Pharmacoeconomic analyses using discrete event simulation

Capacity Constraints and Cost-Effectiveness: A Discrete Event Simulation for Drug-Eluting Stents

Background. Waiting times for access to care, for example, for diagnostic imaging or surgery, are a highly relevant issue in health care. Waiting or deferred treatment caused by limited resource capacities can affect treatment success, quality of life, and costs. However, when treatment alternatives are compared in economic models, often unrestricted availability of resources is assumed, and dynamic changes in waiting lines remain unconsidered. The objective of this study was to evaluate the impact of potential real-world capacity restrictions and implied waiting lines on cost-effectiveness results and additional model outcomes. Methods. A case study of drug-eluting and bare-metal stent treatment illustrates the effect of hypothetical capacity limitations of daily stenting procedures. Therefore, a decision-analytic model which allows for explicitly defined resource capacities and dynamic waiting lines was built using discrete event simulation. Cost-effectiveness, utilization, waiting time, and budgetary impact of alternative treatment scenarios are analyzed under the assumption of limited and unlimited resource capacities. Results. The compared treatment allocation scenarios in the case study demonstrate that the additional cost for waiting increases the average treatment cost per patient. The different scenarios have different impacts on waiting lines because of the number of repeated interventions. Additionally, this effect leads to changes in cost-effectiveness results for the hypothetical capacity limit. Explicitly modeled capacities allow for further analysis of capacity utilization, waiting lines, and budgetary impact. Conclusion. Our model shows that neglected limited capacities can cause wrong cost-effectiveness results. Therefore, capacities should be explicitly included in decision-analytic models if there is evidence of scarcity.

Economic evaluation of atorvastatin for prevention of recurrent stroke based on the SPARCL trial

OBJECTIVES

This study evaluated the economic implications of results obtained by the Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) trial.

METHODS

To enable long-term projection of the trial results, a discrete event simulation of the course of clinical care after a recent stroke or transient ischemic attack (TIA) was developed. It generates pairs of identical patients; both receive usual care, one receives atorvastatin in addition. Their clinical course is simulated based on their risk of stroke, cardiovascular events, and case fatality rates taken from SPARCL, life expectancy from Saskatchewan Health data, and utility weights from literature. Costs, from a US health-care payer perspective in 2005 US dollars, were estimated for a within-trial 5-year period; survival and quality-adjusted life-years (QALYs) were extrapolated over a patient's lifetime; all discounted at 3%/year.

RESULTS

The prevention of stroke, coronary, and other cardiovascular events expected with atorvastatin translates to mean gains of 0.155 life-years gained and 0.172 QALYs per patient over their lifetime. Reducing associated medical costs ($8405 vs. $11,237) but increasing drug costs ($13,984 vs. $8752) results in net $2400/patient, or $13,916/QALY gained. Probabilistic sensitivity analysis indicates no simulations yield ratios above $50,000/QALY.

CONCLUSION

Prescribing atorvastatin for patients with prior stroke or TIA is expected to provide health benefits at an acceptable cost in the United States.

Cost-effectiveness of ziconotide in intrathecal pain management for severe chronic pain patients in the UK

OBJECTIVE

To examine the cost-effectiveness of using intrathecal ziconotide in the treatment of severe chronic pain compared to best supportive care for patients with intractable chronic pain in the United Kingdom.

METHODS

Using a simulation model, the analysis evaluated the cost and health economic consequences of using ziconotide as a treatment for severe chronic pain. The modelled population and clinical data were based on a randomised controlled trial in which the main outcome was reduction in pain as measured by the visual analogue scale of pain intensity (VASPI). Resource use data were elicited using a modified Delphi panel and costed using published sources. Utility values were derived from a separate research study. The main outcome measure was the cost per quality-adjusted life-year (QALY). Extensive scenario analysis was conducted to evaluate parameter uncertainty.

RESULTS

Overall, findings were robust to most assumptions. The cost-effectiveness of ziconotide compared to best supportive care (BSC) was pound 27,443 per QALY (95% CI pound 18,304-38,504). Scenarios were investigated in which discount rates, the time horizon, the threshold for qualifying as a responder, pump-related assumptions, utilities, ziconotide drug dose, and the patient discontinuation rate with ziconotide were varied. The most sensitive parameter was the dosage of ziconotide: using the lower and upper bounds of the average ziconotide dosage observed in the long-term open-label study changed the incremental cost-effectiveness ratio (ICER) to pound 15,500 [pound 8206-25,405] and pound 44,700 [pound 30,541-62, 670].

CONCLUSIONS

Ziconotide may offer an economically feasible alternative solution for patients for whom current treatment is inappropriate or ineffective. The main study limitation is that some model inputs, mainly related to resource use, are based on assumptions or expert interviews.

Interpreting the results of cost-effectiveness studies

In developed nations, health care spending is an increasingly important economic and political issue. The discipline of cost-effectiveness (CE) analysis has developed over several decades as a tool for objectively assessing the value of new medical strategies, by simultaneously examining incremental health benefits in light of incremental costs. The underlying goal of CE research is to allow clinicians and policymakers to make more rational decisions regarding clinical care and resource allocation. This review will provide the reader with an understanding of the theoretical underpinnings of CE analysis, the types of analyses commonly performed and reported in the medical literature, some important strengths and weaknesses of different analytical approaches, and key principles in the interpretation of CE results. Key principles reviewed include the impact of analytic perspective, the importance of proper incremental comparisons, the effect of time horizon, and methods for exploring and describing uncertainty. Illustrative examples from the cardiology literature are discussed.

