical guidelines, in multiple contexts, with system and scenario analysis, in its alignment with the emerging field of implementation science and in recognition of learning health systems. It follows from both claims that it is an ethical imperative and an epistemic responsibility to simulate medical guidelines in context to minimize (avoidable) harm in health care, before guideline implementation.

Cost-effectiveness of In-Home Cognitive Behavioral Therapy for low-income depressed mothers participating in early childhood prevention programs

BACKGROUND

To determine the cost-effectiveness of In-Home Cognitive Behavioral Therapy (IH-CBT) for low-income mothers enrolled in a home visiting program.

METHODS

A cost-utility analysis was conducted using results from a clinical trial of IH-CBT and standard of care for depression derived from the literature. A probabilistic, patient-level Markov model was developed to determine Quality Adjusted Life Years (QALYs). Costs were determined using the Medical Expenditure Panel Survey. A three-year time horizon and payer perspective were used. Sensitivity analyses were employed to determine robustness of the model.

RESULTS

IH-CBT was cost-effective relative to standard of care. IH-CBT was expected to be cost-effective at a three-year time horizon 99.5%, 99.7%, and 99.9% of the time for willingness-to-pay thresholds of US$25,000, US$50,000, and US$100,000, respectively. Patterns were upheld at one-year and five-year time horizons. Over the three-year time horizon, mothers receiving IH-CBT were expected to have 345.6 fewer days of depression relative to those receiving standard home visiting and treatment in the community.

CONCLUSIONS

IH-CBT is a more cost-effective treatment for low-income, depressed mothers than current standards of practice. These findings add to the growing literature demonstrating the cost-effectiveness of CBT for depression, and expand it to cover new mothers. From a payer perspective, IH-CBT is a sound option for treatment of depressed, low-income mothers. Limitations include a restricted time horizon and estimating of standard of care costs.

OP11 Structural Uncertainty In Economic Modelling For Smoking Cessation

INTRODUCTION:

Guidance for developing economic models recommend that model structure is carefully considered, and assumptions varied in sensitivity analysis (1). Models in smoking cessation have typically used cohort-level approaches, although recently discrete event simulations (DESs) have been developed (2). DESs allow additional flexibility such as modelling changing risk over time, and recurrent events. Our aim was to explore the impact of varying model structure and assumptions on the cost-effectiveness of smoking cessation programs.

METHODS:

We built a cohort state-transition model which related mortality to smoking status and considered the prevalence (based on smoking status) of five comorbidities associated with smoking, each of which has an associated cost and quality of life decrement. We additionally built a patient-level DES, using the Discretely Integrated Condition Event framework (3). The DES used the same data as the cohort model, except considering incidence for comorbidities rather than prevalence. We considered a population of smokers aged 16 years old and an intervention costing GBP827 on which 27 percent of people quit, compared with no treatment. We produced results using the two models for comparable scenarios, and ran additional scenarios considering different assumptions.

RESULTS:

In the cohort model, the incremental cost-effectiveness ratio (ICER) for intervention versus no treatment was GBP4,000/quality-adjusted life year (QALY). In the DES, modelling mortality linked to smoker status produced an ICER of GBP1,000/QALY and modelling mortality linked to comorbidities produced an ICER of GBP6,000/QALY. In the DES with mortality linked to comorbidities, varying the relative risk of comorbidities with time since quitting gave an ICER of GBP3,000/QALY. Including relapse increased the ICER to GBP21,000/QALY.

CONCLUSIONS:

The ICER for the smoking cessation program changes when model assumptions are varied, although the choice of DES versus cohort model appears to make a relatively small difference. Inclusion of relapse substantially changes the ICER, demonstrating the importance of long-term effects in economic models.

Translating Pharmacometrics to a Pharmacoeconomic Model of COPD

BACKGROUND

A model-based meta-analysis (MBMA) is a type of meta-regression that uses nonlinear mixed-effects models estimated on trial-level data to relate patient and trial characteristics, dosing, biomarkers, and outcomes of treatment.

OBJECTIVES

To use a pharmacometric MBMA within a pharmacoeconomic model of chronic obstructive pulmonary disease (COPD).

METHODS

A Markov microsimulation model was developed to estimate monthly changes in the key disease severity metrics of COPD (forced expiratory volume in 1 second [FEV₁] and exacerbations) to compare a hypothetical drug that increases FEV₁ to usual care. The MBMA was used to predict a baseline exacerbation rate in a group of actual trial patients, given their known baseline FEV₁. The hypothetical drug increased FEV₁, thereby decreasing individuals' predicted exacerbation rates. Individual patient simulations allowed stochastic changes in monthly FEV₁ decline.

RESULTS

In a sample of 1097 trial patients with a mean FEV₁ of 50%, the MBMA predicted 0.93 exacerbations per year on average. The exacerbation rate ranged from 0.52 to 1.3 per year across moderate and severe patient subgroups. A hypothetical anti-inflammatory drug that increased FEV₁ by 50 ml decreased exacerbations by 26%. Given a simplified estimation of costs and quality-adjusted life-years (QALYs) associated with COPD, a drug with a 50-ml increase priced at €35/mo had an incremental cost-effectiveness ratio ranging from €13,000/QALY to approximately €207,000/QALY across patient severity subgroups.

CONCLUSIONS

The synergistic aspects of MBMA and pharmacoeconomic modeling are highlighted in this hypothetical example. Markov microsimulation modeling allows the finer predictions of MBMA to inform parameters. Such an approach has utility in both early-phase cost-effectiveness estimations and trial design.

Public health economic evaluation of different European Union-level policy options aimed at reducing population dietary trans fat intake

BACKGROUND

The adverse relation between dietary trans fatty acid (TFA) intake and coronary artery disease risk is well established. Many countries in the European Union (EU) and worldwide have implemented different policies to reduce the TFA intake of their populations.

OBJECTIVE

The aim of this study was to assess the added value of EU-level action by estimating the cost-effectiveness of 3 possible EU-level policy measures to reduce population dietary TFA intake. This was calculated against a reference situation of not implementing any EU-level policy (i.e., by assuming only national or self-regulatory measures).

DESIGN

We developed a mathematical model to compare different policy options at the EU level: 1) to do nothing beyond the current state (reference situation), 2) to impose mandatory TFA labeling of prepackaged foods, 3) to seek voluntary agreements toward further reducing industrially produced TFA (iTFA) content in foods, and 4) to impose a legislative limit for iTFA content in foods.

RESULTS

The model indicated that to impose an EU-level legal limit or to make voluntary agreements may, over the course of a lifetime (85 y), avoid the loss of 3.73 and 2.19 million disability-adjusted life-years (DALYs), respectively, and save >51 and 23 billion euros when compared with the reference situation. Implementing mandatory TFA labeling can also avoid the loss of 0.98 million DALYs, but this option incurs more costs than it saves compared with the reference option.

CONCLUSIONS

The model indicates that there is added value of an EU-level action, either via a legal limit or through voluntary agreements, with the legal limit option producing the highest additional health benefits. Introducing mandatory TFA labeling for the EU common market may provide some additional health benefits; however, this would likely not be a cost-effective strategy.

