Pharmacoeconomic analyses using discrete event simulation

Modelling outcomes of complex treatment strategies following a clinical guideline for treatment decisions in patients with rheumatoid arthritis

BACKGROUND

Management of rheumatoid arthritis (RA) is characterised by a sequence of disease-modifying antirheumatic drugs (DMARDs) and biological response modifiers (BRMs). In most of the Western countries, the drug sequences are determined based on disease activity and treatment history of the patients. A model for realistic patient outcomes should reflect the treatment pathways relevant for patients with specific characteristics.

OBJECTIVE

This study aimed at developing a model that could simulate long-term patient outcomes and cost effectiveness of treatment strategies with and without inclusion of BRMs following a clinical guideline for treatment decisions.

METHODS

Discrete event simulation taking into account patient characteristics and treatment history was used for model development. Treatment effect on disease activity, costs, health utilities and times to events were estimated using Dutch observational studies. Long-term progression of physical functioning was quantified using a linear mixed-effects model. Costs and health utilities were estimated using two-part models. The treatment strategy recommended by the Dutch Society for Rheumatology where both DMARDs and BRMs were available (Strategy 2) was compared with the treatment strategy without BRMs (Strategy 1). Ten thousand theoretical patients were tracked individually until death. In the probabilistic sensitivity analysis, Monte Carlo simulations were performed with 1,000 sets of parameters sampled from appropriate probability distributions.

RESULTS

The simulated changes over time in disease activity and physical functioning were plausible. The incremental cost per quality-adjusted life-year gained of Strategy 2 compared with Strategy 1 was <euro>124,011. At a willingness-to-pay threshold higher than <euro>119,167, Strategy 2 dominated Strategy 1 in terms of cost effectiveness but the probability that the Strategy 2 is cost effective never exceeded 0.87.

CONCLUSIONS

It is possible to model the outcomes of complex treatment strategies based on a clinical guideline for the management of RA. Following the Dutch guideline and using real-life data, inclusion of BRMs in the treatment strategy for RA appeared to be less favourable in our model than in most of the existing models that compared drug sequences independent of patient characteristics and used data from randomised controlled clinical trials. Despite complexity and demand for extensive data, our modelling approach can help to identify the knowledge gaps in clinical guidelines for RA management and priorities for future research.

Decision-analytic models: current methodological challenges

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

Cost-effectiveness of early-initiated treatment for advanced-stage epithelial ovarian cancer patients: a modeling study

OBJECTIVE

Between diagnosis and primary treatment of patients with epithelial ovarian cancer (EOC), gaps of several weeks exist. Reducing these time intervals may benefit the patient and may lead to a reduction of costs. We explored the cost-effectiveness of early-initiated treatment of patients with suspected advanced-stage EOC compared with that of current treatment.

METHODS

A discrete event simulation was used to synthesize all available evidences and to evaluate the health care costs and effects (quality-adjusted life years [QALYs]) of the 2 treatment strategies over lifetime. Overall survival, progression-free survival, health-related quality of life, and costs of the separate events were assumed to remain equal. Other uncertainties were addressed using deterministic and probabilistic sensitivity analyses.

RESULTS

The treatment times of current and early-initiated treatment were 27 and 24 weeks, respectively. Early-initiated treatment yielded 3.42 QALYs per patient, for a total expected health care cost of €25,654. Current treatment yielded 3.40 QALYs per patient, for a total expected health care cost of €25,607. This resulted in an incremental cost-effectiveness ratio of €2592 per QALY gained for early-initiated treatment compared with that for current treatment. For the willingness to pay for €30,000 or more per QALY, early-initiated treatment had a 100% probability of being cost-effective compared with current treatment under the previously mentioned assumptions.

CONCLUSIONS

Given the current evidence, early-initiated treatment of patients with suspected advanced-stage EOC leads to additional QALYs and seems to be cost-effective compared with current treatment.

A systematic and critical review of model-based economic evaluations of pharmacotherapeutics in patients with bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a chronic and relapsing mental illness with a considerable health-related and economic burden. The primary goal of pharmacotherapeutics for BD is to improve patients' well-being. The use of decision-analytic models is key in assessing the added value of the pharmacotherapeutics aimed at treating the illness, but concerns have been expressed about the appropriateness of different modelling techniques and about the transparency in the reporting of economic evaluations.

OBJECTIVES

This paper aimed to identify and critically appraise published model-based economic evaluations of pharmacotherapeutics in BD patients.

METHODS

A systematic review combining common terms for BD and economic evaluation was conducted in MEDLINE, EMBASE, PSYCINFO and ECONLIT. Studies identified were summarised and critically appraised in terms of the use of modelling technique, model structure and data sources. Considering the prognosis and management of BD, the possible benefits and limitations of each modelling technique are discussed.

RESULTS

Fourteen studies were identified using model-based economic evaluations of pharmacotherapeutics in BD patients. Of these 14 studies, nine used Markov, three used discrete-event simulation (DES) and two used decision-tree models. Most of the studies (n = 11) did not include the rationale for the choice of modelling technique undertaken. Half of the studies did not include the risk of mortality. Surprisingly, no study considered the risk of having a mixed bipolar episode.

CONCLUSIONS

This review identified various modelling issues that could potentially reduce the comparability of one pharmacotherapeutic intervention with another. Better use and reporting of the modelling techniques in the future studies are essential. DES modelling appears to be a flexible and comprehensive technique for evaluating the comparability of BD treatment options because of its greater flexibility of depicting the disease progression over time. However, depending on the research question, modelling techniques other than DES might also be appropriate in some cases.

Markov modeling and discrete event simulation in health care: a systematic comparison

OBJECTIVES

The aim of this study was to assess if the use of Markov modeling (MM) or discrete event simulation (DES) for cost-effectiveness analysis (CEA) may alter healthcare resource allocation decisions.

METHODS

A systematic literature search and review of empirical and non-empirical studies comparing MM and DES techniques used in the CEA of healthcare technologies was conducted.

RESULTS

Twenty-two pertinent publications were identified. Two publications compared MM and DES models empirically, one presented a conceptual DES and MM, two described a DES consensus guideline, and seventeen drew comparisons between MM and DES through the authors' experience. The primary advantages described for DES over MM were the ability to model queuing for limited resources, capture individual patient histories, accommodate complexity and uncertainty, represent time flexibly, model competing risks, and accommodate multiple events simultaneously. The disadvantages of DES over MM were the potential for model overspecification, increased data requirements, specialized expensive software, and increased model development, validation, and computational time.

CONCLUSIONS

Where individual patient history is an important driver of future events an individual patient simulation technique like DES may be preferred over MM. Where supply shortages, subsequent queuing, and diversion of patients through other pathways in the healthcare system are likely to be drivers of cost-effectiveness, DES modeling methods may provide decision makers with more accurate information on which to base resource allocation decisions. Where these are not major features of the cost-effectiveness question, MM remains an efficient, easily validated, parsimonious, and accurate method of determining the cost-effectiveness of new healthcare interventions.

Economic implications of using bendamustine, alemtuzumab, or chlorambucil as a first-line therapy for chronic lymphocytic leukemia in the US: a cost-effectiveness analysis

BACKGROUND

The objective of this analysis was to evaluate the cost-effectiveness of using bendamustine versus alemtuzumab or bendamustine versus chlorambucil as a first-line therapy in patients with Binet stage B or C chronic lymphocytic leukemia (CLL) in the US.