Treating relapsing multiple sclerosis with subcutaneous versus intramuscular interferon-beta-1a: modelling the clinical and economic implications

The EVIDENCE trial concluded that administering high-dose/high-frequency subcutaneous (SC) interferon-beta-1a (IFNb1a) was more effective in preventing relapses among patients with relapsing multiple sclerosis (MS) than low-dose weekly intramuscular (IM) IFNb1a after 64 weeks. This analysis utilized discrete-event simulation (DES) to model the potential longer-term clinical and economic implications of this trial. A DES predicting the course of relapsing MS and incorporating the effect of IFNb1a therapy was developed. The model began by randomly reading in actual patient data from the trial to create 1000 patients. Each simulated patient was replicated - one was assigned to receive SC IFNb1a three times a week and the other to receive IM IFNb1a once a week. During the simulation, patients may (i) experience relapses, with associated short- and long-term impacts on costs and disability; (ii) develop new T2 lesions detected by a magnetic resonance imaging scan; (iii) discontinue treatment because of adverse events or lack of response; (iv) advance to secondary progressive MS; or (v) die. Model inputs were mainly obtained from the EVIDENCE trial, but were taken from published literature if they could not be obtained from the trial. Direct medical costs ($US, year 2006 values) to the US payers were primarily obtained by updating a published cost analysis. Costs and benefits were discounted at 3% per annum. Extensive sensitivity analyses were conducted to test the robustness of the model results. Based on 100 replications of 1000 patient pairs over 4 years, SC IFNb1a was predicted to enable more patients to avoid relapse (216 vs 147). Total mean costs per patient (discounted) were $US79 890 with SC IFNb1a versus $US74 485 with IM administration, a net increase of $US5405 per patient. However, SC IFNb1a was estimated to prevent 0.50 relapses and save 23 relapse-free days per patient, yielding incremental cost-effectiveness ratios of $US10 755 per relapse prevented and $US232 per relapse-free day gained. Sensitivity analyses revealed that the result was most sensitive to the treatment efficacy, model time horizon and cost of IFNb1a treatment. Based on the results observed in the EVIDENCE trial, the model predicted that SC IFNb1a would yield greater health benefits over 4 years than IM IFNb1a, at a cost that would seem to be a reasonable trade-off.

Comparing three software tools for implementing markov models for health economic evaluations

BACKGROUND

Various software packages are commonly used for the implementation and calculation of decision-analytic models for health economic evaluations. However, comparison of these programs with regard to ease of implementing a model is lacking.

OBJECTIVES

(i) to compare the assets and drawbacks of three commonly used software packages for Markov models with regard to ease of implementation; and (ii) to investigate how a technical model validation can be conducted by comparing the results of the three implementations.

METHODS

A Markov model on chronic obstructive pulmonary disease was implemented in TreeAge, Microsoft Excel and Arena with the same assumptions on model structure, transition probabilities and costs. A hypothetical smoking cessation programme for patients in stage 1 was evaluated against usual care. The packages were compared with respect to time and effort for implementation, run-time, features for the presentation of results, and flexibility. Agreement between the packages on average costs and life-years gained and on the incremental cost-effectiveness ratio was considered for technical validation in the form of expected values (between TreeAge and Excel only) and Monte Carlo simulations.

RESULTS

Ease of implementation was best in TreeAge, whereas Arena offered the highest flexibility. Deterministic results were in agreement between TreeAge and Excel, as were simulated values between all three packages.

CONCLUSIONS

Excel offers an intuitive spreadsheet interface, but the acquisition of and the training in TreeAge or Arena is worthwhile for more complex models. Double implementation is a practicable validation technique that should be conducted to ensure correct model implementation.

Comparison of Markov model and discrete-event simulation techniques for HIV

BACKGROUND

Markov models have been the standard framework for predicting long-term clinical and economic outcomes using the surrogate marker endpoints from clinical trials. However, they are complex, have intensive data requirements and are often difficult for decision makers to understand. Recent developments in modelling software have made it possible to use discrete-event simulation (DES) to model outcomes in HIV. Using published results from 48-week trial data as model inputs, Markov model and DES modelling approaches were compared in terms of clinical outcomes at 5 years and lifetime cost-effectiveness estimates.

METHODS

A randomly selected cohort of 100 antiretroviral-naive patients with a mean baseline CD4+ T-cell count of 175 cells/mm3 treated with lopinavir/ritonavir was selected from Abbott study M97-720. Parameter estimates from this cohort were used to populate both a Markov and a DES model, and the long-term estimates for these cohorts were compared. The models were then modified using the relative risk of undetectable viral load as reported for atazanavir and lopinavir/ritonavir in the published BMS 008 study. This allowed us to compare the mean cost effectiveness of the models. The clinical outcomes included mean change in CD4+ T-cell count, and proportion of subjects with plasma HIV-1 RNA (viral load [VL]) <50 copies/mL, VL 50-400 copies/mL and VL >400 copies/mL. US wholesale acquisition costs (year 2007 values) were used in the mean cost-effectiveness analysis, and the cost and QALY data were discounted at 3%.