Methods to construct a step-by-step beginner's guide to decision analytic cost-effectiveness modeling

BACKGROUND

Although guidance on good research practice in health economic modeling is widely available, there is still a need for a simpler instructive resource which could guide a beginner modeler alongside modeling for the first time.

AIM

To develop a beginner's guide to be used as a handheld guide contemporaneous to the model development process.

METHODS

A systematic review of best practice guidelines was used to construct a framework of steps undertaken during the model development process. Focused methods review supplemented this framework. Consensus was obtained among a group of model developers to review and finalize the content of the preliminary beginner's guide. The final beginner's guide was used to develop cost-effectiveness models.

RESULTS

Thirty-two best practice guidelines were data extracted, synthesized, and critically evaluated to identify steps for model development, which formed a framework for the beginner's guide. Within five phases of model development, eight broad submethods were identified and 19 methodological reviews were conducted to develop the content of the draft beginner's guide. Two rounds of consensus agreement were undertaken to reach agreement on the final beginner's guide. To assess fitness for purpose (ease of use and completeness), models were developed independently and by the researcher using the beginner's guide.

CONCLUSION

A combination of systematic review, methods reviews, consensus agreement, and validation was used to construct a step-by-step beginner's guide for developing decision analytical cost-effectiveness models. The final beginner's guide is a step-by-step resource to accompany the model development process from understanding the problem to be modeled, model conceptualization, model implementation, and model checking through to reporting of the model results.

Do Economic Evaluations in Primary Care Prevention and the Management of Hypertension Conform to Good Practice Guidelines? A Systematic Review

Background: Results of previous research have identified the need for further investigation into the compliance with good practice guidelines for current decision-analytic modeling (DAM). Objective: To identify the extent to which recent model-based economic evaluations of interventions focused on lowering the blood pressure (BP) of patients with hypertension conform to published guidelines for DAM in health care using a five-dimension framework developed to assess compliance to DAM guidelines. Methods: A systematic review of English language articles was undertaken to identify published model-based economic evaluations that examined interventions aimed at lowering BP. The review covered the period January 2000 to March 2015 and included the following electronic bibliographic databases: EMBASE and Medline via Ovid interface and the Centre for Reviews and Dissemination’s (CRD) NHS-EED. Data were extracted based on different components of good practice across five dimensions utilizing a framework to assess compliance to DAM guidelines. Results: Thirteen articles were included in this review. The review found limited compliance to good practice DAM guidelines, which was most frequently justified by the lack of data. Conclusions: The assessment of structural uncertainty cannot yet be considered common practice in primary prevention and management of hypertension, and researchers seem to face difficulties with identifying sources of structural uncertainty and then handling them correctly. Additional guidelines are needed to aid researchers in identifying and managing sources of potential structural uncertainty. Adherence to guidelines is not always possible and it does pose challenges, in particular when there are limitations due to data availability that restrict, for example, a validation process.

Effective but costly: How to tackle difficult trade-offs in evaluating health improving technologies in liver diseases

In the current context of rising health care costs and decreasing sustainability, it is becoming increasingly common to resort to decision analytical modeling and health economics evaluations. Decision analytic models are analytical tools that help decision makers to select the best choice between alternative health care interventions, taking into consideration the complexity of the disease, the socioeconomic context, and the relevant differences in outcomes. We present a brief overview of the use of decision analytical models in health economic evaluations and their applications in the area of liver diseases. The aim is to provide the reader with the basic elements to evaluate health economic analysis reports and to discuss some limitations of the current approaches, as highlighted by the case of the therapy of chronic hepatitis C. To serve its purpose, health economics evaluations must be able to do justice to medical innovation and the market while protecting patients and society and promoting fair access to treatment and its economic sustainability.

CONCLUSION

New approaches and methods able to include variables such as prevalence of the disease, budget impact, and sustainability into the cost-effectiveness analysis are needed to reach this goal. (Hepatology 2016;64:1331-1342).

Mathematical Modeling of the Transmission Dynamics of Clostridium difficile Infection and Colonization in Healthcare Settings: A Systematic Review

BACKGROUND

We conducted a systematic review of mathematical models of transmission dynamic of Clostridium difficile infection (CDI) in healthcare settings, to provide an overview of existing models and their assessment of different CDI control strategies.

METHODS

We searched MEDLINE, EMBASE and Web of Science up to February 3, 2016 for transmission-dynamic models of Clostridium difficile in healthcare settings. The models were compared based on their natural history representation of Clostridium difficile, which could include health states (S-E-A-I-R-D: Susceptible-Exposed-Asymptomatic-Infectious-Resistant-Deceased) and the possibility to include healthcare workers and visitors (vectors of transmission). Effectiveness of interventions was compared using the relative reduction (compared to no intervention or current practice) in outcomes such as incidence of colonization, CDI, CDI recurrence, CDI mortality, and length of stay.

RESULTS

Nine studies describing six different models met the inclusion criteria. Over time, the models have generally increased in complexity in terms of natural history and transmission dynamics and number/complexity of interventions/bundles of interventions examined. The models were categorized into four groups with respect to their natural history representation: S-A-I-R, S-E-A-I, S-A-I, and S-E-A-I-R-D. Seven studies examined the impact of CDI control strategies. Interventions aimed at controlling the transmission, lowering CDI vulnerability and reducing the risk of recurrence/mortality were predicted to reduce CDI incidence by 3-49%, 5-43% and 5-29%, respectively. Bundles of interventions were predicted to reduce CDI incidence by 14-84%.

CONCLUSIONS

Although CDI is a major public health problem, there are very few published transmission-dynamic models of Clostridium difficile. Published models vary substantially in the interventions examined, the outcome measures used and the representation of the natural history of Clostridium difficile, which make it difficult to synthesize results and provide a clear picture of optimal intervention strategies. Future modeling efforts should pay specific attention to calibration, structural uncertainties, and transparent reporting practices.

GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world

The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real‐world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi‐state models, discrete event simulation models, physiology‐based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real‐world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

Development of an enhanced health-economic model and cost-effectiveness analysis of tiotropium + olodaterol Respimat® fixed-dose combination for chronic obstructive pulmonary disease patients in Italy

BACKGROUND

The objective of this study was to compare the cost-effectiveness of the fixed-dose combination (FDC) of tiotropium + olodaterol Respimat(®) FDC with tiotropium alone for patients with chronic obstructive pulmonary disease (COPD) in the Italian health care setting using a newly developed patient-level Markov model that reflects the current understanding of the disease.

METHODS

While previously published models have largely been based around a cohort approach using a Markov structure and GOLD stage stratification, an individual-level Markov approach was selected for the new model. Using patient-level data from the twin TOnado trials assessing Tiotropium + olodaterol Respimat(®) FDC versus tiotropium, outcomes were modelled based on the trough forced expiratory volume (tFEV1) of over 1000 patients in each treatment arm, tracked individually at trial visits through the 52-week trial period, and after the trial period it was assumed to decline at a constant rate based on disease stage. Exacerbation risk was estimated based on a random-effects logistic regression analysis of exacerbations in UPLIFT. Mortality by age and disease stage was estimated from an analysis of TIOSPIR trial data. Cost of bronchodilators and other medications, routine management, and costs of treatment for moderate and severe exacerbations for the Italian setting were included. A cost-effectiveness analysis was conducted over a 15-year time horizon from the perspective of the Italian National Health Service.

