Pharmacoeconomic analyses using discrete event simulation

Evaluating Semi-Markov Processes and Other Epidemiological Time-to-Event Models by Computing Disease Sojourn Density as Partial Differential Equations

IntroductionEpidemiological models benefit from incorporating detailed time-to-event data to understand how disease risk evolves. For example, decompensation risk in liver cirrhosis depends on sojourn time spent with cirrhosis. Semi-Markov and related models capture these details by modeling time-to-event distributions based on published survival data. However, implementations of semi-Markov processes rely on Monte Carlo sampling methods, which increase computational requirements and introduce stochastic variability. Explicitly calculating the evolving transition likelihood can avoid these issues and provide fast, reliable estimates.MethodsWe present the sojourn time density framework for computing semi-Markov and related models by calculating the evolving sojourn time probability density as a system of partial differential equations. The framework is parametrized by commonly used hazard and models the distribution of current disease state and sojourn time. We describe the mathematical background, a numerical method for computation, and an example model of liver disease.ResultsModels developed with the sojourn time density framework can directly incorporate time-to-event data and serial events in a deterministic system. This increases the level of potential model detail over Markov-type models, improves parameter identifiability, and reduces computational burden and stochastic uncertainty compared with Monte Carlo methods. The example model of liver disease was able to accurately reproduce targets without extensive calibration or fitting and required minimal computational burden.ConclusionsExplicitly modeling sojourn time distribution allows us to represent semi-Markov systems using detailed survival data from epidemiological studies without requiring sampling, avoiding the need for calibration, reducing computational time, and allowing for more robust probabilistic sensitivity analyses.HighlightsTime-inhomogeneous semi-Markov models and other time-to-event-based modeling approaches can capture risks that evolve over time spent with a disease.We describe an approach to computing these models that represents them as partial differential equations representing the evolution of the sojourn time probability density.This sojourn time density framework incorporates complex data sources on competing risks and serial events while minimizing computational complexity.

A Systematic Review of Modeling Approaches to Evaluate Treatments for Relapsed Refractory Multiple Myeloma: Critical Review and Considerations for Future Health Economic Models

BACKGROUND AND OBJECTIVE

Multiple myeloma is a rare incurable hematological cancer in which most patients relapse or become refractory to treatment. This systematic literature review aimed to critically review the existing economic models used in economic evaluations of systemic treatments for relapsed/refractory multiple myeloma and to summarize how the models addressed differences in the line of therapy and exposure to prior treatment.

METHODS

Following a pre-approved protocol, literature searches were conducted on 17 February, 2023, in relevant databases for models published since 2014. Additionally, key health technology assessment agency websites were manually searched for models published as part of submission dossiers since 2018. Reported information related to model conceptualization, structure, uncertainty, validation, and transparency were extracted into a pre-defined extraction sheet.

RESULTS

In total, 49 models assessing a wide range of interventions across multiple lines of therapy were included. Only five models specific to heavily pre-treated patients and/or those who were refractory to multiple treatment classes were identified. Most models followed a conventional simple methodology, such as partitioned survival (n = 28) or Markov models (n = 9). All included models evaluated specific interventions rather than the whole treatment sequence. Where subsequent therapies were included in the model, these were generally only considered from a cost and resource use perspective. The models generally used overall and progression-free survival as model inputs, although data were often immature. Sensitivity analyses were frequently reported (n = 41) whereas validation was only considered in less than half (n = 19) of the models.

CONCLUSIONS

Published economic models in relapsed/refractory multiple myeloma rarely followed an individual patient approach, mainly owing to the higher need for complex data assumptions compared with simpler modeling approaches. As many patients experience disease progression on multiple treatment lines, there is a growing need for modeling complex treatment strategies, leading to more sophisticated approaches in the future. Maintaining transparency, high reporting standards, and thorough analyses of uncertainty are crucial to support these advancements.

Improving resource allocation in the precision medicine Era: a simulation-based approach using R

The application of personalized medicine in developing countries is a major challenge, especially for those with poor economic status. A critical factor in improving the application of personalized medicine is the efficient allocation of resources. In healthcare systems, optimizing resource allocation without compromising patient care is paramount. This tutorial employs a simulation-based approach to evaluate the efficiency of bed allocation within a hospital setting. Utilizing a patient arrival model with an exponential distribution, we simulated patient trajectories to examine system bottlenecks, particularly focusing on waiting times. Initial simulations painted a scenario of an 'unstable' system, where waiting times and queue lengths surged due to the limited number of available beds. This research offers insights for hospital management on resource optimization leading to improved patient care.

Discrete Event Simulation to Incorporate Infusion Wait-Time When Assessing Cost-Effectiveness of a Chimeric-Antigen Receptor T Cell Therapy

OBJECTIVES

The main objective was to use discrete event simulation to model the impact of wait-time, defined as the time between leukapheresis and chimeric antigen receptor (CAR-T) infusion, when assessing the cost-effectiveness of tisagenlecleucel in young patients with relapsed/refractory acute lymphoblastic leukemia.

METHODS

The movement of patients through the model was determined by parametric time-to-event distributions, with the competing risk of an event determining the costs and quality-adjusted life-years (QALYs) assigned. Cost-effectiveness was expressed using the incremental cost-effectiveness ratio (ICER) for tisagenlecleucel compared with chemotherapy over the lifetime.

RESULTS

The base case generated a total of 5.79 QALYs and $622 872 for tisagenlecleucel and 1.19 QALYs and $181 219 for blinatumomab, resulting in an ICER of $96 074 per QALY. An increase in mean CAR-T wait-time to 6.20 months reduced the benefit and costs of tisagenlecleucel to 2.78 QALYs and $294 478 because of fewer patients proceeding to infusion, reducing the ICER to $71 112 per QALY. Alternatively, when the cost of tisagenlecleucel was assigned pre-infusion in sensitivity analysis, the ICER increased with increasing wait-time.

CONCLUSIONS

Under a payment arrangement where CAR-T cost is incurred post-infusion, the loss of benefit to patients is not reflected in the ICER. This may be misguiding to decision makers, where cost-effectiveness ratios are used to guide resource allocation. discrete event simulation is an important tool for economic modeling of CAR-T as it is amenable to capturing the impact of wait-time, facilitating better understanding of factors affecting service delivery and consequently informed decision making to deliver faster access to CAR-T for patients.

Cost Effectiveness of Denosumab for Secondary Prevention of Osteoporotic Fractures Among Postmenopausal Women in China: An Individual-Level Simulation Analysis

OBJECTIVE

This study evaluated the cost effectiveness of denosumab versus alendronate for secondary prevention of osteoporotic fractures among post-menopausal women in China.

METHODS

A validated individual-level simulation model of osteoporotic fractures in the Chinese setting was adapted. Allowing both treatment discontinuation and waning effects, the analysis aimed to evaluate the incremental cost-effectiveness ratio of denosumab compared to alendronate by simulating a cohort of previously fractured individuals over the residual lifetime from the healthcare system perspective. Hip, vertebral, and wrist/humeral fractures were tracked along with the associated medical costs and quality-adjusted life-years. Age-related health state utility values, health state utility values of fractures, costs, fracture incidence, and mortality risks for Chinese were used whenever available. Comparative effectiveness data were obtained from a published network meta-analysis. One-way and probabilistic sensitivity analyses were conducted.

RESULTS

In the base case, denosumab was dominated by alendronate with incremental costs of CN¥2743 (US$425) and incremental health outcomes of - 0.20 quality-adjusted life-years at its current price in mainland China. It remained dominated in all one-way sensitivity analysis robustness checks. However, denosumab was cost effective if both drugs did not carry any waning effects. In the probabilistic sensitivity analysis, denosumab remained dominated in all replications.

CONCLUSIONS

Denosumab is not cost effective for preventing secondary fractures among overall postmenopausal women in China. It is advisable to identify alternative denosumab regimens for high-risk subgroups among previously fractured postmenopausal women.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science

Objectives

Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience.

Methods

To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends.

Results

Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased.

Conclusions

Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Cost-Effectiveness Analysis of Pharmacokinetic-Guided Prophylaxis Versus Standard Prophylaxis in Adults with Severe Hemophilia A in China

INTRODUCTION

Multiple studies have demonstrated the effectiveness of pharmacokinetic (PK)-guided individualized prophylaxis with human coagulation factor VIII (FVIII) compared with standard prophylaxis, but no studies have evaluated the economics of PK-guided prophylaxis in China. Hence, we conducted this study to assess the cost-effectiveness of PK-guided prophylaxis with recombinant FVIII (rFVIII) versus standard prophylaxis in Chinese adult patients with severe hemophilia A.