RESULTS

The results show a slight predictive advantage of the DES model for clinical outcomes. The DES model could capture direct input of CD4+ T-cell count, and proportion of subjects with plasma HIV-1 RNA VL <50 copies/mL, VL 50-400 copies/mL and VL >400 copies/mL over a 48-week period, which the Markov model could not. The DES and Markov model estimates were similar to the actual clinical trial estimates for 1-year clinical results; however, the DES model predicted more detailed outcomes and had slightly better long-term (5-year) predictive validity than the Markov model. Similar cost estimates were derived from the Markov model and the DES. Both models predict cost savings at 5 and 10 years, and over a lifetime for the lopinavir/ritonavir treatment regimen as compared with an atazanavir regimen.

CONCLUSION

The DES model predicts the course of a disease naturally, with few restrictions. This may give the model superior face validity with decision makers. Furthermore, this model automatically provides a probabilistic sensitivity analysis, which is cumbersome to perform with a Markov model. DES models allow inclusion of more variables without aggregation, which may improve model precision. The capacity of DES for additional data capture helps explain why this model consistently predicts better survival and thus greater savings than the Markov model. The DES model is better than the Markov model in isolating long-term implications of small but important differences in crucial input data.

Discrete-event simulation applied to analysis of waiting lists. Evaluation of a prioritization system for cataract surgery

OBJECTIVES

To outline the methods used to build a discrete-event simulation model for use in decision-making in the context of waiting list management strategies for cataract surgery by comparing a waiting list prioritization system with the routinely used first-in, first-out (FIFO) discipline.

METHODS

The setting was the Spanish health system. The model reproduced the process of cataract, from incidence of need of surgery (meeting indication criteria), through demand, inclusion on a waiting list, and surgery. "Nonexpressed Need" represented the population that, even with need, would not be included on a waiting list. Parameters were estimated from administrative data and research databases. The impact of introducing a prioritization system on the waiting list compared with the FIFO system was assessed. For all patients entering the waiting list, the main outcome variable was waiting time weighted by priority score. A sensitivity analysis with different scenarios of mean waiting time was used to compare the two alternatives.

RESULTS

The prioritization system shortened waiting time (weighted by priority score) by 1.55 months (95% CI: 1.47 to 1.62) compared with the FIFO system. This difference was statistically significant for all scenarios (which were defined from a waiting time of 4 months to 24 months under the FIFO system). A tendency to greater time savings in scenarios with longer waiting times was observed.

CONCLUSIONS

Discrete-event simulation is useful in decision-making when assessing health services. Introducing a waiting list prioritization system produced greater benefit than allocating surgery by waiting time only. Use of the simulation model would allow the impact of proposed policies to reduce waiting lists or assign resources more efficiently to be tested.

[Discrete-event simulation models in the economic evaluation of health technologies and health products]

The use of mathematical models to assess therapeutic alternatives is increasing in the economic evaluation of health technologies and services and these models are becoming an increasingly important aid to decision making in health care. Until now, 2 types of model have been used, depending to some extent on the disease to be studied: decision trees have been used for acute diseases and Markov models in chronic or recurrent diseases. However, both models present major limitations when addressing complex processes or diseases. Consequently, interest in, and the use of, discrete-event simulation is growing. The present article aims to describe the main characteristics of discrete-event simulation, the state of the art in this field, and the advantages of these models with respect to other kinds of models in health economics, especially in the evaluation of health technologies and product assessment.

Decision analytic models for Alzheimer's disease: state of the art and future directions

Decision analytic policy models for Alzheimer's disease (AD) enable researchers and policy makers to investigate questions about the costs and benefits of a wide range of existing and potential screening, testing, and treatment strategies. Such models permit analysts to compare existing alternatives, explore hypothetical scenarios, and test the strength of underlying assumptions in an explicit, quantitative, and systematic way. Decision analytic models can best be viewed as complementing clinical trials both by filling knowledge gaps not readily addressed by empirical research and by extrapolating beyond the surrogate markers recorded in a trial. We identified and critiqued 13 distinct AD decision analytic policy models published since 1997. Although existing models provide useful insights, they also have a variety of limitations. (1) They generally characterize disease progression in terms of cognitive function and do not account for other distinguishing features, such as behavioral symptoms, functional performance, and the emotional well-being of AD patients and caregivers. (2) Many describe disease progression in terms of a limited number of discrete states, thus constraining the level of detail that can be used to characterize both changes in patient status and the relationships between disease progression and other factors, such as residential status, that influence outcomes of interest. (3) They have focused almost exclusively on evaluating drug treatments, thus neglecting other disease management strategies and combinations of pharmacologic and nonpharmacologic interventions. Future AD models should facilitate more realistic and compelling evaluations of various interventions to address the disease. An improved model will allow decision makers to better characterize the disease, to better assess the costs and benefits of a wide range of potential interventions, and to better evaluate the incremental costs and benefits of specific interventions used in conjunction with other disease management strategies.

Modelling approaches: the case of schizophrenia

Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between co-variates and variability (first-order uncertainty). We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data.

CoolSim: using industrial modeling techniques to examine the impact of selective head cooling in a model of perinatal regionalization

OBJECTIVE

A selective head-cooling device for the treatment of moderate to severe hypoxic-ischemic encephalopathy has been approved by the Food and Drug Administration for use in the United States. To reflect the complexity of health care delivery at the systems level, we used the industrial modeling technique of discrete event simulation to analyze the impact of various deployment strategies for selective head cooling and its partner technology, amplitude-integrated electroencephalography.