METHODS

A discrete event simulation of the disease course of CLL was developed to evaluate the economic implications of single-agent treatment with bendamustine, alemtuzumab, or chlorambucil, which are indicated for a treatment-naïve patient population with Binet stage B or C CLL. Data from clinical trials were used to create a simulated patient population, risk equations for progression-free survival and survival post disease progression, response rates, and rates of adverse events. Costs from a US health care payer perspective in 2012 US dollars, survival (life years), and quality-adjusted life years (QALYs) were estimated over a patient's lifetime; all were discounted at 3% per year.

RESULTS

Compared with alemtuzumab, bendamustine was considered to be a dominant treatment providing greater benefit (6.10 versus 5.37 life years and 4.02 versus 3.45 QALYs) at lower cost ($78,776 versus $121,441). Compared with chlorambucil, bendamustine was associated with higher costs ($78,776 versus $42,337) but with improved health outcomes (6.10 versus 5.21 life years and 4.02 versus 3.30 QALYs), resulting in incremental cost-effectiveness ratios of $40,971 per life year gained and $50,619 per QALY gained.

CONCLUSION

Bendamustine is expected to provide cost savings and greater health benefit than alemtuzumab in treatment-naïve patients with CLL. Furthermore, it can be considered as a cost-effective treatment providing health benefits at an acceptable cost versus chlorambucil in the US.

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

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

Modeling hard clinical end-point data in economic analyses

OBJECTIVE

The availability of hard clinical end-point data, such as that on cardiovascular (CV) events among patients with type 2 diabetes mellitus, is increasing, and as a result there is growing interest in using hard end-point data of this type in economic analyses. This study investigated published approaches for modeling hard end-points from clinical trials and evaluated their applicability in health economic models with different disease features.

METHODS

A review of cost-effectiveness models of interventions in clinically significant therapeutic areas (CV diseases, cancer, and chronic lower respiratory diseases) was conducted in PubMed and Embase using a defined search strategy. Only studies integrating hard end-point data from randomized clinical trials were considered. For each study included, clinical input characteristics and modeling approach were summarized and evaluated.

RESULTS

A total of 33 articles (23 CV, eight cancer, two respiratory) were accepted for detailed analysis. Decision trees, Markov models, discrete event simulations, and hybrids were used. Event rates were incorporated either as constant rates, time-dependent risks, or risk equations based on patient characteristics. Risks dependent on time and/or patient characteristics were used where major event rates were >1%/year in models with fewer health states (<7). Models of infrequent events or with numerous health states generally preferred constant event rates.

LIMITATIONS

The detailed modeling information and terminology varied, sometimes requiring interpretation.

CONCLUSIONS

Key considerations for cost-effectiveness models incorporating hard end-point data include the frequency and characteristics of the relevant clinical events and how the trial data is reported. When event risk is low, simplification of both the model structure and event rate modeling is recommended. When event risk is common, such as in high risk populations, more detailed modeling approaches, including individual simulations or explicitly time-dependent event rates, are more appropriate to accurately reflect the trial data.

Cost effectiveness of mesh prophylaxis to prevent parastomal hernia in patients undergoing permanent colostomy for rectal cancer

BACKGROUND

Parastomal hernia (PSH) is common after stoma formation. Studies have reported that mesh prophylaxis reduces PSH, but there are no cost-effectiveness data. Our objective was to determine the cost effectiveness of mesh prophylaxis vs no prophylaxis to prevent PSH in patients undergoing abdominoperineal resection with permanent colostomy for rectal cancer.

STUDY DESIGN

Using a cohort Markov model, we modeled the costs and effectiveness of mesh prophylaxis vs no prophylaxis at the index operation in a cohort of 60-year-old patients undergoing abdominoperineal resection for rectal cancer during a time horizon of 5 years. Costs were expressed in 2012 Canadian dollars (CAD$) and effectiveness in quality-adjusted life years. Deterministic and probabilistic sensitivity analyses were performed.

RESULTS

In patients with stage I to III rectal cancer, prophylactic mesh was dominant (less costly and more effective) compared with no mesh. In patients with stage IV disease, mesh prophylaxis was associated with higher cost (CAD$495 more) and minimally increased effectiveness (0.05 additional quality-adjusted life years), resulting in an incremental cost-effectiveness ratio of CAD$10,818 per quality-adjusted life year. On sensitivity analyses, the decision was sensitive to the probability of mesh infection and the cost of the mesh, and method of diagnosing PSH.

CONCLUSIONS

In patients undergoing abdominoperineal resection with permanent colostomy for rectal cancer, mesh prophylaxis might be the less costly and more effective strategy compared with no mesh to prevent PSH in patients with stage I to III disease, and might be cost effective in patients with stage IV disease.

Simulated estimates of pre-pregnancy and gestational diabetes mellitus in the US: 1980 to 2008

PURPOSE

To simulate national estimates of prepregnancy and gestational diabetes mellitus (GDM) in non-Hispanic white (NHW) and non-Hispanic black (NHB) women.

METHODS

Prepregnancy diabetes and GDM were estimated as a function of age, race/ethnicity, and body mass index (BMI) using South Carolina live singleton births from 2004-2008. Diabetes risk was applied to a simulated population. Age, natality and BMI were assigned to women according to race- and age-specific US Census, Natality and National Health and Nutrition Examination Surveys (NHANES) data, respectively.

RESULTS

From 1980-2008, estimated GDM prevalence increased from 4.11% to 6.80% [2.68% (95% CI 2.58%-2.78%)] and from 3.96% to 6.43% [2.47% (95% CI 2.39%-2.55%)] in NHW and NHB women, respectively. In NHW women prepregnancy diabetes prevalence increased 0.90% (95% CI 0.85%-0.95%) from 0.95% in 1980 to 1.85% in 2008. In NHB women from 1980 through 2008 estimated prepregnancy diabetes prevalence increased 1.51% (95% CI 1.44%-1.57%), from 1.66% to 3.16%.

CONCLUSIONS

Racial disparities in diabetes prevalence during pregnancy appear to stem from a higher prevalence of prepregnancy diabetes, but not GDM, in NHB than NHW.

Overview of parametric survival analysis for health-economic applications

Health economic models rely on data from trials to project the risk of events (e.g., death) over time beyond the span of the available data. Parametric survival analysis methods can be applied to identify an appropriate statistical model for the observed data, which can then be extrapolated to derive a complete time-to-event curve. This paper describes the properties of the most commonly used statistical distributions as a basis for these models and describes an objective process of identifying the most suitable parametric distribution in a given dataset. The approach can be applied with both individual-patient data as well as with survival probabilities derived from published Kaplan-Meier curves. Both are illustrated with analyses of overall survival from the Sorafenib Hepatocellular Carcinoma Assessment Randomised Protocol trial.

Lenalidomide for multiple myeloma: cost-effectiveness in patients with one prior therapy in England and Wales

PURPOSE

To determine the cost effectiveness of lenalidomide plus dexamethasone (LEN/DEX) versus DEX alone in managing multiple myeloma (MM) patients who have failed one prior therapy.