RESULTS

Aggregating total costs and quality-adjusted life years (QALYs) for each treatment cohort over 15 years and comparing tiotropium + olodaterol Respimat(®) FDC with tiotropium alone, resulted in mean incremental costs per patient of €1167 and an incremental cost-effectiveness ratio (ICER) of €7518 per additional QALY with tiotropium + olodaterol Respimat(®) FDC. The lung function outcomes observed for tiotropium + olodaterol Respimat(®) FDC in TOnado drove the results in terms of slightly higher mean life-years (12.24 versus 12.07) exacerbation-free months (11.36 versus 11.32) per patient and slightly fewer moderate and severe exacerbations per patient-year (0.411 versus 0.415; 0.21 versus 0.24) versus tiotropium. Probabilistic sensitivity analyses showed tiotropium + olodaterol Respimat(®) FDC to be the more cost-effective treatment in 95.2% and 98.4% of 500 simulations at thresholds of €20,000 and €30,000 per QALY respectively.

CONCLUSION

Tiotropium + olodaterol Respimat(®) FDC is a cost-effective bronchodilator in the maintenance treatment of COPD for the Italian health care system.

Systematic Review of Model-Based Economic Evaluations of Treatments for Alzheimer's Disease

BACKGROUND

Numerous economic evaluations using decision-analytic models have assessed the cost effectiveness of treatments for Alzheimer's disease (AD) in the last two decades. It is important to understand the methods used in the existing models of AD and how they could impact results, as they could inform new model-based economic evaluations of treatments for AD.

OBJECTIVE

The aim of this systematic review was to provide a detailed description on the relevant aspects and components of existing decision-analytic models of AD, identifying areas for improvement and future development, and to conduct a quality assessment of the included studies.

METHODS

We performed a systematic and comprehensive review of cost-effectiveness studies of pharmacological treatments for AD published in the last decade (January 2005 to February 2015) that used decision-analytic models, also including studies considering patients with mild cognitive impairment (MCI). The background information of the included studies and specific information on the decision-analytic models, including their approach and components, assumptions, data sources, analyses, and results, were obtained from each study. A description of how the modeling approaches and assumptions differ across studies, identifying areas for improvement and future development, is provided. At the end, we present our own view of the potential future directions of decision-analytic models of AD and the challenges they might face.

RESULTS

The included studies present a variety of different approaches, assumptions, and scope of decision-analytic models used in the economic evaluation of pharmacological treatments of AD. The major areas for improvement in future models of AD are to include domains of cognition, function, and behavior, rather than cognition alone; include a detailed description of how data used to model the natural course of disease progression were derived; state and justify the economic model selected and structural assumptions and limitations; provide a detailed (rather than high-level) description of the cost components included in the model; and report on the face-, internal-, and cross-validity of the model to strengthen the credibility and confidence in model results. The quality scores of most studies were rated as fair to good (average 87.5, range 69.5-100, in a scale of 0-100).

CONCLUSION

Despite the advancements in decision-analytic models of AD, there remain several areas of improvement that are necessary to more appropriately and realistically capture the broad nature of AD and the potential benefits of treatments in future models of AD.

Discretely Integrated Condition Event (DICE) Simulation for Pharmacoeconomics

Several decision-analytic modeling techniques are in use for pharmacoeconomic analyses. Discretely integrated condition event (DICE) simulation is proposed as a unifying approach that has been deliberately designed to meet the modeling requirements in a straightforward transparent way, without forcing assumptions (e.g., only one transition per cycle) or unnecessary complexity. At the core of DICE are conditions that represent aspects that persist over time. They have levels that can change and many may coexist. Events reflect instantaneous occurrences that may modify some conditions or the timing of other events. The conditions are discretely integrated with events by updating their levels at those times. Profiles of determinant values allow for differences among patients in the predictors of the disease course. Any number of valuations (e.g., utility, cost, willingness-to-pay) of conditions and events can be applied concurrently in a single run. A DICE model is conveniently specified in a series of tables that follow a consistent format and the simulation can be implemented fully in MS Excel, facilitating review and validation. DICE incorporates both state-transition (Markov) models and non-resource-constrained discrete event simulation in a single formulation; it can be executed as a cohort or a microsimulation; and deterministically or stochastically.

Cost effectiveness and value of information analyses of islet cell transplantation in the management of 'unstable' type 1 diabetes mellitus

BACKGROUND

Islet cell transplantation is a method to stabilize type 1 diabetes patients with hypoglycemia unawareness and unstable blood glucose levels by reducing insulin dependency and protecting against severe hypoglycemia through restoring endogenous insulin secretion. This study analyses the current cost-effectiveness of this technology and estimates the value of further research to reduce uncertainty around cost-effectiveness.

METHODS

We performed a cost-utility analysis using a Markov cohort model with a mean patient age of 49 to simulate costs and health outcomes over a life-time horizon. Our analysis used intensive insulin therapy (IIT) as comparator and took the provincial healthcare provider perspective. Cost and effectiveness data for up to four transplantations per patient came from the University of Alberta hospital. Costs are expressed in 2012 Canadian dollars and effectiveness in quality-adjusted life-years (QALYs) and life years. To characterize the uncertainty around expected outcomes, we carried out a probabilistic sensitivity analysis within the Bayesian decision-analytic framework. We performed a value-of-information analysis to identify priority areas for future research under various scenarios. We applied a structural sensitivity analysis to assess the dependence of outcomes on model characteristics.

RESULTS

Compared to IIT, islet cell transplantation using non-generic (generic) immunosuppression had additional costs of $150,006 ($112,023) per additional QALY, an average gain of 3.3 life years, and a probability of being cost-effective of 0.5 % (28.3 %) at a willingness-to-pay threshold of $100,000 per QALY. At this threshold the non-generic technology has an expected value of perfect information (EVPI) of $260,744 for Alberta. This increases substantially in cost-reduction scenarios. The research areas with the highest partial EVPI are costs, followed by natural history, and effectiveness and safety.

CONCLUSIONS

Current transplantation technology provides substantial improvements in health outcomes over conventional therapy for highly selected patients with 'unstable' type 1 diabetes. However, it is much more costly and so is not cost-effective. The value of further research into the cost-effectiveness is dependent upon treatment costs. Further, we suggest the value of information should not only be derived from current data alone when knowing that this data will most likely change in the future.

Minimal Residual Disease Evaluation in Childhood Acute Lymphoblastic Leukemia: An Economic Analysis

BACKGROUND

Minimal residual disease (MRD) testing by higher performance techniques such as flow cytometry and polymerase chain reaction (PCR) can be used to detect the proportion of remaining leukemic cells in bone marrow or peripheral blood during and after the first phases of chemotherapy in children with acute lymphoblastic leukemia (ALL). The results of MRD testing are used to reclassify these patients and guide changes in treatment according to their future risk of relapse. We conducted a systematic review of the economic literature, cost-effectiveness analysis, and budget-impact analysis to ascertain the cost-effectiveness and economic impact of MRD testing by flow cytometry for management of childhood precursor B-cell ALL in Ontario.