METHODS

We modeled the course through the perinatal system of all births in Massachusetts over a 1-year period. Cohort and care characteristics were drawn from existing databases. Results of a recently published trial were used to estimate the effects of selective head cooling. One thousand cohort replications were conducted to assess uncertainty. Several policy alternatives were examined, including no use of selective head cooling and scenarios that altered the number and placement of selective head-cooling and amplitude-integrated electroencephalography units throughout the state. Patient-level outcome and cost data were assessed.

RESULTS

For all scenarios tested, the use of amplitude-integrated electroencephalography/selective head cooling resulted in better outcomes at lower cost. However, substantial differences in transfer rates, failure-to-cool rates, and total costs were seen across scenarios. Optimal decision-making regarding the number and placement of devices led to a 16% improvement in cost savings and a 10-fold decrease in failure-to-cool rates, compared with other deployment scenarios. These results were insensitive to significant changes in model inputs.

CONCLUSIONS

On the basis of currently available data, the package of amplitude-integrated electroencephalography and selective head cooling seems to be an economically desirable intervention. Quantifiable techniques to assess system-wide technology performance provide a powerful approach to informing decisions regarding the structure and function of health care systems.

Methods for integrating medication compliance and persistence in pharmacoeconomic evaluations

OBJECTIVES

Suboptimal compliance and failure to persist with drug treatments are important determinants of therapeutic nonresponse and are of potential economic significance. The present article aims to describe the methodologies that may be appropriate for integrating noncompliance and nonpersistence in economic evaluations.

METHODS

MEDLINE and NHS-EED were searched for economic evaluations published in the period between 1997 and 2005. Articles were included if they explored the dependence of cost-effectiveness results on varying levels of some form of compliance-related measure. The different methodologies used were reviewed and articles were appraised critically. Alternative methodological approaches are proposed, illustrated by an example of the impact of different persistence rates on a treatment's cost-effectiveness.

RESULTS

Ten articles were selected for inclusion. These were generally scant on detail relating to how compliance/persistence was assessed and what the impact was on health outcomes. The methods used included Markov models and decision analyses. Markov models allow for persistence to be included directly in the analysis, as patients transit during each cycle. Net-benefit regression models are well suited for analyzing prospective and retrospective studies where patient-level data are available, whereas discrete event simulations have the potential to offer more flexibility.

CONCLUSIONS

Compliance and/or persistence are not included routinely in pharmacoeconomic analyses, despite their potential impact. Where compliance and/or persistence are included, a lack of methodological rigor and consistency in definitions often limits the usefulness of the analyses. The analytical techniques discussed in this article should serve as a basis for developing guidelines on appropriate methodology.

Modeling the economic and health consequences of managing chronic osteoarthritis pain with opioids in Germany: comparison of extended-release oxycodone and OROS hydromorphone

OBJECTIVE

The Osmotic controlled-Release Oral delivery System (OROS) hydromorphone ensures continuous release of hydromorphone over 24 hours. It is anticipated that this will facilitate optimal pain relief, improve quality of sleep and compliance. This simulation compared managing chronic osteoarthritis pain with once-daily OROS hydromorphone with an equianalgesic dose of extended-release (ER) oxycodone administered two or three times a day.

METHODS

This discrete event simulation follows patients for a year after initiating opioid treatment. Pairs of identical patients are created; one receives OROS hydromorphone the other ER oxycodone; undergo dose adjustments and after titration can be dissatisfied or satisfied, suffer adverse events, pain recurrence, or discontinue the opioid. Each is assigned an initial sleep problems score, and an improved score from a treatment dependent distribution at the end of titration; these are translated to a utility value. Utilities are assigned pre-treatment, updated until the patient reaches the optimal dose or is non-compliant or dissatisfied. The OROS hydromorphone and ER oxycodone doses are converted to equianalgesic morphine doses using the following ratios: hydromorphone to morphine ratio; 1:5, oxycodone to morphine ratio; 1:2. Sensitivity analyses explored uncertainty in the conversion ratios and other key parameters. Direct medical costs are in 2005 euros.

RESULTS

Over 1 year on a mean daily morphine-equivalent dose of 90 mg, 14% were estimated to be dissatisfied with each opioid. OROS hydromorphone was predicted to yield 0.017 additional quality-adjusted life years (QALYs)/patient for a small additional annual cost (E141/patient), yielding an incremental cost-effectiveness ratio (ICER) of E8343/QALY gained. Changing the assumed conversion ratio for oxycodone:morphine to 1:1.5 led to lower net costs of E68 per patient, E3979/QALY, and for hydromorphone to 1:7.5 to savings.

CONCLUSION

Based on these analyses, OROS hydromorphone is expected to yield health benefits at reasonable cost in Germany.