METHODS

A discrete event simulation model was developed to simulate 10,000 patients with hemophilia A who received rFVIII treatment over a 1-year time horizon. The standard prophylaxis rFVIII dose was 30 IU/kg by intravenous injection. The PK-guided prophylaxis dosage was adjusted for each patient to maintain FVIII trough level at 1-5 IU/dL. Dosing interval for both approaches was kept fixed at 48 h. The health outcomes included annual joint bleed rate (AJBR) and quality-adjusted life years (QALYs). The model considered the costs of drug. Incremental cost-effectiveness ratio (ICER) was estimated and scenario analysis was performed.

RESULTS

A total of 94.3% of patients receiving PK-guided individualized prophylaxis achieved the goal of maintaining the trough concentration at 1-5 IU/dL compared with 62.7% on standard prophylaxis. AJBR and QALYs gained in PK-guided and standard prophylaxis were 1.527 vs 1.601, and 0.8384 vs 0.8383, respectively. Costs of drug prophylaxis and costs of treatment for bleeding events in PK-guided prophylaxis (148,641.47 USD; 4546.43 USD) were lower than those in standard prophylaxis (159,620.93 USD; 4753.39 USD). An average saving of USD 11,186.47 was obtained by the PK-guided approach. The prophylaxis treatment scenarios were the most influential factors.

CONCLUSION

PK-guided individualized prophylaxis appeared to be a dominant treatment compared with standard prophylaxis, with slightly higher QALYs but lower total costs.

Long-Term Cost-Effectiveness Comparison of Catheter Ablation and Antiarrhythmic Drugs in Atrial Fibrillation Treatment Using Discrete Event Simulation

OBJECTIVES

To evaluate the lifetime cost-effectiveness of 3 widely used atrial fibrillation (AF) treatments from the perspectives of Chinese healthcare system: antiarrhythmic drugs (AADs), ThermoCool SmartTouch guided by ablation index (STAI), and second-generation cryoballoon (CB2).

METHODS

A discrete event simulation (DES) model was implemented to compare the lifetime cost-effectiveness of AADs, STAI, and CB2. AF disease progression was explicitly modeled based on the Atrial Fibrillation Progression Trial clinical study results. The base-case analysis assumed that patients with paroxysmal AF (PAF) entered the model at the age of 55 years and had a CHA2DS2-VASc (Congestive heart failure, Hypertension, Age ( > 65 = 1 point, > 75 = 2 points), Diabetes, previous Stroke/transient ischemic attack (2 points)-Vascular disease (peripheral arterial disease, previous myocardial infarction, aortic atheroma), Age 65 to 74 years, and Sex category) score of 2 for males and 3 for females. Model parameter uncertainties were incorporated throughout the DES simulation with full probabilistic model parameterization.

RESULTS

The lifetime cost-effectiveness evaluations showed that patients treated with AADs gained an average of 4.98 quality-adjusted life-years (QALYs) and 9.63 life-years (LYs) at an average cost of US dollar (USD) 15 374. Patients treated with CB2 gained 5.92 QALYs and 10.74 LYs at an average cost of USD 26 811. The STAI group gained an average of 6.55 QALYs and 11.57 LYs at an average cost of USD 24 722. The incremental cost-effectiveness ratios was USD 5927 and USD 12 167 per QALY for STAI versus AADs and CB2 versus AADs, respectively. Assuming the willingness-to-pay threshold for China is USD 30 390 per QALY, both ablation treatments will be cost-effective compared with AADs for patients with PAF.

CONCLUSIONS

The DES model demonstrated that catheter ablations are more cost-effective than AADs for patients with PAF under the healthcare system in China. Among catheter ablation technologies, STAI provides better outcomes at lower costs.

Being Transparent About Brilliant Failures: An Attempt to Use Real-World Data in a Disease Model for Patients with Castration-Resistant Prostate Cancer

BACKGROUND

Real-world disease models spanning multiple treatment lines can provide insight into the (cost) effectiveness of treatment sequences in clinical practice.

OBJECTIVE

Our objective was to explore whether a disease model based solely on real-world data (RWD) could be used to estimate the effectiveness of treatments for patients with castration-resistant prostate cancer (CRPC) that could then be suitably used in a cost-effectiveness analysis.

METHODS

We developed a patient-level simulation model using patient-level data from the Dutch CAPRI registry as input parameters. Time to event (TTE) and overall survival (OS) were estimated with multivariate regression models, and type of event (i.e., next treatment or death) was estimated with multivariate logistic regression models. To test internal validity, TTE and OS from the simulation model were compared with the observed outcomes in the registry.

RESULTS

Although patient characteristics and survival outcomes of the simulated data were comparable to those in the observed data (median OS 20.6 vs. 19.8 months, respectively), the disease model was less accurate in estimating differences between treatments (median OS simulated vs. observed population: 18.6 vs. 17.9 [abiraterone acetate plus prednisone], 24.0 vs. 25.0 [enzalutamide], 20.2 vs. 18.7 [docetaxel], and 20.0 vs. 23.8 months [radium-223]).

CONCLUSIONS

Overall, the disease model accurately approximated the observed data in the total CRPC population. However, the disease model was unable to predict differences in survival between treatments due to unobserved differences. Therefore, the model is not suitable for cost-effectiveness analysis of CRPC treatment. Using a combination of RWD and data from randomised controlled trials to estimate treatment effectiveness may improve the model.

Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories

OBJECTIVES

To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19.

METHODS

An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data.

RESULTS

To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day.

CONCLUSIONS

The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation

Drive-through clinics have previously been utilized in vaccination efforts and are now being more widely adopted for COVID-19 vaccination in different parts of the world by offering many advantages including utilizing existing infrastructure, large daily throughput and enforcing social distancing by default. Successful, effective, and efficient drive-through facilities require a suitable site and keen focus on layout and process design. To demonstrate the role that high fidelity computer simulation can play in planning and design of drive-through mass vaccination clinics, we used multiple integrated discrete event simulation (DES) and agent-based modelling methods. This method using AnyLogic simulation software to aid in planning, design, and implementation of one of the largest and most successful early COVID-19 mass vaccination clinics operated by UCHealth in Denver, Colorado. Simulations proved to be helpful in aiding the optimization of UCHealth drive through mass vaccination clinic design and operations by exposing potential bottlenecks, overflows, and queueing, and clarifying the necessary number of supporting staff. Simulation results informed the target number of vaccinations and necessary processing times for different drive through station set ups and clinic formats. We found that modern simulation tools with advanced visual and analytical capabilities to be very useful for effective planning, design, and operations management of mass vaccination facilities.

Modeling Early Warning Systems: Construction and Validation of a Discrete Event Simulation Model for Heart Failure

OBJECTIVES

Developing and validating a discrete event simulation model that is able to model patients with heart failure managed with usual care or an early warning system (with or without a diagnostic algorithm) and to account for the impact of individual patient characteristics in their health outcomes.

METHODS

The model was developed using patient-level data from the Trans-European Network - Home-Care Management System study. It was coded using RStudio Version 1.3.1093 (version 3.6.2.) and validated along the lines of the Assessment of the Validation Status of Health-Economic decision models tool. The model includes 20 patient and disease characteristics and generates 8 different outcomes. Model outcomes were generated for the base-case analysis and used in the model validation.

RESULTS

Patients managed with the early warning system, compared with usual care, experienced an average increase of 2.99 outpatient visits and a decrease of 0.02 hospitalizations per year, with a gain of 0.81 life years (0.45 quality-adjusted life years) and increased average total costs of €11 249. Adding a diagnostic algorithm to the early warning system resulted in a 0.92 life year gain (0.57 quality-adjusted life years) and increased average costs of €9680. These patients experienced a decrease of 0.02 outpatient visits and 0.65 hospitalizations per year, while they avoided being hospitalized 0.93 times. The model showed robustness and validity of generated outcomes when comparing them with other models addressing the same problem and with external data.

CONCLUSIONS

This study developed and validated a unique patient-level simulation model that can be used for simulating a wide range of outcomes for different patient subgroups and treatment scenarios. It provides useful information for guiding research and for developing new treatment options by showing the hypothetical impact of these interventions on a large number of important heart failure outcomes.

Cost-Effectiveness of Propofol (Diprivan) Versus Inhalational Anesthetics to Maintain General Anesthesia in Noncardiac Surgery in the United States

OBJECTIVES

It is not known whether using propofol total intravenous anaesthesia (TIVA) to reduce incidence of postoperative nausea and vomiting (PONV) is cost-effective. We assessed the economic impact of propofol TIVA versus inhalational anesthesia in adult patients for ambulatory and inpatient procedures relevant to the US healthcare system.