METHODS

A systematic literature search (1998-2014) identified studies that examined the incremental cost-effectiveness of MRD testing by either flow cytometry or PCR. We developed a lifetime state-transition (Markov) microsimulation model to quantify the cost-effectiveness of MRD testing followed by risk-directed therapy to no MRD testing and to estimate its marginal effect on health outcomes and on costs. Model input parameters were based on the literature, expert opinion, and data from the Pediatric Oncology Group of Ontario Networked Information System. Using predictions from our Markov model, we estimated the 1-year cost burden of MRD testing versus no testing and forecasted its economic impact over 3 and 5 years.

RESULTS

In a base-case cost-effectiveness analysis, compared with no testing, MRD testing by flow cytometry at the end of induction and consolidation was associated with an increased discounted survival of 0.0958 quality-adjusted life-years (QALYs) and increased discounted costs of $4,180, yielding an incremental cost-effectiveness ratio (ICER) of $43,613/QALY gained. After accounting for parameter uncertainty, incremental cost-effectiveness of MRD testing was associated with an ICER of $50,249/QALY gained. In the budget-impact analysis, the 1-year cost expenditure for MRD testing by flow cytometry in newly diagnosed patients with precursor B-cell ALL was estimated at $340,760. We forecasted that the province would have to pay approximately $1.3 million over 3 years and $2.4 million over 5 years for MRD testing by flow cytometry in this population.

CONCLUSIONS

Compared with no testing, MRD testing by flow cytometry in newly diagnosed patients with precursor B-cell ALL represents good value for money at commonly used willingness-to-pay thresholds of $50,000/QALY and $100,000/QALY.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges

Context:

The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner.

Objectives:

Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge.

Methods:

To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA.

Findings:

The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and Ethics) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework:

Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.

Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility.

Ethics: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.

Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.

Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.

Cost-Effectiveness Analysis of Six Strategies to Treat Recurrent Clostridium difficile Infection

Objective

To assess the cost-effectiveness of six treatment strategies for patients diagnosed with recurrent Clostridium difficile infection (CDI) in Canada: 1. oral metronidazole; 2. oral vancomycin; 3.oral fidaxomicin; 4. fecal transplantation by enema; 5. fecal transplantation by nasogastric tube; and 6. fecal transplantation by colonoscopy.

Perspective

Public insurer for all hospital and physician services.

Setting

Ontario, Canada.

Methods

A decision analytic model was used to model costs and lifetime health effects of each strategy for a typical patient experiencing up to three recurrences, over 18 weeks. Recurrence data and utilities were obtained from published sources. Cost data was obtained from published sources and hospitals in Toronto, Canada. The willingness-to-pay threshold was $50,000/QALY gained.

Results

Fecal transplantation by colonoscopy dominated all other strategies in the base case, as it was less costly and more effective than all alternatives. After accounting for uncertainty in all model parameters, there was an 87% probability that fecal transplantation by colonoscopy was the most beneficial strategy. If colonoscopy was not available, fecal transplantation by enema was cost-effective at $1,708 per QALY gained, compared to metronidazole. In addition, fecal transplantation by enema was the preferred strategy if the probability of recurrence following this strategy was below 8.7%. If fecal transplantation by any means was unavailable, fidaxomicin was cost-effective at an additional cost of $25,968 per QALY gained, compared to metronidazole.

Conclusion

Fecal transplantation by colonoscopy (or enema, if colonoscopy is unavailable) is cost-effective for treating recurrent CDI in Canada. Where fecal transplantation is not available, fidaxomicin is also cost-effective.

How Do Diabetes Models Measure Up? A Review of Diabetes Economic Models and ADA Guidelines

Introduction: Economic models and computer simulation models have been used for assessing short-term cost-effectiveness of interventions and modelling long-term outcomes and costs. Several guidelines and checklists have been published to improve the methods and reporting. This article presents an overview of published diabetes models with a focus on how well the models are described in relation to the considerations described by the American Diabetes Association (ADA) guidelines. Methods: Relevant electronic databases and National Institute for Health and Care Excellence (NICE) guidelines were searched in December 2012. Studies were included in the review if they estimated lifetime outcomes for patients with type 1 or type 2 diabetes. Only unique models, and only the original papers were included in the review. If additional information was reported in subsequent or paired articles, then additional citations were included. References and forward citations of relevant articles, including the previous systematic reviews were searched using a similar method to pearl growing. Four principal areas were included in the ADA guidance reporting for models: transparency, validation, uncertainty, and diabetes specific criteria. Results: A total of 19 models were included. Twelve models investigated type 2 diabetes, two developed type 1 models, two created separate models for type 1 and type 2, and three developed joint type 1 and type 2 models. Most models were developed in the United States, United Kingdom, Europe or Canada. Later models use data or methods from earlier models for development or validation. There are four main types of models: Markov-based cohort, Markov-based microsimulations, discrete-time microsimulations, and continuous time differential equations. All models were long-term diabetes models incorporating a wide range of compilations from various organ systems. In early diabetes modelling, before the ADA guidelines were published, most models did not include descriptions of all the diabetes specific components of the ADA guidelines but this improved significantly by 2004. Conclusion: A clear, descriptive short summary of the model was often lacking. Descriptions of model validation and uncertainty were the most poorly reported of the four main areas, but there exist conferences focussing specifically on the issue of validation. Interdependence between the complications was the least well incorporated or reported of the diabetes-specific criterion.

The quality of economic evaluations of ultra-orphan drugs in Europe - a systematic review

An orphan disease is defined in the EU as a disorder affecting less than 1 in 2 000 individuals. The concept of ultra-orphan has been proposed for diseases with a prevalence of less than 1:50 000. Drugs for ultra-orphan diseases are amongst the most expensive medicines on a cost-per-patient basis. The extremely high prices have prompted initiatives to evaluate cost-effectiveness and cost-utility in EU-member states. The objective of this review was to evaluate the quality of cost-effectiveness and cost-utility studies on ultra-orphan drugs. We searched 2 databases and the reference lists of relevant systematic reviews. Studies reporting on full economic evaluations, or at least aiming at such evaluation, were eligible for inclusion. Quality was assessed with the use of the Consensus on Health Economic Criteria (CHEC)-list. Two-hundred-fifty-one studies were identified. Of these, 16 fitted our inclusion criteria. A study on enzyme replacement and substrate reduction therapies for lysosomal storage disorders did not perform a full economic evaluation due to the high drug costs and the lack of a measurable effect on either clinical or health-related quality of life outcomes. Likewise, a cost-effectiveness analysis of laronidase for mucopolysaccharidosis type 1 was considered unfeasible due to lack of clinical effectiveness data, while in the same study a crude model was used to estimate cost-utility of enzyme replacement therapy (ERT) for Fabry disease. Three additional studies, one on ERT for Fabry disease, one on ERT for Gaucher disease and one on eculizumab for paroxysmal nocturnal haemoglobinuria, used an approach that was too simplistic to lead to a realistic estimate of the incremental cost-effectiveness (ICER) or cost-utility ratio (ICUR). In all other studies (N = 11) more sophisticated pharmacoeconomic models were used to estimate cost-effectiveness and cost-utility of the specific drug, mostly ERT or drugs indicated for pulmonary arterial hypertension (PAH). Seven studies used a Markov-state-transition model. Other models used were patient-level simulation models (N = 3) and decision trees (N = 1). Only 4 studies adopted a societal perspective. All but 2 studies discounted costs and effects appropriately. Drugs for metabolic diseases appeared to be significantly less cost-effective than drugs indicated for PAH, with ICERs ranging from €43 532 (Gaucher disease) to €3 282 252 (Fabry disease). Quality of studies using a Markov-state-transition or patient-level simulation model is in general good with 14–19 points on the CHEC-list. We therefore conclude that economic evaluations of ultra-orphan drugs are feasible if pharmacoeconomic modelling is used. Considering the need for modelling of several disease states and the small patient groups, a Markov-state-transition model seems to be most suitable type of model. However, it should be realised that ultra-orphan drugs will usually not meet the conventional criteria for cost-effectiveness. Nevertheless, ultra-orphan drugs are often reimbursed. Further discussion on the use of economic evaluations and their consequences in case of ultra-orphan drugs is therefore warranted.