Methods for determining cost-benefit ratios for pharmaceuticals in Germany

The aim of this methodological paper is to summarize evidence on how to implement cost-benefit assessment according to the new German legislative framework (Competition Enhancement Act). Given the complexity of existing health policy frameworks within industrialised countries in adapting health economics in their respective regulatory scheme, no clear international scientific consensus on which health economic methods should be chosen for assessment can be determined. Nevertheless, a broad consensus on the internal properties of methods itself can be found. Based on these common international standards in methodology, this work provides a minimum catalogue of methods and criteria that meet legal and local German requirements with regard to specific factors of its health care system. Aside from categorising clearly defined standards (e.g., study forms, cost and benefit categories) the suggested catalogue specifies some intensively debated areas in Germany (e.g., the QALY, modelling, the perspective used in the assessment). After the proposition of certain methods the paper leads to a first recommendation of a detailed assessment-process itself specific for the German way in implementing cost-benefit ratios within regulatory decision making in Germany.

Invasive meningococcal disease epidemiology and control measures: a framework for evaluation

BACKGROUND

Meningococcal disease can have devastating consequences. As new vaccines emerge, it is necessary to assess their impact on public health. In the absence of long-term real world data, modeling the effects of different vaccination strategies is required. Discrete event simulation provides a flexible platform with which to conduct such evaluations.

METHODS

A discrete event simulation of the epidemiology of invasive meningococcal disease was developed to quantify the potential impact of implementing routine vaccination of adolescents in the United States with a quadrivalent conjugate vaccine protecting against serogroups A, C, Y, and W-135. The impact of vaccination is assessed including both the direct effects on individuals vaccinated and the indirect effects resulting from herd immunity. The simulation integrates a variety of epidemiologic and demographic data, with core information on the incidence of invasive meningococcal disease and outbreak frequency derived from data available through the Centers for Disease Control and Prevention. Simulation of the potential indirect benefits of vaccination resulting from herd immunity draw on data from the United Kingdom, where routine vaccination with a conjugate vaccine has been in place for a number of years. Cases of disease are modeled along with their health consequences, as are the occurrence of disease outbreaks.

RESULTS

When run without a strategy of routine immunization, the simulation accurately predicts the age-specific incidence of invasive meningococcal disease and the site-specific frequency of outbreaks in the Unite States. 2,807 cases are predicted annually, resulting in over 14,000 potential life years lost due to invasive disease. In base case analyses of routine vaccination, life years lost due to infection are reduced by over 45% (to 7,600) when routinely vaccinating adolescents 12 years of age at 70% coverage. Sensitivity analyses indicate that herd immunity plays an important role when this population is targeted for vaccination. While 1,100 cases are avoided annually when herd immunity effects are included, in the absence of any herd immunity, the number of cases avoided with routine vaccination falls to 380 annually. The duration of vaccine protection also strongly influences results.

CONCLUSION

In the absence of appropriate real world data on outcomes associated with large-scale vaccination programs, decisions on optimal immunization strategies can be aided by discrete events simulations such as the one described here. Given the importance of herd immunity on outcomes associated with routine vaccination, published estimates of the economic efficiency of routine vaccination with a quadrivalent conjugate vaccine in the United States may have considerably underestimated the benefits associated with a policy of routine immunization of adolescents.

Emerging methods in economic modeling of imaging costs and outcomes a short report on discrete event simulation

RATIONALE AND OBJECTIVES

This short report provides a non-technical overview of one emerging modeling technique, discrete event simulation (DES).

METHODS

A selective review of the literature that has applied DES methods to evaluate imaging technologies.

RESULTS

Mathematical models to evaluate the likely costs and outcomes of health technologies have become increasingly accepted. Increasing experience has also brought a mounting awareness of the limitations of conventional modeling techniques such as decision trees and Markov processes. Patient-level simulation, including DES, may provide a more flexible approach to modeling for economic evaluation of health technologies.

CONCLUSIONS

The strengths of DES suggest that it may have an increasingly important role in the future modeling of annual screening programs, diagnosis, and treatment of chronic recurrent disease and modeling the utilization of imaging equipment.

NICE cost-effectiveness appraisal of cholinesterase inhibitors: was the right question posed? Were the best tools used?

The National Institute for Health and Clinical Excellence (NICE) recently issued updated guidance on the use of cholinesterase inhibitors in the treatment of Alzheimer's disease. NICE initially recommended that cholinesterase inhibitors no longer be used, but final guidance restricted treatment to patients with disease of a moderately severe stage. This decision was based largely on results from a heavily criticised economic evaluation that used an adaptation of the Assessment of Health Economics in Alzheimer's Disease (AHEAD) model. As the developers of the AHEAD model, we examined the appropriateness of NICE's economic analyses and presentation of results. We attempted to replicate NICE's results by modifying the original AHEAD model. Sensitivity analyses were then run using the modified AHEAD model to evaluate the extent of uncertainty in predictions. The AHEAD(NICE) analyses resulted in an incremental cost-effectiveness ratio for galantamine of 82,000 pound per QALY gained (year 2003 values) from the perspective of the UK NHS and Personal Social Services. This was later revised to 46,000 pound per QALY, compared with < 9000 pound per discounted QALY gained (year 2001 values) in the original AHEAD model. Using our modified AHEAD with effectiveness estimates matching those of AHEAD(NICE), we show that NICE's choice and presentation of sensitivity analyses obscured the instability of their estimates. In the final NICE evaluation, the recommendation to delay treatment with cholinesterase inhibitors until patients have moderately severe disease was based on critical assumptions in the economic analyses that had little evidence to support them. The case of NICE's guidance on cholinesterase inhibitors highlights the importance of transparent and valid economic evaluations and the dangers of using inappropriate modelling technologies, basing analyses on a limited subset of the available data, and insufficiently reflecting the uncertainty in estimates that are intended to inform decision makers.