MATERIALS AND METHODS

An individual simulation model was designed to capture the costs and outcomes of LEN/DEX versus DEX therapy in relapsed refractory MM patients. MM009/010 efficacy data were adjusted for treatment cross-over and extrapolated to patient lifetime. Resource use for MM disease progression and adverse events were obtained from expert physicians and costed from the perspective of the National Health Service (England and UK) and included a patient access scheme for LEN. Utility values were obtained from published literature.

RESULTS

The simulation model estimated an incremental improvement in time to progression of 9.5 months, an additional 3.2 life-years, and 2.2 quality adjusted life years (QALY) for LEN/DEX compared to DEX alone. Including the costs of therapy with the patient access scheme, adverse events, and disease follow-up, the incremental cost effectiveness ratio was £30,153/QALY for LEN/DEX compared to DEX alone in MM patients who have failed one prior therapy.

CONCLUSION

LEN/DEX is a cost effective oncology therapy from the perspective of the NHS for MM patients with one prior treatment.

Cost-utility analysis of Ruconest(®) (conestat alfa) compared to Berinert(®) P (human C1 esterase inhibitor) in the treatment of acute, life-threatening angioedema attacks in patients with hereditary angioedema

INTRODUCTION

Administration of human C1 esterase inhibitor (Berinert(®) P) from target import is the most widespread treatment strategy for patients with hereditary angioedema (HAE). However, a therapeutic health program including Ruconest(®) (conestat alfa) could shorten a patient's expectancy for a life-saving treatment.

AIM

To evaluate the cost-utility of Ruconest(®) (conestat alfa) financed from public funds within the newly introduced therapeutic health program compared with Berinert(®) P (human C1 esterase inhibitor) in the treatment of acute angioedema attacks in adults with HAE.

MATERIAL AND METHODS

The cost-utility analysis from the Polish healthcare payer's perspective was performed for 1 year (2012). The costs and health outcomes were simulated for three pairs of eligible HAE patient groups (active treatment and corresponding placebo). The incremental costs of each intervention compared with placebo were listed together (direct or indirect comparisons between options were impossible due to limited clinical data available).

RESULTS

The incremental cost-utility ratios (ICURs) for the evaluated interventions compared with placebo were as follows: EUR 15,226 per QALY (Ruconest(®)) and EUR 27,786 per QALY (Berinert(®) P). The probability of cost-utility (ICUR < EUR 24,279 per QALY) assessed for Ruconest(®) administered in the case of acute angioedema attack was 61% and 41% for Berinert(®) P.

CONCLUSIONS

The administration of Ruconest(®) in acute life-threatening angioedema attacks is economically justified from the Polish healthcare payer's perspective, results in lower costs and is characterized by higher cost-utility probability compared with Berinert(®) P.

Economic evaluation of therapies for patients suffering from relapsed-refractory multiple myeloma in Greece

Background

Multiple myeloma is a hematologic malignancy that incurs a substantial economic burden in care management. Since most patients with multiple myeloma eventually relapse or become refractory to current therapies (rrMM), the aim of this study was to assess the cost-effectiveness of the combination of lenalidomide–dexamethasone, relative to bortezomib alone, in patients suffering from rrMM in Greece.

Methods

An international discrete event simulation model was locally adapted to estimate differences in overall survival and treatment costs associated with the two alternative treatment options. The efficacy data utilized came from three international trials (MM-009, MM-010, APEX). Quality of life data were extracted from the published literature. Data on resource use and prices came from relevant local sources and referred to 2012. The perspective of the analysis was that of public providers. Total costs for monitoring and administration of therapy to patients, management of adverse events, and cost of medication were captured. A 3.5% discount rate was used for costs and health outcomes. A Monte Carlo simulation was used to estimate probabilistic results with 95% uncertainty intervals (UI) and a cost-effectiveness acceptability curve.

Results

The mean number of quality-adjusted life years (QALYs) was 3.01 (95% UI 2.81–3.20) and 2.22 (95% UI 2.02–2.41) for lenalidomide–dexamethasone and bortezomib, respectively, giving an incremental gain of 0.79 (95% UI 0.49–1.06) QALYs in favor of lenalidomide–dexamethasone. The mean cost of therapy per patient was estimated at €80;77,670 (95% UI €80;76,509–€80;78,900) and €80;48,928 (95% UI €80;48,300–€80;49,556) for lenalidomide–dexamethasone and bortezomib, respectively. The incremental cost per life year gained with lenalidomide–dexamethasone was estimated at €80;29,415 (95% UI €80;23,484–€80;37,583) and the incremental cost per QALY gained at €80;38,268 (95% UI €80;27,001–€80;58,065). The probability of lenalidomide–dexamethasone being a cost-effective therapy option at a threshold three times the per capita income (€80;60,000 per QALY) was higher than 95%. The results remained constant, without altering the conclusions, under several hypothetical scenarios.

Conclusion

The combination of lenalidomide and dexamethasone may represent a cost-effective choice relative to bortezomib monotherapy for patients in Greece with previously treated multiple myeloma.

Florbetaben PET in the Early Diagnosis of Alzheimer's Disease: A Discrete Event Simulation to Explore Its Potential Value and Key Data Gaps

The growing understanding of the use of biomarkers in Alzheimer's disease (AD) may enable physicians to make more accurate and timely diagnoses. Florbetaben, a beta-amyloid tracer used with positron emission tomography (PET), is one of these diagnostic biomarkers. This analysis was undertaken to explore the potential value of florbetaben PET in the diagnosis of AD among patients with suspected dementia and to identify key data that are needed to further substantiate its value. A discrete event simulation was developed to conduct exploratory analyses from both US payer and societal perspectives. The model simulates the lifetime course of disease progression for individuals, evaluating the impact of their patient management from initial diagnostic work-up to final diagnosis. Model inputs were obtained from specific analyses of a large longitudinal dataset from the New England Veterans Healthcare System and supplemented with data from public data sources and assumptions. The analyses indicate that florbetaben PET has the potential to improve patient outcomes and reduce costs under certain scenarios. Key data on the use of florbetaben PET, such as its influence on time to confirmation of final diagnosis, treatment uptake, and treatment persistency, are unavailable and would be required to confirm its value.

Cost-effectiveness of using a molecular diagnostic test to improve preoperative diagnosis of thyroid cancer

OBJECTIVE

Fine-needle aspiration biopsy (FNAB) is a safe and inexpensive diagnostic procedure for evaluating thyroid nodules.Up to 25% of the results from an FNAB, however, may not be diagnostic or may be indeterminate, leading to a subsequent diagnostic thyroid surgery. A new molecularly based diagnostic test could potentially reduce indeterminate cytological results and, with high accuracy, provide a definitive diagnosis for cancer in thyroid nodules. The aim of the study was to estimate the cost-effectiveness of utilizing a molecular diagnostic (DX) test as an adjunct to FNAB, compared with NoDX, to improve the preoperative diagnosis of thyroid nodules.

METHODS

We constructed a patient-level simulation model to estimate the clinical and economic outcomes of using a DX test compared with current practice (NoDX) for the diagnosis of thyroid nodules. By using a cost-effectiveness framework, we measured incremental clinical benefits in terms of quality-adjusted life-years and incremental costs over a 10-year time horizon.