Cost-Effectiveness of New Direct-Acting Antivirals to Prevent Post-Liver Transplant Recurrent Hepatitis

Preliminary studies on HCV-cirrhotics listed for transplant suggest that sofosbuvir in combination with ribavirin is very effective in promoting viral clearance and preventing disease recurrence. Unfortunately, the high cost of such treatment (€46 500 per 12 weeks of treatment) makes its cost-effectiveness questionable. A semi-Markov model was developed to assess the cost-effectiveness of sofosbuvir/ribavirin treatment in cirrhotic patients without HCC (HCV-CIRRH) and with HCC (HCV-HCC) listed for transplant. In the base-case analysis, the incremental cost-effectiveness ratio for 24 weeks of sofosbuvir/ribavirin was €44 875 per quality-adjusted life-year gained in HCV-CIRRH and €60 380 in HCV-HCC patients. Both results were above the willingness to pay threshold of €37 000 per quality-adjusted life-year. Our data also show that in order to remain cost-effective (with a 24-week treatment), any novel interferon-free treatment endowed with ideal efficacy should cost less than €67 224 or €95 712 in HCV-cirrhotics with and without HCC, respectively. The results shows that sofosbuvir/ribavirin therapy, given to patients listed for transplant, is not cost-effective at current prices despite being very effective, and new, more effective treatments will have little economic margins to remain cost-effective. New interferon-free combinations have the potential to revolutionize the treatment and prognosis of HCV-positive patients listed for transplant; however, without sustainable prices, this revolution is unlikely to happen.

Systematic narrative review of decision frameworks to select the appropriate modelling approaches for health economic evaluations

Background

In constructing or appraising a health economic model, an early consideration is whether the modelling approach selected is appropriate for the given decision problem. Frameworks and taxonomies that distinguish between modelling approaches can help make this decision more systematic and this study aims to identify and compare the decision frameworks proposed to date on this topic area.

Methods

A systematic review was conducted to identify frameworks from peer-reviewed and grey literature sources. The following databases were searched: OVID Medline and EMBASE; Wiley’s Cochrane Library and Health Economic Evaluation Database; PubMed; and ProQuest.

Results

Eight decision frameworks were identified, each focused on a different set of modelling approaches and employing a different collection of selection criterion. The selection criteria can be categorized as either: (i) structural features (i.e. technical elements that are factual in nature) or (ii) practical considerations (i.e. context-dependent attributes). The most commonly mentioned structural features were population resolution (i.e. aggregate vs. individual) and interactivity (i.e. static vs. dynamic). Furthermore, understanding the needs of the end-users and stakeholders was frequently incorporated as a criterion within these frameworks.

Conclusions

There is presently no universally-accepted framework for selecting an economic modelling approach. Rather, each highlights different criteria that may be of importance when determining whether a modelling approach is appropriate. Further discussion is thus necessary as the modelling approach selected will impact the validity of the underlying economic model and have downstream implications on its efficiency, transparency and relevance to decision-makers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1202-0) contains supplementary material, which is available to authorized users.

Sample Size Estimation for Non-Inferiority Trials: Frequentist Approach versus Decision Theory Approach

BACKGROUND

Non-inferiority trials are performed when the main therapeutic effect of the new therapy is expected to be not unacceptably worse than that of the standard therapy, and the new therapy is expected to have advantages over the standard therapy in costs or other (health) consequences. These advantages however are not included in the classic frequentist approach of sample size calculation for non-inferiority trials. In contrast, the decision theory approach of sample size calculation does include these factors. The objective of this study is to compare the conceptual and practical aspects of the frequentist approach and decision theory approach of sample size calculation for non-inferiority trials, thereby demonstrating that the decision theory approach is more appropriate for sample size calculation of non-inferiority trials.

METHODS

The frequentist approach and decision theory approach of sample size calculation for non-inferiority trials are compared and applied to a case of a non-inferiority trial on individually tailored duration of elastic compression stocking therapy compared to two years elastic compression stocking therapy for the prevention of post thrombotic syndrome after deep vein thrombosis.

RESULTS

The two approaches differ substantially in conceptual background, analytical approach, and input requirements. The sample size calculated according to the frequentist approach yielded 788 patients, using a power of 80% and a one-sided significance level of 5%. The decision theory approach indicated that the optimal sample size was 500 patients, with a net value of €92 million.

CONCLUSIONS

This study demonstrates and explains the differences between the classic frequentist approach and the decision theory approach of sample size calculation for non-inferiority trials. We argue that the decision theory approach of sample size estimation is most suitable for sample size calculation of non-inferiority trials.

A Systematic Review of Research Guidelines in Decision-Analytic Modeling

BACKGROUND

Decision-analytic modeling (DAM) has been increasingly used to aid decision making in health care. The growing use of modeling in economic evaluations has led to increased scrutiny of the methods used.

OBJECTIVE

The objective of this study was to perform a systematic review to identify and critically assess good practice guidelines, with particular emphasis on contemporary developments.

METHODS

A systematic review of English language articles was undertaken to identify articles presenting guidance for good practice in DAM in the evaluation of health care. The inclusion criteria were articles providing guidance or criteria against which to assess good practice in DAM and studies providing criteria or elements for good practice in some areas of DAM. The review covered the period January 1990 to March 2014 and included the following electronic bibliographic databases: Cochrane Library, Cochrane Methodology Register and Health Technology Assessment, NHS Economic Evaluation Database, MEDLINE, and PubMed (Embase). Additional studies were identified by searching references.

RESULTS

Thirty-three articles were included in this review. A practical five-dimension framework was developed that describe the key elements of good research practice that should be considered and reported to increase the credibility of results obtained from DAM in the evaluation of health care.

CONCLUSIONS

This study is the first to critically review all available guidelines and statements of good practice in DAM since 2006. The development of good practice guidelines is an ongoing process, and important efforts have been made to identify what is good practice and to keep these guidelines up to date.

Mathematical modelling in tobacco control research: protocol for a systematic review

INTRODUCTION

Tobacco control researchers have recently become more interested in systems science methods and mathematical modelling techniques as a means to understand how complex inter-relationships among various factors translate into population-level summaries of tobacco use prevalence and its associated medical and social costs. However, there is currently no resource that provides an overview of how mathematical modelling has been used in tobacco control research. This review will provide a summary of studies that employ modelling techniques to predict tobacco-related outcomes. It will also propose a conceptual framework for grouping existing modelling studies by their objectives.