Can discrete event simulation be of use in modelling major depression?

BACKGROUND

Depression is among the major contributors to worldwide disease burden and adequate modelling requires a framework designed to depict real world disease progression as well as its economic implications as closely as possible.

OBJECTIVES

In light of the specific characteristics associated with depression (multiple episodes at varying intervals, impact of disease history on course of illness, sociodemographic factors), our aim was to clarify to what extent "Discrete Event Simulation" (DES) models provide methodological benefits in depicting disease evolution.

METHODS

We conducted a comprehensive review of published Markov models in depression and identified potential limits to their methodology. A model based on DES principles was developed to investigate the benefits and drawbacks of this simulation method compared with Markov modelling techniques.

RESULTS

The major drawback to Markov models is that they may not be suitable to tracking patients' disease history properly, unless the analyst defines multiple health states, which may lead to intractable situations. They are also too rigid to take into consideration multiple patient-specific sociodemographic characteristics in a single model. To do so would also require defining multiple health states which would render the analysis entirely too complex. We show that DES resolve these weaknesses and that its flexibility allow patients with differing attributes to move from one event to another in sequential order while simultaneously taking into account important risk factors such as age, gender, disease history and patients attitude towards treatment, together with any disease-related events (adverse events, suicide attempt etc.).

CONCLUSION

DES modelling appears to be an accurate, flexible and comprehensive means of depicting disease progression compared with conventional simulation methodologies. Its use in analysing recurrent and chronic diseases appears particularly useful compared with Markov processes.

A review of health care models for coronary heart disease interventions

This article reviews models for the treatment of coronary heart disease (CHD). Whereas most of the models described were developed to assess the cost effectiveness of different treatment strategies, other models have also been used to extrapolate clinical trials, for capacity and resource planning, or to predict the future population with heart disease. In this paper we investigate the use of modelling techniques in relation to different types of health intervention, and we discuss the assumptions and limitations of these approaches. Many of the models reviewed in this paper use decision tree models for acute or short term interventions, and Markov or state transition models for chronic or long term interventions. Discrete event simulation has, however, been used for more complex whole system models, and for modelling resource-constrained interventions and operational planning. Nearly all of the studies in our review used cohort-based models rather than population based models, and therefore few models could estimate the likely total costs and benefits for a population group. Most studies used de novo purpose built models consisting of only a small number of health states. Models of the whole disease system were less common. The model descriptions were often incomplete. We recommend that the reporting of model structure, assumptions and input parameters is more explicit, to reduce the risk of biased reporting and ensure greater confidence in the model results.

Budgetary impact of treating acute bipolar mania in hospitalized patients with quetiapine: an economic analysis of clinical trials

OBJECTIVE

To present a tool that allows estimation of the budget impact of treatments for acute mania in bipolar I disorder from a US healthcare payer perspective.

METHODS

Using discrete event simulation, the course of individuals is simulated beginning with hospitalization. Discharge depends on symptom level measured by the Young Mania Rating Scale (YMRS). The treatment effect is determined using time-dependent regression equations derived from trial data, and decision rules obtained from clinical experts. Outcomes include: time to response and symptom resolution; proportion of subjects reaching each outcome; number of adverse events. Costs were obtained from hospital discharge databases, the National Medicare Physician Fee Schedule and RedBook. Different scenarios are examined, each describing the proportion of subjects on the various treatments (lithium, divalproex sodium, olanzapine, risperidone, and quetiapine--monotherapy and in combination with lithium). Analyses are intention-to-treat over 100 days, corresponding to follow-up in mania trials. Despite its flexibility and structural adaptability, the model has some important limitations related to the characteristics of the clinical trials. These include focus on inpatient management of acute mania, use of the YMRS as the model driver, polypharmacy restricted to two-drug regimens, no explicit consideration of titration and dose changes, and relatively short time horizon.

RESULTS

Scenarios with a greater proportion of quetiapine users (5% vs. 40% and 100%) result in a smaller impact on the healthcare budget (6912, 6277, and 5525 dollars per patient, respectively) and improvements in patient outcomes (e.g., 43%, 47%, and 54% responding at day 21; 74%, 77%, and 80% remitting by day 84). Sensitivity analyses showed that the budget impact is influenced by drug prices, discharge criteria and side-effect management.

CONCLUSION

Results suggest that increased use of quetiapine for bipolar mania in the US is economically justified and improves health outcomes. In addition, this model illustrates that discrete event simulation is a useful and versatile tool for budget impact analyses.

Economic implications of growth hormone use in patients with short bowel syndrome

OBJECTIVE

Short bowel syndrome is a rare, life-threatening condition that can result in nutritional malabsorption. Parenteral nutrition provides life-saving support but can lead to complications and affect quality of life. Recombinant human growth hormone, somatropin (rDNA origin), has been shown to significantly reduce dependence on nutritional support (p < 0.05). This study evaluates the economic impact of somatropin use in the management of short bowel syndrome.