RESULTS

Assuming 95% sensitivity and specificity of the Dx test when used as an adjunct to FNAB, the utilization of the DX test resulted in a gain of 0.046 quality-adjusted life-years (95% confidence interval 0.019-0.078) and a saving of $1087 (95% confidence interval $691-$1533) in direct costs per patient. If the cost of the Dx test is less than $1087 per test, we expect to save quality-adjusted life-years and reduce costs when it is utilized. Sensitivity of the DX test, compared with specificity, had a larger influence on the overall outcomes.

Contribution of modeling approaches and virtual populations in transposing the results of clinical trials into real life and in enlightening public health decisions

Modeling consists in aggregating separate pieces of knowledge, according to a given structure and rules. It allows studying the behavior of more or less complex systems by simulation techniques. Modeling is used in different state-of-the-art technological domains (meteorology, aeronautics). Its use has grown for the evaluation of medicines and medical devices, from conception to prescription (marketing authorization, reimbursement, price setting and re-registrations). It follows a scientific approach and is the object of good practice recommendations. Coupling models to virtual populations allows obtaining realistic results at the population level, testing diagnostic or therapeutic strategies, as well as estimating the consequences of transposing the results of clinical trials to the population. Through examples, the participants of the Round Table analyzed the contributions of the coupling of models and realistic virtual populations, and proposed guidelines for their judicious and systematic use.

Net costs of dementia in Sweden - an incidence based 10 year simulation study

BACKGROUND

Aging of the population results in increasing number people suffering from dementia, and this will have a great impact on costs for the society. Because of the long duration of dementia disorders, it is difficult to collect empirical data for the whole survival period of incident cases. Therefore, modeling approaches are frequently used. The purpose of this study was to describe the costs of an incident dementia cohort with progression modeling.

METHODS

Epidemiological data indicated that the incidence of dementia in Sweden was 24,000 people in 2005. Thus, incident cases were run in a Markov model for 10 cycles of 1 year each. Severity state specific costs were used and defined by Clinical Dementia Rating scale.

RESULTS

Total cost for the cohort was 27.24 billion Swedish Krona (SEK). The mean cost per person and year was 269,558 SEK. Total cost for long-term institutional care was 21 billion SEK during the modeled period.

CONCLUSION

Cost of long-term institutional care is the major cost driver, even in mild dementia.

Modeling Good Research Practices—Overview

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

State-Transition Modeling

State-transition modeling (STM) is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling, including both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, STM is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. STMs have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs.

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

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

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

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

A critical review of model-based economic studies of depression: modelling techniques, model structure and data sources

Depression is the most common mental health disorder and is recognized as a chronic disease characterized by multiple acute episodes/relapses. Although modelling techniques play an increasingly important role in the economic evaluation of depression interventions, comparatively little attention has been paid to issues around modelling studies with a focus on potential biases. This, however, is important as different modelling approaches, variations in model structure and input parameters may produce different results, and hence different policy decisions. This paper presents a critical review of literature on recently published model-based cost-utility studies of depression. Taking depression as an illustrative example, through this review, we discuss a number of specific issues in relation to the use of decision-analytic models including the type of modelling techniques, structure of models and data sources. The potential benefits and limitations of each modelling technique are discussed and factors influencing the choice of modelling techniques are addressed. This review found that model-based studies of depression used various simulation techniques. We note that a discrete-event simulation may be the preferred technique for the economic evaluation of depression due to the greater flexibility with respect to handling time compared with other individual-based modelling techniques. Considering prognosis and management of depression, the structure of the reviewed models are discussed. We argue that a few reviewed models did not include some important structural aspects such as the possibility of relapse or the increased risk of suicide in patients with depression. Finally, the appropriateness of data sources used to estimate input parameters with a focus on transition probabilities is addressed. We argue that the above issues can potentially bias results and reduce the comparability of economic evaluations.

Cost effectiveness of quetiapine in patients with acute bipolar depression and in maintenance treatment after an acute depressive episode

BACKGROUND

Bipolar disorder has a significant impact upon a patient's quality of life, imposing a considerable economic burden on the individual, family members and society as a whole. Several medications are indicated for the acute treatment of mania and depression associated with bipolar disorder as well as for maintenance therapy; however, these have varying efficacy, tolerability and costs.

OBJECTIVE

The objective of this study was to develop a new discrete-event simulation model to analyse the long-term consequences of pharmacological therapy for the management of bipolar I and II disorders (acute treatment of episodes of mania and depression as well as maintenance therapy).

METHODS

Probabilities of remission and relapse were obtained from clinical trial data and meta-analyses. Costs (year 2011 values) were assessed from a UK healthcare payer's perspective, and included pharmacological therapy and resource use associated with the treatment of mood events and selected adverse events. The health effects were measured in terms of QALYs.

RESULTS

For a patient starting with acute depression or in remission at 40 years of age (which was the average age in the clinical trials), quetiapine 300 mg/day was a cost-effective strategy compared with olanzapine 15 mg/day over a 5-year time frame. With acute bipolar depression as a starting episode, the 5-year medical costs were £323 higher and QALYs were 0.038 higher for quetiapine compared with olanzapine, corresponding to a cost-effectiveness ratio of £8600 per QALY gained.

CONCLUSION

Compared with olanzapine, the results suggest that quetiapine is cost effective as a maintenance treatment for bipolar depression.

Pharmacoeconomic considerations of treating patients with advanced Parkinson's disease

INTRODUCTION

Parkinson's disease (PD) is one of the most common neurodegenerative diseases. In the later (advanced) stages of PD, the initial treatment of early PD becomes less effective and long-term side effects of dopaminergic treatment become apparent. In advanced PD, motor and non-motor complications occur, which increase treatment costs. Increasing disability and impaired activities of daily living concomitantly raise indirect costs, due to loss in productivity. Hence, the economic burden of advanced PD is substantial for both the society and the patients with their caregivers.

AREAS COVERED

A systematic literature search was performed involving the databases NHS CRD (National Health Service Centre for Reviews and Dissemination) and PubMed until July 15, 2011. "Parkinson" [Mesh] and "cost" were used as search terms in PubMed and only "Parkinson" in the CRD database.

EXPERT OPINION

Economic evaluations are scarce and heterogeneous, and their interpretation may be limited due to methodological shortcomings. Dopamine agonists, COMT and MAO-B inhibitors as well levodopa infusion and deep brain stimulation are reported to be cost-effective in the respective decision frameworks. However, these results are heavily dependent on assumptions of drug costs and effect sizes used in the models. More detailed real-life information from long-term clinical trials is needed to feed the economic models, especially for head-to-head comparisons. To date, no economic evaluation has been undertaken for possible neuroprotective/disease modifying effects, and further research is needed for evaluations of interventions for non-motor symptoms.

Evaluating the cost effectiveness of donepezil in the treatment of Alzheimer's disease in Germany using discrete event simulation

BACKGROUND

Previous cost-effectiveness studies of cholinesterase inhibitors have modeled Alzheimer's disease (AD) progression and treatment effects through single or global severity measures, or progression to "Full Time Care". This analysis evaluates the cost-effectiveness of donepezil versus memantine or no treatment in Germany by considering correlated changes in cognition, behavior and function.

METHODS

Rates of change were modeled using trial and registry-based patient level data. A discrete event simulation projected outcomes for three identical patient groups: donepezil 10 mg, memantine 20 mg and no therapy. Patient mix, mortality and costs were developed using Germany-specific sources.