METHODS

Two models simulate individual patient pathways through inpatient and ambulatory surgery with propofol TIVA or inhalational anesthesia with economic inputs from studies on adult surgical US patients. Efficacy inputs were obtained from a meta-analysis of randomized controlled trials. Probabilistic and deterministic sensitivity analyses assessed the robustness of the model estimates.

RESULTS

Lower PONV rate, shorter stay in the post-anesthesia care unit, and reduced need for rescue antiemetics offset the higher costs for anesthetics, analgesics, and muscle relaxants with propofol TIVA and reduced cost by 11.41 ± 10.73 USD per patient in the inpatient model and 11.25 ± 9.81 USD in the ambulatory patient model. Sensitivity analyses demonstrated strong robustness of the results.

CONCLUSIONS

Maintenance of general anesthesia with propofol was cost-saving compared to inhalational anesthesia in both inpatient and ambulatory surgical settings in the United States. These economic results support current guideline recommendations, which endorse propofol TIVA to reduce PONV risk and enhance postoperative recovery.

Overview and Use of Tools for Selecting Modelling Techniques in Health Economic Studies

The availability and use of tools to guide the choice of modelling technique are not well understood. Our study aims to review existing tools and explore the use of those tools in health economic models. Two reviews and one case study were conducted. Review 1 aimed to identify tools based on expert opinion and citation searching and explore the value of the tools for health economic models. Review 2, based on citation searching, aimed to describe how those tools have been used in health economic models. Both reviews were conducted using Web of Science and Scopus. Two independent reviewers selected studies for inclusion. A case study, focused on economic evaluations of antipsychotic medication in schizophrenia, was conducted to compare the modelling techniques used by existing models with modelling techniques recommended by identified tools. Seven tools were identified, of which the revised Brennan's toolkit, was assessed to be the most appropriate for health economic models. The seven tools were cited 126 times in publications reporting health economic models. Only 17 of these (13.5%) reported that they used the tool(s) to guide the choice of modelling technique. Application of these tools suggested discrete event simulation is most appropriate for modelling antipsychotic medication in schizophrenia, but discrete event simulation was only used by 17% of existing models. There is considerable inconsistency between the modelling techniques used by existing models and modelling techniques recommended by tools. It is recommended that for future modelling studies the choice of modelling technique should be justified, this can be achieved by the application of model selection tools, such as the revised Brennan's toolkit. Future research is required to explore the barriers to using model selection tools in health economic models and to update existing tools and make them easier to use.

Burden of cardiovascular diseases associated with fine particulate matter in Beijing, China: an economic modelling study

Objective

To evaluate the economic and humanistic burden associated with cardiovascular diseases that were attributable to fine particulate matter (≤ 2.5 μg/m³ in aerodynamic diameter; PM_2.5) in Beijing.

Methods

This study used a health economic modelling approach to compare the actual annual average PM_2.5 concentration with the PM_2.5 concentration limit (35 µg/m³) as defined by the Chinese Ambient Air Quality Standard in terms of cardiovascular disease outcomes in Beijing adult population. The outcomes included medical costs, quality-adjusted life-years (QALYs) and net monetary loss (NML). Beijing annual average PM_2.5 concentration was around 105 µg/m³ during 2013–2015. Therefore, we estimated the differences in cardiovascular outcomes of Beijing adults between exposure to the PM_2.5 concentration of 105 µg/m³ and exposure to the concentration of 35 µg/m³. According to WHO estimates, the hazard ratios of coronary heart disease and stroke associated with the increase of PM_2.5 concentration from 35 to 105 µg/m³ were 1.15 and 1.29, respectively.

Results

The total 1-year excess medical costs of cardiovascular diseases associated with PM_2.5 pollution in Beijing was US$147.9 million and the total 1-year QALY loss was 92 574 in 2015, amounting to an NML of US$2281.8 million. The expected lifetime incremental costs for a male Beijing adult and a female Beijing adult were US$237 and US$163, the corresponding QALY loss was 0.14 and 0.12, and the corresponding NML was US$3514 and US$2935.

Conclusions

PM_2.5-related cardiovascular diseases imposed high economic and QALY burden on Beijing society. Continuous and intensive investment on reducing PM_2.5 concentration is warranted even when only cardiovascular benefits are considered.

Leveraging DICE (Discretely-Integrated Condition Event) Simulation to Simplify the Design and Implementation of Hybrid Models

OBJECTIVES

Using an example of an existing model constructed by the National Institute for Health and Care Excellence (NICE) to inform a real health technology assessment, this study seeks to demonstrate how a discretely integrated condition event (DICE) simulation can improve the implementation of Markov models.

METHODS

Using the technical report and spreadsheet, the original model was translated to a standard DICE simulation without making any changes to the design. All original analyses were repeated and the results were compared. Aspects that could have improved the original design were then considered.

RESULTS

The original model consisted of 32 copies (8 risk strata × 4 treatments) of the Markov structure, containing more than 6000 Microsoft Excel® formulas (18 MB files). Three aspects (nonadherence, scheduled treatment stop, and end of fracture risk) were handled by incorporating weighted averages into the cycle-specific calculations. The DICE implementation used 3 conditions to represent the states and a single transition event to apply the probabilities; 3 additional events processed the special aspects, and profiles handled the 8 strata (0.12 MB file). One replication took 16 seconds. The original results were reproduced but extensive additional sensitivity analyses, including structural analyses, were enabled.

CONCLUSION

Implementing a real Markov model using DICE simulation both preserves the advantages of the approach and expands the available tools, improving transparency and ease of use and review.

Generating evidence to inform health technology assessment of treatments for SLE: a systematic review of decision-analytic model-based economic evaluations

This study aimed to understand and appraise the approaches taken to handle the complexities of a multisystem disease in published decision-analytic model-based economic evaluations of treatments for SLE. A systematic review was conducted to identify all published model-based economic evaluations of treatments for SLE. Treatments that were considered for inclusion comprised antimalarial agents, immunosuppressive therapies, and biologics including rituximab and belimumab. Medline and Embase were searched electronically from inception until September 2018. Titles and abstracts were screened against the inclusion criteria by two reviewers; agreement between reviewers was calculated according to Cohen’s κ. Predefined data extraction tables were used to extract the key features, structural assumptions and data sources of input parameters from each economic evaluation. The completeness of reporting for the methods of each economic evaluation was appraised according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Six decision-analytic model-based economic evaluations were identified. The studies included azathioprine (n=4), mycophenolate mofetil (n=3), cyclophosphamide (n=2) and belimumab (n=1) as relevant comparator treatments; no economic evaluation estimated the relative cost-effectiveness of rituximab. Six items of the CHEERS statement were reported incompletely across the sample: target population, choice of comparators, measurement and valuation of preference-based outcomes, estimation of resource use and costs, choice of model, and the characterisation of heterogeneity. Complexity in the diagnosis, management and progression of disease can make decision-analytic model-based economic evaluations of treatments for SLE a challenge to undertake. The findings from this study can be used to improve the relevance of model-based economic evaluations in SLE and as an agenda for research to inform future health technology assessment and decision-making.

The Diffusion of Discrete Event Simulation Approaches in Health Care Management in the Past Four Decades: A Comprehensive Review

This study systematically examines the diffusion of the discrete event simulation (DES) approach in health services and health care management by examining relevant factors such as research areas, channels with the objective of promoting the application of DES in the health field. We examined 483 journal papers referencing this approach that were published in 230 journals during 1981 to 2014. The application of DES has extended from health service operational research evaluation to the assessment of interventions in diverse health arenas. The increase in the number of adopters (paper authors) of DES and the increase in number of related channels (journals publishing DES-related articles) are highly correlated, which suggests an increase of DES-related publications in health research. The same conclusion is reached, that is, an increased diffusion of DES in health research, when we focus on the temporal trends of the channels and adopters. The applications of DES in health research cover 22 major areas based on our categorization. The expansion in the health areas also suggests to a certain extent the rapid diffusion of DES in health research.

Application of Discrete-Event Simulation in Health Technology Assessment: A Cost-Effectiveness Analysis of Alzheimer's Disease Treatment Using Real-World Evidence in Thailand

OBJECTIVES

Decision-analytic models for Alzheimer's disease (AD) have been advanced to a discrete-event simulation (DES), in which individual-level modeling of disease progression across continuous severity spectra become feasible. This study aimed to apply DES to perform cost-effectiveness analysis of AD treatment in Thailand.