METHODS

A discrete event simulation (DES) model was developed to estimate the benefits and costs associated with somatropin use. Risks of treatment complications and of disease-related events were modeled in identical patient pairs--one receiving parenteral nutrition alone, the other receiving 4 weeks of somatropin--for 2 years following initiation of treatment. Life expectancy was assumed equivalent. Risk functions were estimated from the literature and one randomized clinical trial. Total and component costs associated with each strategy were determined. The distribution of patients reducing parenteral nutrition need and the final parenteral nutrition frequency were also estimated. Sensitivity analyses were completed for key inputs. Direct medical costs are reported in US 2004 dollars.

RESULTS

The model predicted that 96.0% of patients receiving somatropin reduce or eliminate parenteral nutrition within 6 weeks: average use was reduced by 2.8 days and one-third weaned completely. Based on 1.9 L of parenteral nutrition per day, estimated costs were 118,098 dollars in year one and 132,935 dollars in year two. With somatropin, costs dropped to 84,309 dollars in year one--despite the 17,459 dollars cost of somatropin treatment--and 81,250 dollars in year two. Over 2 years savings totaled 85,474 dollars.

LIMITATIONS

Insufficient data required that assumptions be made for some inputs. DES is new in pharmacoeconomics and may be perceived as a limitation.

CONCLUSIONS

Somatropin use improves quality of life by reducing the need for parenteral nutrition and results in health care cost savings.

Alternative Decision Analysis Modeling in the Economic Evaluation of Tumor Necrosis Factor Inhibitors for Rheumatoid Arthritis

OBJECTIVES

To provide a review of the studies that use decision models in the economic evaluation of tumor necrosis factor (TNF) inhibitors in rheumatoid arthritis (RA) and to address some important issues surrounding the choice of such modeling techniques in these economic evaluations.

METHODS

A systematic literature search was conducted by 1 author from the literature published from January 1996 to March 2005 through Medline, Embase, and Cochrane library databases.

RESULTS

The review yielded 29 studies that used decision models. Only 10 studies used a decision model in the economic analysis of the TNF inhibitors and were included in the final review. Decision model types included the following in the review articles: decision tree (2), Markov model (7), and discrete event simulation (1). These models vary in complexity and their choice depends on the course of disease, the impact of treatment, and the available data.

CONCLUSIONS

Based on the results derived from alternative modeling techniques, it is safe to say that all methods can provide useful information with regard to economic evaluations of TNF inhibitors. Even though different modeling techniques provide an appropriate representation of available data, their results should be interpreted contingent on the input data, assumptions, sensitivity analyses, and other alternative scenario analyses.

RELEVANCE

The transparency in the models will encourage end users such as policymakers and prescribers to make informed judgments regarding the appropriateness of the methods and the validity of the results.

Modelling the economic and health consequences of cardiac resynchronization therapy in the UK

OBJECTIVE

Clinical evidence supports the use of cardiac resynchronization therapy (CRT) in advanced heart failure, but its cost-effectiveness is still unclear. This analysis assessed the economic and health consequences in the UK of implanting a CRT in patients with NYHA class III-IV heart failure.

METHODS

A discrete event simulation of heart failure was used to compare the course over 5 years of 1000 identical pairs of patients -- one receiving both CRT and optimum pharmacologic treatment (OPT), the other OPT alone. All inputs were obtained from the data collected in the CArdiac REsynchronization in Heart Failure (CARE-HF) trial and a hospital in the UK. Direct medical costs (in 2004 pound) from the perspective of the National Health Service were considered. Both costs and benefits were discounted at 3.5%. Sensitivity analyses addressed all model inputs and multivariate analyses were performed by varying key parameters simultaneously.

RESULTS

The model predicted 471 deaths and 2263 hospitalizations over 5 years with OPT alone and 348 deaths and 1764 hospitalizations with CRT, equivalent to a 26% reduction in mortality and 22% in hospitalizations, at a discounted cost of pound 11,423 per patient with CRT vs. pound 4,900 with OPT alone. CRT was predicted to increase quality-adjusted survival by 0.43 QALYs per patient, resulting in an incremental cost-effectiveness ratio of pound 15,247 per QALY gained (range: pound 12,531- pound 23,184). Sensitivity analyses revealed that this outcome was most sensitive to time horizon and cost of implantation.

CONCLUSION

Based on these 5-year analyses, CRT is expected to yield substantial health benefits at a reasonable cost.

Modelling the health benefits and economic implications of implanting dual-chamber vs. single-chamber ventricular pacemakers in the UK

AIMS

To estimate the consequences of managing bradycardia due to sinoatrial node disease or atrioventricular block with dual-chamber vs. single-chamber ventricular pacemakers.

METHODS AND RESULTS

A discrete-event simulation was conducted to predict outcomes over 5 years. Patients could develop post-operative complications, clinically relevant pacemaker syndrome leading to replacement of single-chamber with dual-chamber, atrial fibrillation (AF; which if chronic might require anticoagulants) or stroke. Survival, quality-adjusted life years (QALYs), complications, and associated direct medical costs were estimated (2003 British Pounds pounds sterling). Identical patients were simulated after receiving a single-chamber device or a more expensive dual-chamber pacemaker. Probabilities of conditions were obtained from clinical trials. Benefits were discounted at 1.5% and costs at 6%. Post-operative complications increased from 6.4% with single-chamber to 7.7% with dual-chamber but AF decreased (22 vs. 18%) as did clinically relevant pacemaker symptoms (16.8 vs. 0%). Approximately 4300 pounds sterling were accrued per patient over 5 years. Additional health benefits with dual-chamber are achieved at a mean net cost of 43 pounds sterling per patient, leading to 0.09 QALY with a cost-effectiveness ratio of 477 pounds sterling/QALY.