RESULTS

Treatment of patients with mild to moderately severe AD with donepezil compared to no treatment was associated with 0.13 QALYs gained per patient, and 0.01 QALYs gained per caregiver and resulted in average savings of €7,007 and €9,893 per patient from the healthcare system and societal perspectives, respectively. In patients with moderate to moderately-severe AD, donepezil compared to memantine resulted in QALY gains averaging 0.01 per patient, and savings averaging €1,960 and €2,825 from the healthcare system and societal perspective, respectively.In probabilistic sensitivity analyses, donepezil dominated no treatment in most replications and memantine in over 70% of the replications. Donepezil leads to savings in 95% of replications versus memantine.

CONCLUSIONS

Donepezil is highly cost-effective in patients with AD in Germany, leading to improvements in health outcomes and substantial savings compared to no treatment. This holds across a variety of sensitivity analyses.

Cost-effectiveness analysis for antidepressants and cognitive behavioral therapy for major depression in Thailand

OBJECTIVE

To determine the cost-effectiveness of fluoxetine and cognitive-behavioral therapy (CBT) for major depression in Thailand.

METHODS

A microsimulation model was developed to describe the variation in course of disease between individuals. Model inputs included Thai data on disease parameters and costs while impact measures were derived from a systematic review and meta-analysis of the international literature. Fluoxetine as the cheapest antidepressant drug in Thailand was analyzed for treatment of episodes plus a 6-month continuation phase and for maintenance treatment over 5 years of follow-up. CBT was analyzed for episodic treatment and for 5-year maintenance treatment. Results are presented as cost (Thai bahts) per disability-adjusted life-year (DALY) averted, compared with a "do-nothing" scenario.

RESULTS

The cost-effectiveness ratios of all interventions were below 1 time Thailand's gross domestic product of 110,000 bahts per capita. The uncertainty ranges around the cost-effectiveness ratios overlap: maintenance treatment with CBT 11,000 bahts per DALY (8,000-14,000); episodic treatment with CBT 23,000 bahts per DALY (10,000-36,000); episodic plus continuation drug treatment 33,000 bahts per DALY (26,000-44,000); maintenance drug treatment 38,000 bahts per DALY (30,000-48,000); and episodic drug treatment 42,000 bahts per DALY (32,000-57,000).

CONCLUSIONS

CBT and generic fluoxetine are cost-effective treatment options for both episodic and maintenance treatment of major depression in Thailand. Maintenance treatment has the greatest potential of health gain.

A discrete event modelling framework for simulation of long-term outcomes of sequential treatment strategies for ankylosing spondylitis

OBJECTIVE

To develop a modelling framework which can simulate long-term quality of life, societal costs and cost-effectiveness as affected by sequential drug treatment strategies for ankylosing spondylitis (AS).

METHODS

Discrete event simulation paradigm was selected for model development. Drug efficacy was modelled as changes in disease activity (Bath Ankylosing Spondylitis Disease Activity Index (BASDAI)) and functional status (Bath Ankylosing Spondylitis Functional Index (BASFI)), which were linked to costs and health utility using statistical models fitted based on an observational AS cohort. Published clinical data were used to estimate drug efficacy and time to events. Two strategies were compared: (1) five available non-steroidal anti-inflammatory drugs (strategy 1) and (2) same as strategy 1 plus two tumour necrosis factor α inhibitors (strategy 2). 13,000 patients were followed up individually until death. For probability sensitivity analysis, Monte Carlo simulations were performed with 1000 sets of parameters sampled from the appropriate probability distributions.

RESULTS

The models successfully generated valid data on treatments, BASDAI, BASFI, utility, quality-adjusted life years (QALYs) and costs at time points with intervals of 1-3 months during the simulation length of 70 years. Incremental cost per QALY gained in strategy 2 compared with strategy 1 was €35,186. At a willingness-to-pay threshold of €80,000, it was 99.9% certain that strategy 2 was cost-effective.

CONCLUSIONS

The modelling framework provides great flexibility to implement complex algorithms representing treatment selection, disease progression and changes in costs and utilities over time of patients with AS. Results obtained from the simulation are plausible.

Decision-analytic models to simulate health outcomes and costs in heart failure: a systematic review

Chronic heart failure (CHF) is a critical public health issue with increasing effect on the healthcare budgets of developed countries. Various decision-analytic modelling approaches exist to estimate the cost effectiveness of health technologies for CHF. We sought to systematically identify these models and describe their structures. We performed a systematic literature review in MEDLINE/PreMEDLINE, EMBASE, EconLit and the Cost-Effectiveness Analysis Registry using a combination of search terms for CHF and decision-analytic models. The inclusion criterion required 'use of a mathematical model evaluating both costs and health consequences for CHF management strategies'. Studies that were only economic evaluations alongside a clinical trial or that were purely descriptive studies were excluded. We identified 34 modelling studies investigating different interventions including screening (n = 1), diagnostics (n = 1), pharmaceuticals (n = 15), devices (n = 13), disease management programmes (n = 3) and cardiac transplantation (n = 1) in CHF. The identified models primarily focused on middle-aged to elderly patients with stable but progressed heart failure with systolic left ventricular dysfunction. Modelling approaches varied substantially and included 27 Markov models, three discrete-event simulation models and four mathematical equation sets models; 19 studies reported QALYs. Three models were externally validated. In addition to a detailed description of study characteristics, the model structure and output, the manuscript also contains a synthesis and critical appraisal for each of the modelling approaches. Well designed decision models are available for the evaluation of different CHF health technologies. Most models depend on New York Heart Association (NYHA) classes or number of hospitalizations as proxy for disease severity and progression. As the diagnostics and biomarkers evolve, there is the hope for better intermediate endpoints for modelling disease progression as those that are currently in use all have limitations.

Model-based economic evaluation in Alzheimer's disease: a review of the methods available to model Alzheimer's disease progression

OBJECTIVE

To consider the methods available to model Alzheimer's disease (AD) progression over time to inform on the structure and development of model-based evaluations, and the future direction of modelling methods in AD.

METHODS

A systematic search of the health care literature was undertaken to identify methods to model disease progression in AD. Modelling methods are presented in a descriptive review.

RESULTS

The literature search identified 42 studies presenting methods or applications of methods to model AD progression over time. The review identified 10 general modelling frameworks available to empirically model the progression of AD as part of a model-based evaluation. Seven of these general models are statistical models predicting progression of AD using a measure of cognitive function. The main concerns with models are on model structure, around the limited characterization of disease progression, and on the use of a limited number of health states to capture events related to disease progression over time. None of the available models have been able to present a comprehensive model of the natural history of AD.

CONCLUSIONS

Although helpful, there are serious limitations in the methods available to model progression of AD over time. Advances are needed to better model the progression of AD and the effects of the disease on peoples' lives. Recent evidence supports the need for a multivariable approach to the modelling of AD progression, and indicates that a latent variable analytic approach to characterising AD progression is a promising avenue for advances in the statistical development of modelling methods.