METHODS AND ANALYSIS

We will conduct a systematic review that is informed by Cochrane procedures, as well as guidelines developed for reviews that are specifically intended to inform policy and programme decision-making. We will search 5 electronic databases to identify studies that use a mathematical model to project a tobacco-related outcome. An online data extraction form will be developed based on the ISPOR-SMDM Modeling Good Research Practices. We will perform a qualitative synthesis of included studies.

ETHICS AND DISSEMINATION

Ethical approval is not required for this study. An initial paper, published in a peer-reviewed journal, will provide an overview of our findings. Subsequent papers will provide greater detail on results within each study objective category and an assessment of the risk of bias of these grouped studies.

Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report

Clinical trials evaluating medicines, medical devices, and procedures now commonly assess the economic value of these interventions. The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. As decision makers increasingly demand evidence of economic value for health care interventions, conducting high-quality economic analyses alongside clinical studies is desirable because they broaden the scope of information available on a particular intervention, and can efficiently provide timely information with high internal and, when designed and analyzed properly, reasonable external validity. In 2005, ISPOR published the Good Research Practices for Cost-Effectiveness Analysis Alongside Clinical Trials: The ISPOR RCT-CEA Task Force report. ISPOR initiated an update of the report in 2014 to include the methodological developments over the last 9 years. This report provides updated recommendations reflecting advances in several areas related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task force members note that trials should be designed to evaluate effectiveness (rather than efficacy) when possible, should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Collection of economic data should be fully integrated into the study. An incremental analysis should be conducted with an intention-to-treat approach, complemented by relevant subgroup analyses. Uncertainty should be characterized. Articles should adhere to established standards for reporting results of cost-effectiveness analyses. Economic studies alongside trials are complementary to other evaluations (e.g., modeling studies) as information for decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.

Comparative economics of a 12-gene assay for predicting risk of recurrence in stage II colon cancer

BACKGROUND

Prior economic analysis that compared the 12-gene assay to published patterns of care predicted the assay would improve outcomes while lowering medical costs for stage II, T3, mismatch-repair-proficient (MMR-P) colon cancer patients. This study assessed the validity of those findings with real-world adjuvant chemotherapy (aCT) recommendations from the US third-party payer perspective.

METHODS

Costs and quality-adjusted life-years (QALYs) were estimated for stage II, T3, MMR-P colon cancer patients using guideline-compliant, state-transition probability estimation methods in a Markov model. A study of 141 patients from 17 sites in the Mayo Clinic Cancer Research Consortium provided aCT recommendations before and after knowledge of the 12-gene assay results. Progression and adverse events data with aCT regimens were based on published literature. Drug and administration costs for aCT were obtained from 2014 Medicare Fee Schedule. Sensitivity analyses evaluated the drivers and robustness of the primary outcomes.

RESULTS

After receiving the 12-gene assay results, physician recommendations in favor of aCT decreased 22 %; fluoropyrimidine monotherapy and FOLFOX recommendations each declined 11 %. Average per-patient drugs, administration, and adverse events costs decreased $US2,339, $US733, and $US3,211, respectively. Average total direct medical costs decreased $US991. Average patient well-being improved by 0.114 QALYs. Savings are expected to persist even if the cost of oxaliplatin drops by >75 % due to generic substitution.

CONCLUSIONS

This study provides evidence that real-world changes in aCT recommendations due to the 12-gene assay are likely to reduce direct medical costs and improve well-being for stage II, T3, MMR-P colon cancer patients.

Decision-analytic models: current methodological challenges

Modelers seeking to help inform decisions about insurance (public or private) coverage of the cost of pharmaceuticals or other health care interventions face various methodological challenges. In this review, which is not meant to be comprehensive, we cover those that in our experience are most vexing. The biggest challenge is getting decision makers to trust the model. This is a major problem because most models undergo only cursory validation; our field has lacked the motivation, time, and data to properly validate models intended to inform health care decisions. Without documented, adequate validation, there is little basis for decision makers to have confidence that the model's results are credible and should be used in a health technology appraisal. A fundamental problem for validation is that the models are very artificial and lack sufficient depth to adequately represent the reality they are simulating. Typically, modelers assume that all resources have infinite capacity so any patient needing care receives it immediately; there are no waiting times or queues, contrary to the common experience in actual practice. Moreover, all the patients enter the model simultaneously at time zero rather than over time as happens in actuality; differences between patients are ignored or minimized and structural modeling choices that make little sense (e.g., using states to represent events) are forced by commitment to a technique (and even to specific spreadsheet software!). The resulting structural uncertainty is rarely addressed, because methods are lacking and even probabilistic analysis of parameter uncertainty suffers from weak consideration of correlation and arbitrary distribution choices. Stakeholders must see to it that models are fit for the stated purpose and provide the best possible estimates given available data-the decisions at stake deserve nothing less.

Challenges in modelling the cost effectiveness of various interventions for cardiovascular disease

OBJECTIVES

Decision analytic modelling is essential in performing cost-effectiveness analyses (CEAs) of interventions in cardiovascular disease (CVD). However, modelling inherently poses challenges that need to be dealt with since models always represent a simplification of reality. The aim of this study was to identify and explore the challenges in modelling CVD interventions.

METHODS

A document analysis was performed of 40 model-based CEAs of CVD interventions published in high-impact journals. We analysed the systematically selected papers to identify challenges per type of intervention (test, non-drug, drug, disease management programme, and public health intervention), and a questionnaire was sent to the corresponding authors to obtain a more thorough overview. Ideas for possible solutions for the challenges were based on the papers, responses, modelling guidelines, and other sources.

RESULTS

The systematic literature search identified 1,720 potentially relevant articles. Forty authors were identified after screening the most recent 294 papers. Besides the challenge of lack of data, the challenges encountered in the review suggest that it was difficult to obtain a sufficiently valid and accurate cost-effectiveness estimate, mainly due to lack of data or extrapolating from intermediate outcomes. Despite the low response rate of the questionnaire, it confirmed our results.

CONCLUSIONS

This combination of a review and a survey showed examples of CVD modelling challenges found in studies published in high-impact journals. Modelling guidelines do not provide sufficient guidance in resolving all challenges. Some of the reported challenges are specific to the type of intervention and disease, while some are independent of intervention and disease.

When to use discrete event simulation (DES) for the economic evaluation of health technologies? A review and critique of the costs and benefits of DES

Modelling in economic evaluation is an unavoidable fact of life. Cohort-based state transition models are most common, though discrete event simulation (DES) is increasingly being used to implement more complex model structures. The benefits of DES relate to the greater flexibility around the implementation and population of complex models, which may provide more accurate or valid estimates of the incremental costs and benefits of alternative health technologies. The costs of DES relate to the time and expertise required to implement and review complex models, when perhaps a simpler model would suffice. The costs are not borne solely by the analyst, but also by reviewers. In particular, modelled economic evaluations are often submitted to support reimbursement decisions for new technologies, for which detailed model reviews are generally undertaken on behalf of the funding body. This paper reports the results from a review of published DES-based economic evaluations. Factors underlying the use of DES were defined, and the characteristics of applied models were considered, to inform options for assessing the potential benefits of DES in relation to each factor. Four broad factors underlying the use of DES were identified: baseline heterogeneity, continuous disease markers, time varying event rates, and the influence of prior events on subsequent event rates. If relevant, individual-level data are available, representation of the four factors is likely to improve model validity, and it is possible to assess the importance of their representation in individual cases. A thorough model performance evaluation is required to overcome the costs of DES from the users' perspective, but few of the reviewed DES models reported such a process. More generally, further direct, empirical comparisons of complex models with simpler models would better inform the benefits of DES to implement more complex models, and the circumstances in which such benefits are most likely.