METHODS

A data set of Thai AD patients, representing unique demographic and clinical characteristics, was bootstrapped to generate a baseline cohort of 50 000 patients. Each patient was cloned and assigned to donepezil, galantamine, rivastigmine, memantine, or no treatment. Correlated changes in cognitive and behavioral status over time were developed using patient-level data. Treatment effects were obtained from the most recent network meta-analysis. Treatment persistence; mortality; and predictive equations for functional status, costs (Thai baht in 2017), and quality-adjusted life-year (QALY) were derived from country-specific real-world data.

RESULTS

From a societal perspective, only the prescription of donepezil to AD patients with all disease-severity levels was found to be cost-effective (incremental cost-effectiveness ratio): 138 524 Thai baht/QALY ($4062/QALY)]. Regardless of whether the treatment-stopping rule when the mini-mental state examination score <10 was introduced, providing early treatment with donepezil to mild AD patients further reduced the incremental cost-effectiveness ratio. Extensive sensitivity analyses indicated robust simulation findings.

CONCLUSIONS

Discrete-event simulation greatly enhances the real-world representativeness of decision-analytic models for AD. Donepezil is the most cost-effective treatment option for AD in Thailand and is worth being considered for universal financial coverage. Application of DES in heath technology assessment should be encouraged, especially when the validity of the model is questionable with classical modeling methods.

Cost-effectiveness of routine transoesophageal echocardiography during cardiac surgery: a discrete-event simulation study

BACKGROUND

The aim of this study was to simulate and compare the healthcare and economic outcomes associated with routine use of intraoperative transoesophageal echocardiography (TOE) in patients undergoing cardiac surgery with those associated with a scenario where TOE is not routinely used.

METHODS

The impact of TOE on surgical decision-making was estimated through a systematic literature review. Individual short-term morbidity and mortality estimates were generated by application of the Society of Thoracic Surgeons risk calculator. Long-term event rates, unit costs, and utility weights were sourced from published literature and expert opinion. A discrete-event simulation model was then constructed to simulate both the in-hospital and post-discharge outcomes for patients undergoing cardiac surgery. Robustness of the base case results was examined through deterministic and probabilistic sensitivity analyses. An incremental cost-effectiveness ratio of €30 000 per quality-adjusted life-year gained was assumed to represent acceptable cost-effectiveness.

RESULTS

Routine use of intraoperative TOE was associated with lower costs and higher benefits per patient, which indicates that use of TOE is a dominant strategy. The intervention resulted in the avoidance of 299 cardiac complications, 20 strokes, and 11 all-cause deaths per 10 000 patients. Routine intraoperative TOE was associated with an increased occurrence of bleeding owing to more valvular surgery and subsequent long-term anticoagulation.

CONCLUSIONS

Routine intraoperative TOE is a cost-effective procedure for patients undergoing cardiac surgery, leading to lower overall costs. It was associated with a decrease in long-term complications including stroke, cardiac complications, and death, although there was a slight increase in extracranial bleeding events.

The Structure and Parameterization of the Breast Cancer Transition Model Among Chinese Women

OBJECTIVES

Markov model simulation based on the natural history of disease is commonly employed for the comparative research of health interventions. The present study aims to simulate the natural progression of breast cancer and parameterize the initial and transition probabilities of multiple states of breast cancer development among Chinese women.

METHODS

The age-specific incidence, mortality, and clinical stage distribution of breast cancer; and relapse rate of each clinical stage were collected from China's cancer registry yearbooks and clinical epidemiological studies to simulate the process from full health to breast cancer to death among Chinese women aged 30 to 80 through a Markov cohort study. The validity analysis was conducted to evaluate the accuracy of the model estimation.

RESULTS

A Markov transition model with 7 states (no breast cancer, clinical stages 0-IV breast cancer, and death) was constructed for Chinese women. The age-specific incidence, mortality, and clinical stage distribution of breast cancer estimated by the initial and transition probabilities among different Markov states were highly consistent with the registered data and observed studies.

CONCLUSION

A breast cancer transition model for Chinese women has been established with validity. It could be a point of reference for further economic evaluations and breast cancer screening policy formulation.

Economic Evaluation of the 2016 Chinese Guideline and Alternative Risk Thresholds of Initiating Statin Therapy for the Management of Atherosclerotic Cardiovascular Disease

OBJECTIVE

The 2016 Chinese guidelines for the management of dyslipidemia recommended mixed rules that centered around a 10% 10-year risk threshold to initiate statins for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). The present study aimed to evaluate the cost-effectiveness of the guideline statin-initiation strategy and alternative strategies.

METHODS

A decision analytic model using discrete event simulation with event probabilities based on a validated ASCVD risk prediction tool for Chinese was constructed. Risk factor inputs were from the dataset of a nationally representative survey of middle-aged and elderly Chinese. Data of statin treatment effectiveness were from a published meta-analysis. Other key input data were identified from the literature or relevant databases. The strategies we evaluated were the guideline strategy, a 15% 10-year risk threshold strategy and a 20% 10-year risk threshold strategy. After excluding any extended dominance strategies, the incremental costs per quality-adjusted life year (QALY) gained of each strategy was calculated.

RESULTS

The 20% 10-year risk threshold strategy was an extended dominance option. The incremental costs per QALY gained from the 15% 10-year risk threshold strategy compared with no treatment and the guideline strategy compared with the 15% 10-year risk threshold strategy were CN¥69,309 and CN¥154,944, respectively. The results were robust in most sensitivity analyses.

CONCLUSIONS

The guideline strategy and the 15% 10-year risk threshold strategy are optimal when using the three times and the two times the gross domestic product per capita willingness-to-pay standards, respectively.

An introduction to the methods of decision-analytic modelling used in economic evaluations for Dermatologists

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.

Cost-effectiveness Analysis of Sequential Treatment of Abaloparatide Followed by Alendronate Versus Teriparatide Followed by Alendronate in Postmenopausal Women With Osteoporosis in the United States

BACKGROUND

The US Food and Drug Administration has recently approved abaloparatide (ABL) for treatment of women with postmenopausal osteoporosis (PMO) at high risk of fracture. With increasing health care spending and drug prices, it is important to quantify the value of newly available treatment options for PMO.

OBJECTIVE

To determine cost-effectiveness of ABL compared with teriparatide (TPTD) for treatment of women with PMO in the United States.

METHODS

A discrete-event simulation (DES) model was developed to assess cost-effectiveness of ABL from the US health care perspective. The model included three 18-month treatment strategies with either placebo (PBO), TPTD, or ABL, all followed by additional 5-year treatment with alendronate (ALN). High-risk patients were defined as women with PMO ⩾65 years old with a prior vertebral fracture. Baseline clinical event rates, risk reductions, and patient characteristics were based on the Abaloparatide Comparator Trial in Vertebral Endpoints (ACTIVE) trial.

RESULTS

Over a 10-year period, the DES model yielded average total discounted per-patient costs of $10 212, $46 783, and $26 837 and quality-adjusted life-years (QALYs) of 6.742, 6.781, and 6.792 for PBO/ALN, TPTD/ALN, and ABL/ALN, respectively. Compared with TPTD/ALN, ABL/ALN accrued higher QALYs at lower cost and produced an incremental cost-effectiveness ratio (ICER) of $333 266/QALY relative to PBO/ALN. In high-risk women, ABL/ALN also had more QALYs and less cost over TPTD/ALN and yielded an ICER of $188 891/QALY relative to PBO/ALN. Conclusion and Relevance: ABL is a dominant treatment strategy over TPTD. In women with PMO at high risk of fracture, ABL is an alternative cost-effective treatment.

Health Economics and Outcomes Research Knowledge Needs Assessment for Latin America

BACKGROUND

An increasing awareness of the importance of health economics and outcomes research (HEOR) skills has been reported in Latin America. There is, however, no published study directly assessing perceived knowledge levels and knowledge gaps on specific HEOR topics among professionals and students in the region.

OBJECTIVES

To assess perceived HEOR knowledge levels and identify knowledge gaps in Latin America.

METHODS

An online needs assessment survey was developed to quantify perceived HEOR knowledge levels and identify knowledge gaps. Members of the International Society for Pharmacoeconomics and Outcomes Research in the Latin American region, regional chapters, and student chapter presidents were invited to participate in the survey. The survey, developed using the SurveyMonkey tool, was distributed to participants electronically. Data were extracted from the survey and analyzed using Microsoft Excel. Data analysis was conducted using descriptive statistics to summarize the survey respondents' demographic information, current and desired knowledge levels, and preferred method/format for delivery of educational training.