CONCLUSION

Implanting the costlier device increases the cost of the initial operation; however, this is expected to be offset by a reduction in costs associated with re-operations and AF.

Economic Implications of Extended-Release Metoprolol Succinate for Heart Failure in the MERIT-HF Trial: A US Perspective of the MERIT-HF Trial

BACKGROUND

The MERIT-HF trial demonstrated improved survival and fewer hospitalizations for worsening heart failure with extended-release (ER) metoprolol succinate in patients with heart failure. This study sought to estimate the economic implications of this trial from a US perspective.

METHODS AND RESULTS

A discrete event simulation was developed to examine the course of patients with heart failure. Characteristics of the population modeled, probabilities of hospitalization and death with standard therapy, and risk reductions with ER metoprolol succinate were obtained from Metoprolol CR/XL Randomized Intervention Trial in Chronic Heart Failure (MERIT-HF) and evaluated in weekly cycles. Direct medical costs were estimated from US databases in 2001 US dollars. Uncertainty in inputs was incorporated and analyses were carried out to estimate events prevented total and net costs. The model predicts that ER metoprolol succinate will prevent approximately 7 deaths and 15 hospitalizations from heart failure per 100 patients over 2 years. Compared with standard therapy alone, this translates to a cost reduction between $395 and $1112 per patient, depending on whether the costs of hospitalizations for other causes are included. Savings were maintained in 90% of the simulations.

CONCLUSION

This analysis predicts that the positive effect of ER metoprolol succinate on mortality and morbidity demonstrated in MERIT-HF leads to substantial savings.

Health and economic consequences of sevelamer use for hyperphosphatemia in patients on hemodialysis

OBJECTIVES

The safety and efficacy of sevelamer hydrochloride in binding phosphate in patients with end-stage renal disease and its ability to attenuate the progression of cardiac calcification have been well documented but not the longer-term health and economic implications. Thus, a model of the predicted long-term consequences of sevelamer compared with calcium-based binders (acetate and carbonate) was developed.

METHODS

Long-term cardiovascular implications of 1 year of treatment with phosphate binders in patients on hemodialysis are estimated based on the patient's demographics, comorbidities, and physiologic and renal parameters. The initial calcification score and expected changes over 1 year are derived using regression equations developed from the Treat-to-Goal study and translated to cardiovascular disease risk based on equations developed from a long-term cohort study. In this article, the implications of cardiovascular disease for life expectancy and medical costs are accounted for from a US payer perspective.

RESULTS

The cardioprotective effect of sevelamer over 1 year is estimated to result in a 12% reduction in cardiovascular events compared with calcium acetate. In a population of 100 patients, the savings of 205,600 dollars accrued due to avoiding nine cardiovascular events with sevelamer, largely offset the increased binder costs, leading to a favorable cost-effectiveness ratio of about 2200 dollars per (discounted) life-year gained.

CONCLUSIONS

Although both binders provide equivalent phosphate binding capacity, the results indicate that the advantage of 1 year of treatment with sevelamer in attenuating the progression of calcification has important clinical and economic consequences, suggesting that this provides good value for money.

Modelling the treated course of schizophrenia: development of a discrete event simulation model

In schizophrenia, modelling techniques may be needed to estimate the long-term costs and effects of new interventions. However, it seems that a simple direct link between symptoms and costs does not exist. Decisions about whether a patient will be hospitalized or admitted to a different healthcare setting are based not only on symptoms but also on social and environmental factors. This paper describes the development of a model to assess the dependencies between a broad range of parameters in the treatment of schizophrenia. In particular, the model attempts to incorporate social and environmental factors into the decision-making process for the prescription of new drugs to patients. The model was used to analyse the potential benefits of improving compliance with medication by 20% in patients in the UK. A discrete event simulation (DES) model was developed, to describe a cohort of schizophrenia patients with multiple psychotic episodes. The model takes into account the patient's sex, disease severity, potential risk of harm to self and society, and social and environmental factors. Other variables that change over time include the number of psychiatric consultations, the presence of psychotic episodes, symptoms, treatments, compliance, side-effects, the lack of ability to take care of him/herself, care setting and risk of harm. Outcomes are costs, psychotic episodes and symptoms. Univariate and multivariate sensitivity analyses were performed. Direct medical costs were considered (year of costing 2002), applying a 6.0% discount rate for costs and a 1.5% discount rate for outcome. The timeframe of the model is 5 years. When 50% of the decisions about the patient care setting are based on symptoms, a 20% increase in compliance was estimated to save 16,147 pounds and to avoid 0.55 psychotic episodes per patient over 5 years. Sensitivity analysis showed that the costs savings associated with increased compliance are robust over a range of variations in parameters. DES offers a flexible structure for modelling a disease, taking into account how a patient's history affects the course of the disease over time. This approach is particularly pertinent to schizophrenia, in which treatment decisions are complex. The model shows that better compliance increases the time between relapses, decreases the symptom score, and reduces the requirement for treatment in an intensive patient care setting, leading to cost savings. The extent of the cost savings depends on the relative importance of symptoms and of social and environmental factors in these decisions.