A pilot study to assess feasibility of value based pricing in Cyprus through pharmacoeconomic modelling and assessment of its operational framework: sorafenib for second line renal cell cancer

Background

The continuing increase of pharmaceutical expenditure calls for new approaches to pricing and reimbursement of pharmaceuticals. Value based pricing of pharmaceuticals is emerging as a useful tool and possess theoretical attributes to help health system cope with rising pharmaceutical expenditure.

Aim

To assess the feasibility of introducing a value-based pricing scheme of pharmaceuticals in Cyprus and explore the integrative framework.

Methods

A probabilistic Markov chain Monte Carlo model was created to simulate progression of advanced renal cell cancer for comparison of sorafenib to standard best supportive care. Literature review was performed and efficacy data were transferred from a published landmark trial, while official pricelists and clinical guidelines from Cyprus Ministry of Health were utilised for cost calculation. Based on proposed willingness to pay threshold the maximum price of sorafenib for the indication of second line renal cell cancer was assessed.

Results

Sorafenib value based price was found to be significantly lower compared to its current reference price.

Conclusion

Feasibility of Value Based Pricing is documented and pharmacoeconomic modelling can lead to robust results. Integration of value and affordability in the price are its main advantages which have to be weighed against lack of documentation for several theoretical parameters that influence outcome. Smaller countries such as Cyprus may experience adversities in establishing and sustaining essential structures for this scheme.

A systematic review of the quality of economic models comparing thrombosis inhibitors in patients with acute coronary syndrome undergoing percutaneous coronary intervention

BACKGROUND

Thrombosis inhibitors can be used to treat acute coronary syndromes (ACS). However, there are various alternative treatment strategies, of which some have been compared using health economic decision models.

OBJECTIVE

To assess the quality of health economic decision models comparing thrombosis inhibitors in patients with ACS undergoing percutaneous coronary intervention, and to identify areas for quality improvement.

DATA SOURCES

The literature databases MEDLINE, EMBASE, EconLit, National Health Service Economic Evaluation Database (NHS EED), Database of Abstracts of Reviews of Effects (DARE) and Health Technology Assessment (HTA).

STUDY APPRAISAL AND SYNTHESIS METHODS

A review of the quality of health economic decision models was conducted by two independent reviewers, using the Philips checklist.

RESULTS

Twenty-one relevant studies were identified. Differences were apparent regarding the model type (six decision trees, four Markov models, eight combinations, three undefined models), the model structure (types of events, Markov states) and the incorporation of data (efficacy, cost and utility data). Critical issues were the absence of particular events (e.g. thrombocytopenia, stroke) and questionable usage of utility values within some studies.

LIMITATIONS

As we restricted our search to health economic decision models comparing thrombosis inhibitors, interesting aspects related to the quality of studies of adjacent medical areas that compared stents or procedures could have been missed.

CONCLUSIONS

This review identified areas where recommendations are indicated regarding the quality of future ACS decision models. For example, all critical events and relevant treatment options should be included. Models also need to allow for changing event probabilities to correctly reflect ACS and to incorporate appropriate, age-specific utility values and decrements when conducting cost-utility analyses.

Questionnaire to assess relevance and credibility of modeling studies for informing health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report

The evaluation of the cost and health implications of agreeing to cover a new health technology is best accomplished using a model that mathematically combines inputs from various sources, together with assumptions about how these fit together and what might happen in reality. This need to make assumptions, the complexity of the resulting framework, the technical knowledge required, as well as funding by interested parties have led many decision makers to distrust the results of models. To assist stakeholders reviewing a model's report, questions pertaining to the credibility of a model were developed. Because credibility is insufficient, questions regarding relevance of the model results were also created. The questions are formulated such that they are readily answered and they are supplemented by helper questions that provide additional detail. Some responses indicate strongly that a model should not be used for decision making: these trigger a "fatal flaw" indicator. It is hoped that the use of this questionnaire, along with the three others in the series, will help disseminate what to look for in comparative effectiveness evidence, improve practices by researchers supplying these data, and ultimately facilitate their use by health care decision makers.

The food choice at work study: effectiveness of complex workplace dietary interventions on dietary behaviours and diet-related disease risk - study protocol for a clustered controlled trial

BACKGROUND

Dietary behaviour interventions have the potential to reduce diet-related disease. Ample opportunity exists to implement these interventions in the workplace. The overall aim is to assess the effectiveness and cost-effectiveness of complex dietary interventions focused on environmental dietary modification alone or in combination with nutrition education in large manufacturing workplace settings.

METHODS/DESIGN

A clustered controlled trial involving four large multinational manufacturing workplaces in Cork will be conducted. The complex intervention design has been developed using the Medical Research Council's framework and the National Institute for Health and Clinical Excellence (NICE) guidelines and will be reported using the TREND statement for the transparent reporting of evaluations with non-randomized designs. It will draw on a soft paternalistic 'nudge' theoretical perspective. It will draw on a soft paternalistic "nudge" theoretical perspective. Nutrition education will include three elements: group presentations, individual nutrition consultations and detailed nutrition information. Environmental dietary modification will consist of five elements: (a) restriction of fat, saturated fat, sugar and salt, (b) increase in fibre, fruit and vegetables, (c) price discounts for whole fresh fruit, (d) strategic positioning of healthier alternatives and (e) portion size control. No intervention will be offered in workplace A (control). Workplace B will receive nutrition education. Workplace C will receive nutrition education and environmental dietary modification. Workplace D will receive environmental dietary modification alone. A total of 448 participants aged 18 to 64 years will be selected randomly. All permanent, full-time employees, purchasing at least one main meal in the workplace daily, will be eligible. Changes in dietary behaviours, nutrition knowledge, health status with measurements obtained at baseline and at intervals of 3 to 4 months, 7 to 9 months and 13 to 16 months will be recorded. A process evaluation and cost-effectiveness economic evaluation will be undertaken.

DISCUSSION

A 'Food Choice at Work' toolbox (concise teaching kit to replicate the intervention) will be developed to inform and guide future researchers, workplace stakeholders, policy makers and the food industry.

TRIAL REGISTRATION

Current Controlled Trials, ISRCTN35108237.