RESULTS

Survey responses were collected from 106 participants. The largest knowledge gap was calculated for methods for integrating medication adherence and persistence in health economic evaluations (mean = 2.30 ± 1.48). The smallest knowledge gap was calculated for types of healthcare costs (mean = 1.01 ± 1.17). Most respondents (74% [n = 66]) preferred to receive educational materials related to HEOR topics through online learning and continuing education programs.

CONCLUSIONS

The knowledge gap assessment provided current knowledge gap perceptions among members of the International Society for Pharmacoeconomics and Outcomes Research in Latin America. The survey data collected support a need for developing educational programs for topics with the highest perceived knowledge gap.

Application of discrete event simulation in health care: a systematic review

BACKGROUND

The objective was to explore the current advances and extent of DES (Discrete Event Simulation) applied to assisting with health decision making, as well as to categorize the wide spectrum of health-related topics where DES was applied.

METHODS

A systematic review was conducted of the literature published over the last two decades. Original research articles were included and reviewed if they concentrated on the topic of DES technique applied to health care management with model frameworks explicitly demonstrated. No restriction regarding the settings of DES application was applied.

RESULTS

A total of 211 papers met the predefined inclusion criteria. The number of publications included increased significantly especially after 2010.101 papers (48%) stated explicitly disease areas targeted, the most frequently modeled of which are related to circulatory system, nervous system and Neoplasm. The DES applications were distributed unevenly into 4 major classes: health and care systems operation (HCSO) (65%), disease progression modeling (DPM) (28%), screening modeling (SM) (5%) and health behavior modeling (HBM) (2%). More than 68% of HCSO by DES were focused on specific problems in individual units. However, more attempts at modeling highly integrated health service systems as well as some new trends were identified.

CONCLUSIONS

DES technique has been an effective tool to approach a wide variety of health care issues. Among all DES applications in health care, health system operations research occupied the most considerable proportion and increased most significantly. Health Economic Evaluation (HEE) was the second most common topic for DES in health care, but with stable rather than increasing numbers of publications.

Treatment Carryover Impacts on Effectiveness of Intraocular Pressure Lowering Agents, Estimated by a Discrete Event Simulation Model

Purpose

To compare the effectiveness of two treatment sequences, latanoprost–latanoprost timolol fixed combination (L-LT) versus travoprost–travoprost timolol fixed combination (T-TT), in the treatment of open-angle glaucoma (OAG) or ocular hypertension (OHT).

Methods

A discrete event simulation (DES) model was constructed. Patients with either OAG or OHT were treated first-line with a prostaglandin, either latanoprost or travoprost. In case of treatment failure, patients were switched to the specific prostaglandin-timolol sequence LT or TT. Failure was defined as intraocular pressure higher than or equal to 18 mmHg at two visits. Time to failure was estimated from two randomized clinical trials. Log-rank tests were computed. Linear functions after log-log transformation were used to model time to failure. The time horizon of the model was 60 months. Outcomes included treatment failure and disease progression. Sensitivity analyses were performed.

Results

Latanoprost treatment resulted in more treatment failures than travoprost (p<0.01), and LT more than TT (p<0.01). At 60 months, the probability of starting a third treatment line was 39.2% with L-LT versus 29.9% with T-TT. On average, L-LT patients developed 0.55 new visual field defects versus 0.48 for T-TT patients. The probability of no disease progression at 60 months was 61.4% with L-LT and 65.5% with T-TT.

Conclusions

Based on randomized clinical trial results and using a DES model, the T-TT sequence was more effective at avoiding starting a third line treatment than the L-LT sequence. T-TT treated patients developed less glaucoma progression.

Parenteral nutrition including an omega-3 fatty-acid-containing lipid emulsion for intensive care patients in China: a pharmacoeconomic analysis

Background/objectives

Parenteral nutrition (PN) incorporating omega-3 fatty-acid-enriched lipid emulsions has been shown to be cost effective in Western populations. A pharmacoeconomic evaluation was performed within the Chinese intensive care unit (ICU) setting. This assessed whether the additional acquisition cost of PN with omega-3 fatty-acid-enriched lipid emulsion (SMOFlipid) vs standard PN was offset by improved clinical outcomes that can reduce subsequent costs.

Materials and methods

A pharmacoeconomic discrete event simulation model was developed, based on an update to efficacy data from a previous international meta-analysis, with China-specific clinical and economic input parameters. Sensitivity analyses were undertaken to assess the effects of uncertainty around input parameters.

Results

The model predicted that PN with an omega-3 fatty-acid-enriched lipid emulsion was more effective and less costly than PN with standard lipid emulsions for Chinese ICU patients, as follows: reduced length of overall hospital length of stay (19.48 vs 21.35 days, respectively), reduced length of ICU stay (5.03 vs 6.18 days, respectively), and prevention of 35.6% of nosocomial infections leading to a lower total cost per patient (¥47 189 [US $6937] vs ¥54 783 [US $8053], respectively). Additional treatment costs were offset by savings in overall hospital and ICU stay cost, and antibiotic cost, resulting in a mean cost saving of ¥7594 (US $1116) per patient. Sensitivity analyses confirmed the robustness of these findings.

Conclusions

PN enriched with an omega-3 fatty-acid-containing lipid emulsion vs standard PN may be effective in reducing length of hospital and ICU stay and infectious complications in Chinese ICU patients, and also decreases overall treatment costs. This results in a favorable cost-effectiveness ratio. Thus, PN enriched with an omega-3 fatty-acid-containing lipid emulsion can be seen as a win–win situation for patients, hospital administration, and health insurance companies.

Model-Based Economic Evaluation of Treatments for Depression: A Systematic Literature Review

BACKGROUND

An increasing number of model-based studies that evaluate the cost effectiveness of treatments for depression are being published. These studies have different characteristics and use different simulation methods.

OBJECTIVE

We aimed to systematically review model-based studies evaluating the cost effectiveness of treatments for depression and examine which modelling technique is most appropriate for simulating the natural course of depression.

METHODS

The literature search was conducted in the databases PubMed, EMBASE and PsycInfo between 1 January 2002 and 1 October 2016. Studies were eligible if they used a health economic model with quality-adjusted life-years or disability-adjusted life-years as an outcome measure. Data related to various methodological characteristics were extracted from the included studies. The available modelling techniques were evaluated based on 11 predefined criteria.

RESULTS

This methodological review included 41 model-based studies, of which 21 used decision trees (DTs), 15 used cohort-based state-transition Markov models (CMMs), two used individual-based state-transition models (ISMs), and three used discrete-event simulation (DES) models. Just over half of the studies (54%) evaluated antidepressants compared with a control condition. The data sources, time horizons, cycle lengths, perspectives adopted and number of health states/events all varied widely between the included studies. DTs scored positively in four of the 11 criteria, CMMs in five, ISMs in six, and DES models in seven.

CONCLUSION

There were substantial methodological differences between the studies. Since the individual history of each patient is important for the prognosis of depression, DES and ISM simulation methods may be more appropriate than the others for a pragmatic representation of the course of depression. However, direct comparisons between the available modelling techniques are necessary to yield firm conclusions.

Cost-effectiveness of surgical interventions for the management of osteoarthritis: a systematic review of the literature

Background

The primary purpose of this study is to assess the existing evidence on the cost-effectiveness of surgical interventions for the management of knee and hip osteoarthritis by systematically reviewing published economic evaluation studies.

Methods

A systematic review was conducted for the period 2004 to 2016. Electronic databases were searched to identify both trial and model based economic evaluation studies that evaluated surgical interventions for knee and hip osteoarthritis.

Results

A total of 23 studies met the inclusion criteria and an assessment of these studies showed that total knee arthroplasty (TKA), and total hip arthroplasty (THA) showed evidence of cost-effectiveness and improvement in quality of life of the patients when compared to non-operative and non-surgical procedures. On the other hand, even though delaying TKA and THA may lead to some cost savings in the short-run, the results from the study showed that this was not a cost-effective option.

Conclusions

TKA and THA are cost-effective and should be recommended for the management of patients with end stage/severe knee and hip OA. However, there needs to be additional studies to assess the cost-effectiveness of other surgical interventions in order for definite conclusions to be reached.

Electronic supplementary material

The online version of this article (doi:10.1186/s12891-017-1540-2) contains supplementary material, which is available to authorized users.

Better guidelines for better care: accounting for multimorbidity in clinical guidelines – structured examination of exemplar guidelines and health economic modelling

Background

Multimorbidity is common but most clinical guidelines focus on single diseases.