A discrete-event simulation of smoking-cessation strategies based on varenicline pivotal trial data

BACKGROUND

Smoking is the leading cause of preventable death in the US. While one in five individuals smoke, and 70% of these indicate a desire to quit, <5% of unaided quit attempts succeed. Cessation aids can double or triple the odds of successfully quitting. Models of smoking-cessation behaviour can elucidate the implications of individual abstinence patterns to allow better tailoring of quit attempts to an individual's characteristics.

OBJECTIVE

The objectives of this study were to develop and validate a discrete-event simulation (DES) to evaluate the benefits of smoking abstinence using data from the pooled pivotal clinical trials of varenicline versus bupropion or placebo for smoking cessation and to provide a foundation for the development of a lifetime smoking-cessation model.

METHODS

The DES model simulated the outcome of a single smoking-cessation attempt over 1 year, in accordance with the clinical trial timeframes. Pharmaceutical costs were assessed from the perspective of a healthcare payer. The model randomly sampled patient profiles from the pooled varenicline clinical trials. All patients were physically and mentally healthy adult smokers who were motivated to quit abruptly. The model allowed for comparisons of up to five distinct treatment approaches for smoking cessation. In the current analyses, three interventions corresponding to the clinical trials were evaluated, which included brief counselling plus varenicline 1.0 mg twice daily (bid) or bupropion SR 150 mg bid versus placebo (i.e. brief counselling only). The treatment periods in the clinical trials were 12 weeks (target quit date: day 8), with a 40-week non-treatment follow-up, and counselling continuing over the entire 52-week period in all treatment groups. The main outcome modelled was the continuous abstinence rate (CAR; defined as complete abstinence from smoking and confirmed by exhaled carbon monoxide ≤ 10 ppm) at end of treatment (weeks 9-12) and long-term follow-up (weeks 9-52), and total time abstinent from smoking over the course of 52 weeks. The model also evaluated costs and cost-effectiveness outcomes.

RESULTS

For the varenicline, bupropion and placebo cohorts, respectively, the model predicted CARs for weeks 9-12 of 44.3%, 30.4% and 18.6% compared with observed rates of 44.0%, 29.7% and 17.7%; over weeks 9-52, predicted CARs in the model compared with observed rates in the pooled clinical studies were 22.9%, 16.4% and 9.4% versus 22.4%, 15.4% and 9.3%, respectively. Total mean abstinence times accrued in the model varenicline, bupropion and placebo groups, respectively, were 3.6, 2.6 and 1.5 months and total pharmaceutical treatment costs were $US261, $US442 and $US0 (year 2008 values) over the 1-year model period. Using cost per abstinent-month achieved as a measure of cost effectiveness, varenicline dominated bupropion and yielded an incremental cost-effectiveness ratio of $US124 compared with placebo.

CONCLUSION

The model accurately replicated abstinence patterns observed in the clinical trial data using individualized predictions and indicated that varenicline was more effective and may be less costly than bupropion. This simulation incorporated individual predictions of abstinence and relapse, and provides a framework for lifetime modelling that considers multiple quit attempts over time in diverse patient populations using a variety of quit attempt strategies.

Cost-effectiveness of novel relapsed-refractory multiple myeloma therapies in Norway: lenalidomide plus dexamethasone vs bortezomib

OBJECTIVE

To estimate the cost-effectiveness (cost per additional life-year [LY] and quality-adjusted life-year [QALY] gained) of lenalidomide plus dexamethasone (LEN/DEX) compared with bortezomib for the treatment of relapsed-refractory multiple myeloma (rrMM) in Norway.

METHODS

A discrete-event simulation model was developed to predict patients? disease course using patient data, best response, and efficacy levels obtained from LEN/DEX MM-009/-010 trials and the bortezomib (APEX) published clinical trial. Predictive equations for time-to-progression (TTP) and post-progression survival (PPS) were developed by identifying the best fitting parametric survival distributions and selecting the most significant predictors. Disease and adverse event management was obtained via survey from Norwegian experts. Costs, derived from official Norwegian pricing data bases, included drug, administration, monitoring, and adverse event management costs.

RESULTS

Complete or partial responders were 65% for LEN/DEX compared to 43% for bortezomib. Derived median TTP was 11.45 months for LEN/DEX compared to 5.15 months for bortezomib. LYs and QALYs were higher for LEN/DEX (4.06 and 2.95, respectively) than for bortezomib (3.11 and 2.19, respectively). The incremental costs per QALY and LY gained from LEN/DEX were NOK 247,978 and NOK 198,714, respectively, compared to bortezomib. Multiple sensitivity analyses indicated the findings were stable. The parameters with the greatest impact were 4-year time horizon (NOK 441,457/QALY) and higher bound confidence intervals for PPS (NOK 118,392).

LIMITATIONS

The model analyzed two therapies not compared in head-to-head trials, and predicted results using an equation incorporating patient-level characteristics. It is a limited estimation of the costs and outcomes in a Norwegian setting.

CONCLUSIONS

The simulation model showed that treatment with LEN/DEX leads to greater LYs and QALYs when compared to bortezomib in the treatment of rrMM patients. The incremental cost-effectiveness ratio indicated treatment with LEN/DEX to be cost-effective and was the basis of the reimbursement approval of LEN/DEX in Norway.

Discrete event simulation: the preferred technique for health economic evaluations?

OBJECTIVES

To argue that discrete event simulation should be preferred to cohort Markov models for economic evaluations in health care.

METHODS

The basis for the modeling techniques is reviewed. For many health-care decisions, existing data are insufficient to fully inform them, necessitating the use of modeling to estimate the consequences that are relevant to decision-makers. These models must reflect what is known about the problem at a level of detail sufficient to inform the questions. Oversimplification will result in estimates that are not only inaccurate, but potentially misleading.

RESULTS

Markov cohort models, though currently popular, have so many limitations and inherent assumptions that they are inadequate to inform most health-care decisions. An event-based individual simulation offers an alternative much better suited to the problem. A properly designed discrete event simulation provides more accurate, relevant estimates without being computationally prohibitive. It does require more data and may be a challenge to convey transparently, but these are necessary trade-offs to provide meaningful and valid results.

CONCLUSION

In our opinion, discrete event simulation should be the preferred technique for health economic evaluations today.

Cost-effectiveness of adolescent pertussis vaccination for the Netherlands: using an individual-based dynamic model

BACKGROUND

Despite widespread immunization programs, a clear increase in pertussis incidence is apparent in many developed countries during the last decades. Consequently, additional immunization strategies are considered to reduce the burden of disease. The aim of this study is to design an individual-based stochastic dynamic framework to model pertussis transmission in the population in order to predict the epidemiologic and economic consequences of the implementation of universal booster vaccination programs. Using this framework, we estimate the cost-effectiveness of universal adolescent pertussis booster vaccination at the age of 12 years in the Netherlands.

METHODS/PRINCIPAL FINDINGS

We designed a discrete event simulation (DES) model to predict the epidemiological and economic consequences of implementing universal adolescent booster vaccination. We used national age-specific notification data over the period 1996-2000--corrected for underreporting--to calibrate the model assuming a steady state situation. Subsequently, booster vaccination was introduced. Input parameters of the model were derived from literature, national data sources (e.g. costing data, incidence and hospitalization data) and expert opinions. As there is no consensus on the duration of immunity acquired by natural infection, we considered two scenarios for this duration of protection (i.e. 8 and 15 years). In both scenarios, total pertussis incidence decreased as a result of adolescent vaccination. From a societal perspective, the cost-effectiveness was estimated at €4418/QALY (range: 3205-6364 € per QALY) and €6371/QALY (range: 4139-9549 € per QALY) for the 8- and 15-year protection scenarios, respectively. Sensitivity analyses revealed that the outcomes are most sensitive to the quality of life weights used for pertussis disease.