Life Years Lost Associated with Obesity-Related Diseases for U.S. Non-Smoking Adults

The objectives of this paper are to predict life years lost associated with obesity-related diseases (ORDs) for U.S. non-smoking adults, and to examine the relationship between those ORDs and mortality. Data from the National Health Interview Survey, 1997-2000, were used. We employed mixed proportional hazard models to estimate the association between those ORDs and mortality and used simulations to project life years lost associated with the ORDs. We found that obesity-attributable comorbidities are associated with large decreases in life years and increases in mortality rates. The life years lost associated with ORDs is more marked for younger adults than older adults, for blacks than whites, for males than females, and for the more obese than the less obese. Using U.S. non-smoking adults aged 40 to 49 years as an example to illustrate percentage of the life years lost associated with ORDs, we found that the mean life years lost associated with ORDs for U.S. non-smoking black males aged 40 to 49 years with a body mass index above 40 kg/m(2) was 5.43 years, which translates to a 7.5% reduction in total life years. White males of the same age range and same degree of obesity lost 5.23 life years on average - a 6.8% reduction in total life years, followed by black females (5.04 years, a 6.5% reduction in life years), and white females (4.7 years, a 5.8% reduction in life years). Overall, ORDs increased chances of dying and lessened life years by 0.2 to 11.7 years depending on gender, race, BMI classification, and age.

Lopinavir/ritonavir versus darunavir plus ritonavir for HIV infection: a cost-effectiveness analysis for the United States

BACKGROUND

The ARTEMIS trial compared first-line antiretroviral therapy (ART) with lopinavir/ritonavir (LPV/r) to darunavir plus ritonavir (DRV + RTV) for HIV-1-infected subjects. In order to fully assess the implications of this study, economic modelling extrapolating over a longer term is required.

OBJECTIVE

The aim of this study was to simulate the course of HIV and its management, including the multiple factors known to be of importance in ART.

METHODS

A comprehensive discrete event simulation was created to represent, as realistically as possible, ART management and HIV outcomes. The model was focused on patients for whom clinicians believed that LPV/r or DRV + RTV were good options as a first regimen. Prognosis was determined by the impact of initial treatment on baseline CD4+ T-cell count and viral load, adherence, virological suppression/failure/rebound, acquired resistance mutations, and ensuing treatment changes. Inputs were taken from trial data (ARTEMIS), literature and, where necessary, stated assumptions. Clinical measures included AIDS events, side effects, time on sequential therapies, cardiovascular events, and expected life-years lost as a result of HIV infection. The model underwent face, technical and partial predictive validation. Treatment-naive individuals similar to those in the ARTEMIS trial were modelled over a lifetime, and outcomes with first-line DRV + RTV were compared with those with LPV/r, both paired with tenofovir and emtricitabine. Up to three regimen changes were permitted. Drug prices were based on wholesale acquisition cost. Outcomes were lifetime healthcare costs (in 2011 US dollars) from the US healthcare system perspective and quality-adjusted life-years (QALYs) (discounted at 3 % per annum).

RESULTS

Choice of LPV/r over DRV + RTV as initial ART resulted in nearly identical clinical outcomes, but distinctly different economic consequences. Starting with an LPV/r regimen potentially results in approximately US$25,000 discounted lifetime savings. Accumulated QALYs for LPV/r and DRV + RTV were 12.130 and 12.083, respectively (a 19-day difference). In sensitivity analyses, net monetary benefit ranged from US$12,000 to US$31,000, favouring LPV/r (base case US$27,762).

CONCLUSIONS

A comprehensive simulation of lifetime course of HIV in the USA indicated that using LPV/r as first-line therapy compared with DRV + RTV may result in cost savings, with similar clinical outcomes.

Limitations of basing screening policies on screening trials: The US Preventive Services Task Force and Prostate Cancer Screening

BACKGROUND

The US Preventive Services Task Force recently recommended against prostate-specific antigen screening for prostate cancer based primarily on evidence from the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the US Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial.

OBJECTIVE

: To examine limitations of basing screening policy on evidence from screening trials.

METHODS

We reviewed published modeling studies that examined population and trial data. The studies (1) project the roles of screening and changes in primary treatment in the US mortality decline; (2) extrapolate the ERSPC mortality reduction to the long-term US setting; (3) estimate overdiagnosis based on US incidence trends; and (4) quantify the impact of control arm screening on PLCO mortality results.

RESULTS

Screening plausibly explains 45% and changes in primary treatment can explain 33% of the US prostate cancer mortality decline. Extrapolating the ERSPC results to the long-term US setting implies an absolute mortality reduction at least 5 times greater than that observed in the trial. Approximately 28% of screen-detected cases are overdiagnosed in the United States versus 58% of screen-detected cases suggested by the ERSPC results. Control arm screening can explain the null result in the PLCO trial.

CONCLUSIONS

Modeling studies indicate that population trends and trial results extended to the long-term population setting are consistent with greater benefit of prostate-specific antigen screening-and more favorable harm-benefit tradeoffs-than has been suggested by empirical trial evidence.

State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--3

State-transition modeling is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling including both Markov model cohort simulation and individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, state-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. State-transition models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs. The goal of this article was to provide consensus-based guidelines for the application of state-transition models in the context of health care. We structured the best practice recommendations in the following sections: choice of model type (cohort vs. individual-level model), model structure, model parameters, analysis, reporting, and communication. In each of these sections, we give a brief description, address the issues that are of particular relevance to the application of state-transition models, give specific examples from the literature, and provide best practice recommendations for state-transition modeling. These recommendations are directed both to modelers and to users of modeling results such as clinicians, clinical guideline developers, manufacturers, or policymakers.

Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1

Models--mathematical frameworks that facilitate estimation of the consequences of health care decisions--have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR Modeling Task Force reported in 2003 has led to a new Task Force, jointly convened with the Society for Medical Decision Making, and this series of seven articles presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently. This overview article introduces the work of the Task Force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these articles includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.

Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--5

The transmissible nature of communicable diseases is what sets them apart from other diseases modeled by health economists. The probability of a susceptible individual becoming infected at any one point in time (the force of infection) is related to the number of infectious individuals in the population, will change over time, and will feed back into the future force of infection. These nonlinear interactions produce transmission dynamics that require specific consideration when modeling an intervention that has an impact on the transmission of a pathogen. Best practices for designing and building these models are set out in this article.

Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--4

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article was to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as among the wider modeling task force.

Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--6

A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value of information analysis. The article also makes extensive recommendations around the reporting of uncertainty, in terms of both deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--7

Trust and confidence are critical to the success of health care models. There are two main methods for achieving this: transparency (people can see how the model is built) and validation (how well the model reproduces reality). This report describes recommendations for achieving transparency and validation developed by a taskforce appointed by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making. Recommendations were developed iteratively by the authors. A nontechnical description--including model type, intended applications, funding sources, structure, intended uses, inputs, outputs, other components that determine function, and their relationships, data sources, validation methods, results, and limitations--should be made available to anyone. Technical documentation, written in sufficient detail to enable a reader with necessary expertise to evaluate the model and potentially reproduce it, should be made available openly or under agreements that protect intellectual property, at the discretion of the modelers. Validation involves face validity (wherein experts evaluate model structure, data sources, assumptions, and results), verification or internal validity (check accuracy of coding), cross validity (comparison of results with other models analyzing the same problem), external validity (comparing model results with real-world results), and predictive validity (comparing model results with prospectively observed events). The last two are the strongest form of validation. Each section of this article contains a number of recommendations that were iterated among the authors, as well as among the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.