Aim

To test the feasibility of new approaches to developing single-disease guidelines to better account for multimorbidity.

Design

Literature-based and economic modelling project focused on areas where multimorbidity makes guideline application problematic.

Methods

(1) Examination of accounting for multimorbidity in three exemplar National Institute for Health and Care Excellence guidelines (type 2 diabetes, depression, heart failure); (2) examination of the applicability of evidence in multimorbidity for the exemplar conditions; (3) exploration of methods for comparing absolute benefit of treatment; (4) incorporation of treatment pay-off time and competing risk of death in an exemplar economic model for long-term preventative treatments with slowly accruing benefit; and (5) development of a discrete event simulation model-based cost-effectiveness analysis for people with both depression and coronary heart disease.

Results

(1) Comorbidity was rarely accounted for in the clinical research questions that framed the development of the exemplar guidelines, and was rarely accounted for in treatment recommendations. Drug–disease interactions were common only for comorbid chronic kidney disease, but potentially serious drug–drug interactions between recommended drugs were common and rarely accounted for in guidelines. (2) For all three conditions, the trials underpinning treatment recommendations largely excluded older, more comorbid and more coprescribed patients. The implications of low applicability varied by condition, with type 2 diabetes having large differences in comorbidity, whereas potentially serious drug–drug interactions were more important for depression. (3) Comparing absolute benefit of treatments for different conditions was shown to be technically feasible, but only if guideline developers are willing to make a number of significant assumptions. (4) The lifetime absolute benefit of statins for primary prevention is highly sensitive to the presence of both the direct treatment disutility of taking a daily tablet and competing risk of death. (5) It was feasible to use a discrete event simulation-based model to represent the relevant care pathways to estimate the relative cost-effectiveness of pharmacological treatments of major depressive disorder in primary care for patients who are also likely to go on and receive treatment for coronary heart disease but the analysis was reliant on eliciting some parameter values from experts, which increases the inherent uncertainty in the results. The key limitation was that real-life use in guideline development was not examined.

Conclusions

Guideline developers could feasibly (1) use epidemiological data characterising the guideline population to inform consideration of applicability and interactions; (2) systematically compare the absolute benefit of long-term preventative treatments to inform decision-making in people with multimorbidity and high treatment burden; and (3) modify the output from economic models used in guideline development to examine time to benefit in terms of the pay-off time and varying competing risk of death from other conditions.

Future work

Further research is needed to optimise presentation of comparative absolute benefit information to clinicians and patients, to evaluate the use of epidemiological and time-to-benefit data in guideline development, to better quantify direct treatment disutility and to better quantify benefit and harm in people with multimorbidity.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

A Systematic Review and Critical Appraisal of Economic Evaluations of Pharmacological Interventions for People with Bipolar Disorder

BACKGROUND

Bipolar disorder (BD) is a chronic mood disorder that causes substantial psychological and financial burden. Various pharmacological treatments are effective in the management and prevention of acute episodes of BD. In an era of tighter healthcare budgets and a need for more efficient use of resources, several economic evaluations have evaluated the cost effectiveness of treatments for BD.

OBJECTIVE

The aim of this study was to systematically review and appraise published economic evaluations of pharmacological interventions for BD.

METHODS

A systematic search combining search terms specific to BD with a health economics search filter was conducted on six bibliographic databases (EMBASE, MEDLINE, PsycINFO, HTA, NHS EED, CENTRAL) in order to identify trial- or model-based full economic evaluations of pharmacological treatments of any phase of the disorder that were published between 1 January 1990 and 18 December 2015. Studies that met the inclusion criteria were critically appraised using the Quality of Health Economic Studies (QHES) checklist, and synthesised in a narrative way.

RESULTS

The review included 19 economic studies, which varied with regard to the type and number of interventions assessed, the study design, the phase of treatment (acute or maintenance), the source of efficacy data and the method for evidence synthesis, the outcome measures, the time horizon and the countries/settings in which the studies were conducted. The study quality was variable but the majority of studies were of high or fair quality.

CONCLUSION

Pharmacological interventions are cost effective, compared with no treatment, in the management of BD, both in the acute and maintenance phases. However, it is difficult to draw safe conclusions on the relative cost effectiveness between drugs due to differences across studies and limitations characterising many of them. Future economic evaluations need to consider the whole range of treatment options available for the management of BD and adopt appropriate methods for evidence synthesis and economic modelling, to explore more robustly the relative cost effectiveness of pharmacological interventions for people with BD.

Evaluation on the cost-effective threshold of osteoporosis treatment on elderly women in China using discrete event simulation model

This study used a simulation model to determine the cost-effective threshold of fracture risk to treat osteoporosis among elderly Chinese women. Osteoporosis treatment is cost-effective among average-risk women who are at least 75 years old and above-average-risk women who are younger than 75 years old.

INTRODUCTION

Aging of the Chinese population is imposing increasing economic burden of osteoporosis. This study evaluated the cost-effectiveness of osteoporosis treatment among the senior Chinese women population.

METHODS

A discrete event simulation model using age-specific probabilities of hip fracture, clinical vertebral fracture, wrist fracture, humerus fracture, and other fracture; costs (2015 US dollars); and quality-adjusted life years (QALYs) was used to assess the cost-effectiveness of osteoporosis treatment. Incremental cost-effectiveness ratio (ICER) was calculated. The willingness to pay (WTP) for a QALY in China was compared with the calculated ICER to decide the cost-effectiveness. To determine the absolute 10-year hip fracture probability at which the osteoporosis treatment became cost-effective, average age-specific probabilities for all fractures were multiplied by a relative risk (RR) that was systematically varied from 0 to 10 until the WTP threshold was observed for treatment relative to no intervention. Sensitivity analyses were also performed to evaluate the impacts from WTP and annual treatment costs.

RESULTS

In baseline analysis, simulated ICERs were higher than the WTP threshold among Chinese women younger than 75, but much lower than the WTP among the older population. Sensitivity analyses indicated that cost-effectiveness could vary due to a higher WTP threshold or a lower annual treatment cost. A 30 % increase in WTP or a 30 % reduction in annual treatment costs will make osteoporosis treatment cost-effective for Chinese women population from 55 to 85.

CONCLUSIONS

The current study provides evidence that osteoporosis treatment is cost-effective among a subpopulation of Chinese senior women. The results also indicate that the cost-effectiveness of using osteoporosis treatment is sensitive to the WTP threshold and annual treatment costs.

Markov Influence Diagrams

Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs—including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis—with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.

Expected lifetime numbers, risks, and burden of osteoporotic fractures for 50-year old Chinese women: a discrete event simulation incorporating FRAX

This work was undertaken to provide an estimation of expected lifetime numbers, risks, and burden of fractures for 50-year-old Chinese women. A discrete event simulation model was developed to simulate the lifetime fractures of 50-year-old Chinese women at average risk of osteoporotic fracture. Main events in the model included hip fracture, clinical vertebral fracture, wrist fracture, humerus fracture, and other fracture. Fracture risks were calculated using the FRAX^® tool. Simulations of 50-year-old Chinese women without fracture risks were also carried out as a comparison to determine the burden of fractures. A 50-year-old Chinese woman at average risk of fracture is expected to experience 0.135 (95 % CI: 0.134-0.137) hip fractures, 0.120 (95 % CI: 0.119-0.122) clinical vertebral fractures, 0.095 (95 % CI: 0.094-0.096) wrist fractures, 0.079 (95 % CI: 0.078-0.080) humerus fractures, and 0.407 (95 % CI: 0.404-0.410) other fractures over the remainder of her life. The residual lifetime risk of any fracture, hip fracture, clinical vertebral fracture, wrist fracture, humerus fracture, and other fracture for a 50-year-old Chinese woman is 37.36, 11.77, 10.47, 8.61, 7.30, and 27.80 %, respectively. The fracture-attributable excess quality-adjusted life year (QALY) loss and lifetime costs are estimated at 0.11 QALYs (95 % CI: 0.00-0.22 QALYs) and US $714.61 (95 % CI: US $709.20-720.02), totaling a net monetary benefit loss of US $1,104.43 (95 % CI: US $904.09-1,304.78). Chinese women 50 years of age are at high risk of osteoporotic fracture, and the expected economic and quality-of-life burden attributable to osteoporotic fractures among Chinese women is substantial.

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.