CONCLUSIONS/SIGNIFICANCE

To our knowledge we designed the first individual-based dynamic framework to model pertussis transmission in the population. This study indicates that adolescent pertussis vaccination is likely to be a cost-effective intervention for The Netherlands. The model is suited to investigate further pertussis booster vaccination strategies.

Cost-effectiveness of the addition of rituximab to CHOP chemotherapy in first-line treatment for diffuse large B-cell lymphoma in a population-based observational cohort in British Columbia, Canada

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) has primarily been treated with cyclophosphamide, doxorubicin, vincristine, and predisone (CHOP) chemotherapy since the 1970s. Recently, the addition of rituximab to CHOP (CHOP-R) has been found to improve survival and trial-based results have suggested that it is a cost-effective alternative to CHOP.

OBJECTIVES

The objective in this study was to evaluate the cost-effectiveness of CHOP-R relative to CHOP in first-line treatment of DLBCL in a population-based setting in British Columbia, Canada.

METHODS

We created a patient-level simulation model describing potential pathways for DLBCL patients initiating treatment with either CHOP or CHOP-R. Model parameters were populated with statistical analyses of individual-level treatment and effectiveness data and published cost estimates. All results were stratified by age at treatment initiation (<60 years vs. ≥60 years). The base-case scenario was based on a 15-year time horizon and a 3% discount rate. Probabilistic sensitivity analysis was performed. All costs are reported as 2006 $CDN.

RESULTS

For the base-case scenario, incremental cost-effectiveness ratios (ICERs) for younger individuals ranged from $11,965 per disease-free life-year gained to $19,144 per quality-adjusted life-year gained. For older individuals, estimated ICERs for all health outcomes were below $10,000 per unit outcome gained for a 15-year time horizon.

CONCLUSIONS

Using population-based data, CHOP-R was found to be a cost-effective alternative to CHOP, particularly for individuals aged 60 years and older. Results from this Canadian observational data source were consistent with international clinical trial-based studies. The use of CHOP-R as a first-line treatment for DLBCL is recommended, with respect to both clinical and cost-effectiveness.

Tutorial in medical decision modeling incorporating waiting lines and queues using discrete event simulation

In most decision-analytic models in health care, it is assumed that there is treatment without delay and availability of all required resources. Therefore, waiting times caused by limited resources and their impact on treatment effects and costs often remain unconsidered. Queuing theory enables mathematical analysis and the derivation of several performance measures of queuing systems. Nevertheless, an analytical approach with closed formulas is not always possible. Therefore, simulation techniques are used to evaluate systems that include queuing or waiting, for example, discrete event simulation. To include queuing in decision-analytic models requires a basic knowledge of queuing theory and of the underlying interrelationships. This tutorial introduces queuing theory. Analysts and decision-makers get an understanding of queue characteristics, modeling features, and its strength. Conceptual issues are covered, but the emphasis is on practical issues like modeling the arrival of patients. The treatment of coronary artery disease with percutaneous coronary intervention including stent placement serves as an illustrative queuing example. Discrete event simulation is applied to explicitly model resource capacities, to incorporate waiting lines and queues in the decision-analytic modeling example.

The cost effectiveness and cost utility of valsartan in chronic heart failure therapy in Italy: a probabilistic markov model

To evaluate the cost effectiveness and cost utility of the use of valsartan in addition to standard therapy for the treatment of patients with chronic heart failure with low left ventricular ejection fraction (LVEF). The study was conducted by means of a cohort simulation based on a probabilistic Markov model and projecting the 23-month follow-up results of the Val-HeFT (Valsartan Heart Failure Trial) study over a 10-year time horizon. The model included four states (New York Heart Association [NYHA] classes II, III, IV, and death), and had a cycle duration of 1 month. Probabilistic simulations were performed using the WinBUGS software for Bayesian analysis. The distribution of patient parameters (sex, age, use of beta-adrenoceptor antagonists, and ACE inhibitors) in the simulated population were derived from the Italian heart failure patient population. Individual mortality data were derived from general mortality data by multiplying by a NYHA state-specific relative risk, while the probability of changing NYHA class was taken from the Val-HeFT data. Costs (2007 values) were calculated from the perspective of the Italian Health Service (IHS) and included costs for drugs and heart failure hospitalizations. Quality-of-life (QOL) weights were obtained by using published health-related QOL data for heart failure patients. A 3.5% annual discount rate was applied. Probabilistic sensitivity analysis was performed on each parameter using original-source 95% confidence interval (CI) values, or a +/-10% range when 95% CI values were unavailable. For the 10-year time horizon, patients were estimated to live for an average of 2.3 years or 1.7 quality-adjusted life-years (QALYs), with slight increases in the valsartan group. In this group, hospitalizations for worsening heart failure were predicted to be significantly reduced and overall treatment costs per patient to decrease by about and U20AC;550. In subgroup analyses, valsartan lost dominance in patients in NYHA II, and in those receiving beta-adrenoceptor antagonists or ACE inhibitors; the mean incremental cost-utility ratio for these groups was 21 240, 129 200, and 36 500 and U20AC;/QALY, respectively. Valsartan in addition to standard therapy is predicted to dominate standard therapy alone in Italian patients with mild to severe heart failure and low LVEF. There are relevant differences among various patient subgroups, and valsartan is expected to be good value for money particularly in the treatment of the most severe and less intensively treated (no ACE inhibitors, no beta-adrenoceptor antagonist) heart failure patients.

Assessing the relationship between computational speed and precision: a case study comparing an interpreted versus compiled programming language using a stochastic simulation model in diabetes care

BACKGROUND

Simulation techniques are well suited to modelling diseases yet can be computationally intensive. This study explores the relationship between modelled effect size, statistical precision, and efficiency gains achieved using variance reduction and an executable programming language.

METHODS

A published simulation model designed to model a population with type 2 diabetes mellitus based on the UKPDS 68 outcomes equations was coded in both Visual Basic for Applications (VBA) and C++. Efficiency gains due to the programming language were evaluated, as was the impact of antithetic variates to reduce variance, using predicted QALYs over a 40-year time horizon.

RESULTS

The use of C++ provided a 75- and 90-fold reduction in simulation run time when using mean and sampled input values, respectively. For a series of 50 one-way sensitivity analyses, this would yield a total run time of 2 minutes when using C++, compared with 155 minutes for VBA when using mean input values. The use of antithetic variates typically resulted in a 53% reduction in the number of simulation replications and run time required. When drawing all input values to the model from distributions, the use of C++ and variance reduction resulted in a 246-fold improvement in computation time compared with VBA - for which the evaluation of 50 scenarios would correspondingly require 3.8 hours (C++) and approximately 14.5 days (VBA).

CONCLUSIONS

The choice of programming language used in an economic model, as well as the methods for improving precision of model output can have profound effects on computation time. When constructing complex models, more computationally efficient approaches such as C++ and variance reduction should be considered; concerns regarding model transparency using compiled languages are best addressed via thorough documentation and model validation.