Clopidogrel versus aspirin in patients with recent ischemic stroke and established peripheral artery disease: an economic evaluation in a Chinese setting

BACKGROUND AND OBJECTIVES

Clopidogrel or aspirin are indicated for patients with recent ischemic stroke (IS) or established peripheral artery disease (PAD). We compared the cost effectiveness of clopidogrel with that of aspirin in Chinese patients with recent IS or established PAD.

METHODS

A discrete-event simulation was developed to evaluate the economic implications of secondary prevention with clopidogrel versus aspirin. All available evidence was derived from clinical studies. Costs from a Chinese healthcare perspective in 2013 US dollars and quality-adjusted life-years (QALYs) were projected over patients' lifetimes. Uncertainties were addressed using sensitivity analyses.

RESULTS

Compared with aspirin, clopidogrel yielded a marginally increased life expectancy by 0.46 and 0.21 QALYs at an incremental cost-effectiveness ratio of $US5246 and $US9890 per QALY in patients with recent IS and established PAD, respectively. One-way sensitivity analyses showed that the evaluation of patients with PAD and recent IS was robust except for the parameter of patient age. Given a willingness-to-pay of $US19,877 per QALY gained, clopidogrel had a probability of 90 and 68% of being cost effective in the recent IS or established PAD subgroups compared with aspirin, respectively.

CONCLUSIONS

The analysis suggests that clopidogrel for secondary prevention is cost effective for patients with either PAD or recent IS in a Chinese setting in comparison with aspirin.

Activating clinical trials: a process improvement approach

Background

The administrative process associated with clinical trial activation has been criticized as costly, complex, and time-consuming. Prior research has concentrated on identifying administrative barriers and proposing various solutions to reduce activation time, and consequently associated costs. Here, we expand on previous research by incorporating social network analysis and discrete-event simulation to support process improvement decision-making.

Methods

We searched for all operational data associated with the administrative process of activating industry-sponsored clinical trials at the Office of Clinical Research of the University of South Florida in Tampa, Florida. We limited the search to those trials initiated and activated between July 2011 and June 2012. We described the process using value stream mapping, studied the interactions of the various process participants using social network analysis, and modeled potential process modifications using discrete-event simulation.

Results

The administrative process comprised 5 sub-processes, 30 activities, 11 decision points, 5 loops, and 8 participants. The mean activation time was 76.6 days. Rate-limiting sub-processes were those of contract and budget development. Key participants during contract and budget development were the Office of Clinical Research, sponsors, and the principal investigator. Simulation results indicate that slight increments on the number of trials, arriving to the Office of Clinical Research, would increase activation time by 11 %. Also, incrementing the efficiency of contract and budget development would reduce the activation time by 28 %. Finally, better synchronization between contract and budget development would reduce time spent on batching documentation; however, no improvements would be attained in total activation time.

Conclusion

The presented process improvement analytic framework not only identifies administrative barriers, but also helps to devise and evaluate potential improvement scenarios. The strength of our framework lies in its system analysis approach that recognizes the stochastic duration of the activation process and the interdependence between process activities and entities.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1227-2) contains supplementary material, which is available to authorized users.

The Economic Impact of Lower Protein Infant Formula for the Children of Overweight and Obese Mothers

The global prevalence of obesity is rising rapidly, highlighting the importance of understanding risk factors related to the condition. Childhood obesity, which has itself become increasingly prevalent, is an important predictor of adulthood obesity. Studies suggest that the protein content consumed in infanthood is an important predictor of weight gain in childhood, which may contribute to higher body mass index (BMI). For instance, there is evidence that a lower protein infant formula (lpIF) for infants of overweight or obese mothers can offer advantages over currently-used infant formulas with regard to preventing excessive weight gain. The current study used health economic modelling to predict the long-term clinical and economic outcomes in Mexico associated with lpIF compared to a currently-used formula. A discrete event simulation was constructed to extrapolate the outcomes of trials on the use of formula in infanthood to changes in lifetime BMI, the health outcomes due to the changes in BMI and the healthcare system costs, productivity and quality of life impact associated with these outcomes. The model predicts that individuals who receive lpIF in infancy go on to have lower BMI levels throughout their lives, are less likely to be obese or develop obesity-related disease, live longer, incur fewer health system costs and have improved productivity. Simulation-based economic modelling suggests that the benefits seen in the short term, with the use of lpIF over a currently-used formula, could translate into considerable health and economic benefits in the long term. Modelling over such long timeframes is inevitably subject to uncertainty. Further research should be undertaken to improve the certainty of the model.

Developing a tool to measure pharmacoeconomic outcomes of post-surgical pain management interventions

BACKGROUND

Financial pressures have limited the ability of providers to use medication that may improve clinical outcomes and patient satisfaction. New interventions are often fraught with resistance from individual cost centers. A value realization tool (VRT) is essential for separate cost centers to communicate and comprehend the overall financial and clinical implications of post-surgical pain management medication interventions (PSMI). The goal was to describe development of a VRT.

METHODS

An evaluation of common in-patient PSMI approaches, impacts, and costs was performed. A multidisciplinary task force guided development of the VRT to ensure appropriate representation and relevance to clinical practice. The main outcome was an Excel-based tool that communicates the overall cost/benefit of PSMI for the post-operative patient encounter.

RESULTS

The VRT aggregated input data on costs, clinical impact, and nursing burden of PSMI assessment and monitoring into two high-level outcome reports: Overall Cost Impact and Nurse & Patient Impact. Costs included PSMI specific medication, equipment, professional placement, labor, overall/opioid-related adverse events, re-admissions, and length of stay. Nursing impact included level of practice interference, job satisfaction, and patient care metrics. Patient impact included pain scores, opioid use, PACU time, and satisfaction. Reference data was provided for individual institutions that may not collect all variables included in the VRT.

CONCLUSIONS

The VRT is a valuable way for administrators to assess PSMI cost/benefits and for individual cost centers to see the overall value of individual interventions. The user-friendly, decision-support tool allows the end-user to use built-in referenced or personalized outcome data, increasing relevance to their institutions. This broad picture could facilitate communication across cost centers and evidence-based decisions for appropriate use and impacts of PSMI.

Overview of cost-effectiveness analysis and health state utilities in multiple myeloma and estimations of health state utilities from real-world macedonian data

Multiple myeloma (MM) is a hematological neoplastic plasma-cell disorder in the bone marrow. Nowadays, the survival of MM patients has improved by using novel therapies (e.g., bortezomib, thalidomide). Decision-analytic models (DAM) are known to be very useful in guiding clinical and health policy decisions by systematically evaluating expected outcomes of alternative treatments. When conducting DAM, it is recommended by National Institute for Clinical Excellence (NICE) as the preferred framework to use qualityadjusted life years as the main measure for health outcomes derived from generic preference-based instruments. The objective of our study was to identify and analyze the structural and methodological approaches of published studies that evaluated: (1) DAM for various treatment strategies in MM and (2) published health utility values (HUVs) after MM treatment. The searches were developed in the electronic literature database PubMed/MEDLINE. Additionally, we aimed to derive real-world data for HUVs from the current patients’ treatment landscape in the Hematology Clinical Center in Skopje. The survey collecting national HUVs was conducted during the period January-November 2016, using the generic preference-based instrument EQ-5D-5L, administered at one-time point in 20 MM patients. Only five studies reported on using a DAM. Seven studies were included in the extraction of already published HUVs. The calculated national health utility score of 0.723 was comparable to the published data in the previously identified studies reporting on similar MM treatment protocols. Several modeling approaches and HUVs, developed for different treatment strategies and target groups in MM were identified. Those data could be used to parameterize a DAM for MM treatment.

Dealing with Time in Health Economic Evaluation: Methodological Issues and Recommendations for Practice

Time is an important aspect of health economic evaluation, as the timing and duration of clinical events, healthcare interventions and their consequences all affect estimated costs and effects. These issues should be reflected in the design of health economic models. This article considers three important aspects of time in modelling: (1) which cohorts to simulate and how far into the future to extend the analysis; (2) the simulation of time, including the difference between discrete-time and continuous-time models, cycle lengths, and converting rates and probabilities; and (3) discounting future costs and effects to their present values. We provide a methodological overview of these issues and make recommendations to help inform both the conduct of cost-effectiveness analyses and the interpretation of their results. For choosing which cohorts to simulate and how many, we suggest analysts carefully assess potential reasons for variation in cost effectiveness between cohorts and the feasibility of subgroup-specific recommendations. For the simulation of time, we recommend using short cycles or continuous-time models to avoid biases and the need for half-cycle corrections, and provide advice on the correct conversion of transition probabilities in state transition models. Finally, for discounting, analysts should not only follow current guidance and report how discounting was conducted, especially in the case of differential discounting, but also seek to develop an understanding of its rationale. Our overall recommendations are that analysts explicitly state and justify their modelling choices regarding time and consider how alternative choices may impact on results.

Cost and effectiveness of omega-3 fatty acid supplementation in Chinese ICU patients receiving parenteral nutrition

Background and objectives

Clinical evidence supports the use of omega-3 polyunsaturated fatty acid (PUFA)-enriched lipid emulsions in place of standard lipid emulsions in parenteral nutrition (PN) for intensive care unit (ICU) patients, but uptake may be limited by higher costs. We compared clinical and economic outcomes for these two types of lipid emulsion in the Chinese ICU setting.

Methods

We developed a pharmacoeconomic discrete event simulation model, based on efficacy data from an international meta-analysis and patient characteristics, resource consumption, and unit costs from a Chinese institutional setting. Probabilistic sensitivity analyses were undertaken to assess the effects of uncertainty around input parameters. Model predictive validity was assessed by comparing results with data observed in a patient subset not used in the modeling.

Results

The model predicted that omega-3 PUFA-enriched emulsion (Omegaven^® 10% fish oil emulsion) would dominate standard lipid emulsions, with better clinical outcomes and lower overall health care costs (mean savings ~10,000 RMB), mainly as a result of faster recovery and shorter hospital stay (by ~6.5 days). The external validation process confirmed the reliability of the model predictions.

Conclusion

Omega-3 PUFA-enriched lipid emulsions improved clinical outcome and decreased overall costs in Chinese ICU patients requiring PN.

Economic evaluations with agent-based modelling: an introduction

Agent-based modelling (ABM) is a relatively new technique, which overcomes some of the limitations of other methods commonly used for economic evaluations. These limitations include linearity, homogeneity and stationarity. Agents in ABMs are autonomous entities, who interact with each other and with the environment. ABMs provide an inductive or 'bottom-up' approach, i.e. individual-level behaviours define system-level components. ABMs have a unique property to capture emergence phenomena that otherwise cannot be predicted by the combination of individual-level interactions. In this tutorial, we discuss the basic concepts and important features of ABMs. We present a case study of an application of a simple ABM to evaluate the cost effectiveness of screening of an infectious disease. We also provide our model, which was developed using an open-source software program, NetLogo. We discuss software, resources, challenges and future research opportunities of ABMs for economic evaluations.

Health economic evaluation of plasma oxysterol screening in the diagnosis of Niemann-Pick Type C disease among intellectually disabled using discrete event simulation

BACKGROUND

Recently a less invasive method of screening and diagnosing Niemann-Pick C (NP-C) disease has emerged. This approach involves the use of a metabolic screening test (oxysterol assay) instead of the current practice of clinical assessment of patients suspected of NP-C (review of medical history, family history and clinical examination for the signs and symptoms). Our objective is to compare costs and outcomes of plasma oxysterol screening versus current practice in diagnosis of NP-C disease among intellectually disabled (ID) patients using decision-analytic methods.

METHODS

A discrete event simulation model was conducted to follow ID patients through the diagnosis and treatment of NP-C, forecast the costs and effectiveness for a cohort of ID patients and compare the outcomes and costs in two different arms of the model: plasma oxysterol screening and routine diagnosis procedure (anno 2013) over 5 years of follow up. Data from published sources and clinical trials were used in simulation model. Unit costs and quality-adjusted life-years (QALYs) were discounted at a 3% annual rate in the base case analysis. Deterministic and probabilistic sensitivity analyses were conducted.

RESULTS

The outcomes of the base case model showed that using plasma oxysterol screening for diagnosis of NP-C disease among ID patients is a dominant strategy. It would result in lower total cost and would slightly improve patients' quality of life. The average amount of cost saving was $3642 CAD and the incremental QALYs per each individual ID patient in oxysterol screening arm versus current practice of diagnosis NP-C was 0.0022 QALYs. Results of sensitivity analysis demonstrated robustness of the outcomes over the wide range of changes in model inputs.

CONCLUSION

Whilst acknowledging the limitations of this study, we conclude that screening ID children and adolescents with oxysterol tests compared to current practice for the diagnosis of NP-C is a dominant strategy with clinical and economic benefits. The less costly, more sensitive and specific oxysterol test has potential to save costs to the healthcare system while improving patients' quality of life and may be considered as a routine tool in the NP-C diagnosis armamentarium for ID. Further research is needed to elucidate its effectiveness in patients presenting characteristics other than ID in childhood and adolescence.

Applying dynamic simulation modeling methods in health care delivery research-the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force

Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.

Long-acting olanzapine versus long-acting risperidone for schizophrenia in Spain - a cost-effectiveness comparison

BACKGROUND

In schizophrenia, medication adherence is critical to achieve better patient outcomes and to avoid relapses, which are responsible for a significant proportion of total healthcare costs for this chronic illness. The aim of this study was to assess the cost-effectiveness of olanzapine long-acting injection (OLAI) compared with risperidone long-acting injection (RLAI) in patients with schizophrenia in Spain.

METHODS

A discrete event simulation (DES) model was developed from a Spanish healthcare system perspective to estimate clinical and economic outcomes for patients with schizophrenia over a five-year period. Patients who had earlier responded to oral medication and have a history of relapse due to adherence problems were considered. Identical model populations were treated with either OLAI or RLAI. In the absence of a head-to-head clinical trial, discontinuation and relapse rates were obtained from open-label studies. The model accounted for age, gender, risks of relapse and discontinuation, relapse management, hospitalization, treatment switching and adverse events. Direct medical costs for the year 2011 and outcomes including relapse avoided, life years (LYs), and quality-adjusted life years (QALYs) were discounted at a rate of 3%.

RESULTS

When comparing RLAI and OLAI, the model predicts that OLAI would decrease 5-year costs by €2,940 (Standard Deviation between replications 300.83), and result in a QALY and LY gains of 0.07 (SD 0.019) and 0.04 (SD 0.025), respectively. Patients on OLAI had fewer relapses compared to RLAI (1.392 [SD 0.035] vs. 1.815 [SD 0.035]) and fewer discontinuations (1.222 [SD 0.031] vs. 1.710 [SD 0.039]). Sensitivity analysis indicated that the study was robust and conclusions were largely unaffected by changes in a wide range of parameters.

CONCLUSIONS

The present evaluation results in OLAI being dominant over RLAI, meaning that OLAI represents a more effective and less costly alternative compared to RLAI in the treatment of patients with schizophrenia in the Spanish setting.

Modeling rheumatoid arthritis using different techniques - a review of model construction and results

BACKGROUND

Rheumatoid arthritis (RA) is a chronic, inflammatory disease with severe effects on the functional ability of patients. Due to the prevalence of 0.5 to 1.0 percent in western countries, new treatment options are a major concern for decision makers with regard to their budget impact. In this context, cost-effectiveness analyses are a helpful tool to evaluate new treatment options for reimbursement schemes.

OBJECTIVES

To analyze and compare decision analytic modeling techniques and to explore their use in RA with regard to their advantages and shortcomings.

METHODS

A systematic literature review was conducted in PubMED and 58 studies reporting health economics decision models were analyzed with regard to the modeling technique used.

RESULTS

From the 58 reviewed publications, we found 13 reporting decision tree-analysis, 25 (cohort) Markov models, 13 publications on individual sampling methods (ISM) and seven discrete event simulations (DES). Thereby 26 studies were identified as presenting independently developed models and 32 models as adoptions. The modeling techniques used were found to differ in their complexity and in the number of treatment options compared. Methodological features are presented in the article and a comprehensive overview of the cost-effectiveness estimates is given in Additional files 1 and 2.

DISCUSSION

When compared to the other modeling techniques, ISM and DES have advantages in the coverage of patient heterogeneity and, additionally, DES is capable to model more complex treatment sequences and competing risks in RA-patients. Nevertheless, the availability of sufficient data is necessary to avoid assumptions in ISM and DES exercises, thereby enabling biased results. Due to the different settings, time frames and interventions in the reviewed publications, no direct comparison of modeling techniques was applicable. The results from other indications suggest that incremental cost-effective ratios (ICERs) do not differ significantly between Markov and DES models, but DES is able to report more outcome parameters.

CONCLUSIONS

Given a sufficient data supply, DES is the modeling technique of choice when modeling cost-effectiveness in RA. Otherwise transparency on the data inputs is crucial for valid results and to inform decision makers about possible biases. With regard to ICERs, Markov models might provide similar estimates as more advanced modeling techniques.