Economic evaluation of doripenem for the treatment of nosocomial pneumonia in the US: discrete event simulation

OBJECTIVE

Patients with nosocomial pneumonia, particularly associated with ventilator use, are at an increased risk of death and further morbidity. Doripenem is a new broad-spectrum carbapenem that is approved for complicated intra-abdominal infection and complicated urinary tract infection and is under the Food and Drug Administration (FDA)'s review for nosocomial pneumonia and ventilator-associated pneumonia in the United States (US). The economic implications of this new antibiotic, relative to imipenem for treatment of nosocomial pneumonia, were investigated.

RESEARCH DESIGN AND METHODS

An economic model of the clinical course of nosocomial pneumonia after initiation of treatment was developed using discrete event simulation. Pairs of identical patients are generated; one receives doripenem and the other receives imipenem. Their clinical course is simulated using clinical trial data on treatment response, seizure rates, mortality, length of hospital and intensive care unit stays, days on mechanical ventilation, and emerging Pseudomonas aeruginosa (PsA) resistance; published treatment and hospital costs; and PsA transmission risk. Analyses of 10,000 patients per treatment for 100 replications were performed. From the perspective of a comprehensive payer, costs - in 2007 US Dollars (USDs) - were estimated for a treatment episode (35-49 days) without discounting. Study limitation includes clinical trial-driven design of the model in which some aspects may not represent actual practice; some assumptions around efficacy data due to data unavailability; and lack of consideration of factors that impact disease transmission such as hospital environment, hospital size, and hospital infection control as these analyses were not intended for any specific healthcare institution.

RESULTS

Doripenem yielded an average of approximately $7000 in savings per patient compared to imipenem, with 95% driven by reduction in hospital length of stay. The model predicted 63% fewer seizures, 52% fewer emerging PsA resistance, and 15% shorter stays leading to 46% fewer transmissions associated with doripenem.

CONCLUSION

Using doripenem for treatment of nosocomial pneumonia is expected to yield significant savings compared to imipenem use in the US.

No head-to-head trial? simulate the missing arms

Establishing efficacy relative to placebo is no longer sufficient for payers to agree to cover new interventions. Evidence from comparisons of competing interventions is increasingly important, although head-to-head studies are seldom available to inform decisions. In this article, we describe the simulated treatment comparison (STC) approach to incorporating 'missing arms' into an existing trial. This approach yields a simulated head-to-head trial and can address many of the differences among source trials. It provides inputs for economic models and can inform decision makers until actual trial data are available. A simulation is constructed to replicate an index trial, including enrolment, randomization and follow-up of patients. The simulation is driven by predictive equations derived from the index trial. Separate data for the comparators are used to calibrate the index equations to reflect the alternative interventions. The simulation is used to add the missing arms to the index trial and estimate the results that would have been obtained in a head-to-head trial. The STC can also be used to estimate results in various settings and populations and to explore variations in the trial design. An STC offers a way to derive comparative effectiveness in the absence of direct trial evidence and a platform to test design features that may help in planning future head-to-head studies.

Modelling the clinical and economic implications of galantamine in the treatment of mild-to-moderate Alzheimer's disease in Germany

OBJECTIVE

This analysis was to assess the long-term clinical and economic implications of galantamine in the treatment of mild-to-moderate Alzheimer's disease (AD) in Germany.

METHODS

An economic model was developed using discrete event simulation to predict the course of AD through changes in cognition, behavioural disturbance, and function over time. It compares the costs and benefits of galantamine versus no-drug treatment and ginkgo biloba. Clinical data were mainly derived from analyses of pooled data from clinical trials. Epidemiological and cost data were obtained from literature and public data sources. Costs (2009 euros) from the perspective of the German Statutory Health Insurance were used.

RESULTS

The mean survival time for the model population is about 3.44 years over 10 years of simulation. Galantamine delays average time to severe stage of the disease by 3.57 and 3.36 months, compared to no-drug treatment and ginkgo biloba, respectively. Galantamine reduces time spent in an institution by 2.34 and 2.21 months versus no-drug treatment and ginkgo biloba, respectively. The use of galantamine is projected to yield net savings of €3,978 and €3,972 per patient versus no-drug and ginkgo biloba treatments. These results, however, may be limited by lack of long-term comparative efficacy data as well as data on long-term care costs based on multiple outcome measures.

CONCLUSION

Compared to no-drug treatment and ginkgo biloba, galantamine therapy provides clinical benefits and achieves savings in healthcare costs associated with care for patients with mild-to-moderate AD in Germany.

Budget impact analysis of thrombolysis for stroke in Spain: a discrete event simulation model

OBJECTIVE

Thrombolysis within the first 3 hours after the onset of symptoms of a stroke has been shown to be a cost-effective treatment because treated patients are 30% more likely than nontreated patients to have no residual disability. The objective of this study was to calculate by means of a discrete event simulation model the budget impact of thrombolysis in Spain.

METHODS

The budget impact analysis was based on stroke incidence rates and the estimation of the prevalence of stroke-related disability in Spain and its translation to hospital and social costs. A discrete event simulation model was constructed to represent the flow of patients with stroke in Spain.

RESULTS

If 10% of patients with stroke from 2000 to 2015 would receive thrombolytic treatment, the prevalence of dependent patients in 2015 would decrease from 149,953 to 145,922. For the first 6 years, the cost of intervention would surpass the savings. Nevertheless, the number of cases in which patient dependency was avoided would steadily increase, and after 2006 the cost savings would be greater, with a widening difference between the cost of intervention and the cost of nonintervention, until 2015.

CONCLUSION

The impact of thrombolysis on society's health and social budget indicates a net benefit after 6 years, and the improvement in health grows continuously. The validation of the model demonstrates the adequacy of the discrete event simulation approach in representing the epidemiology of stroke to calculate the budget impact.

Health economics and the value of therapy in Alzheimer's disease

In increasingly aging societies throughout the developed and developing world, Alzheimer's disease and related dementias are fast becoming a critical public health issue, exacting an enormous toll on individuals and healthcare systems. Over the past 10 years, five drugs have been developed and approved for the symptomatic treatment of Alzheimer's dementia, and several disease-modifying drugs are in various stages of clinical development. While symptomatic medications were consistently shown to have clinical benefit in numerous efficacy studies, the cost effectiveness of antidementia therapies and their value to healthcare systems remain unclear. The pharmacoeconomics of antidementia therapies is an evolving field, with several unanswered questions. This poses many challenges for biopharmaceutical companies developing these therapies, regulatory agencies responsible for their approval, and payers responsible for ensuring their availability to patients. The challenge partly relates to the unique nature of dementia as a disease of impaired cognition, behavior, and function. Thus, the selection of appropriate outcome measures that directly relate to healthcare utilization, quality of life, caregiver burden, and pharmacoeconomic analysis has been difficult. The development of meaningful and widely acceptable outcome measures, as well as novel clinical-study designs, is needed to better evaluate cost effectiveness and to demonstrate the value of therapeutics for Alzheimer's disease. Providing the decision-makers in healthcare systems with a body of evidence that demonstrates a positive relationship between clinical outcomes and the economic and humanistic benefits of antidementia therapeutics will improve patient access to novel drugs as they become available.