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

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Poststroke Cognitive Impairment in Model-Based Economic Evaluation: A Systematic Review

INTRODUCTION

Cognitive impairment (CI) is a frequent consequence of stroke and is associated with increased costs and reduced quality of life. However, its inclusion in model-based economic evaluation for stroke is limited.

OBJECTIVE

To identify, review, and critically appraise current models of stroke for use in economic evaluation, and to identify applicability to modeling poststroke CI.

METHODS

PubMed, EMBASE, and the NHS Economic Evaluations Database (NHS EED) were systematically searched for papers published from January 2008 to August 2018. Studies that described the development or design of a model of stroke progression intended for use in economic evaluation were included. Abstracts were screened, followed by full text review of potentially relevant articles. Models that included CI were retained for data extraction, and among the remainder, models that included both stroke recurrence and disability were also retained. Relevance and potential for adaptation for modeling CI were assessed using a standard questionnaire.

RESULTS

Forty modeling studies were identified and categorized into 4 groups: Markov disability/recurrence (k = 29); CI (k = 2); discrete event simulation (k = 4), and other (k = 5). Only 2 modeling studies included CI as an outcome, and both focused on narrow populations at risk of intracranial aneurysm. None of the models allowed for disease progression in the absence of a stroke recurrence. None of the included studies carried out any sensitivity analysis in relation to model design or structure.

CONCLUSIONS

Current stroke models used in economic evaluation are not adequate to model poststroke CI or dementia, and will require adaptation to be used for this purpose.

A systematic literature review of methods of incorporating mortality in cost-effectiveness analyses of lipid-lowering therapies

AIMS

Cost effectiveness analysis (CEA) is a useful tool for estimating the value of an intervention in relation to alternatives. In cardiovascular disease (CVD), CEA is especially important, given the high economic and clinical burden. One key driver of value is CVD mortality prevention. However, data used to inform CEA parameters can be limited, given the difficulty in demonstrating statistically significant mortality benefit in randomized clinical trials (RCTs), due in part to the frequency of fatal events and limited trial durations. This systematic review identifies and summarizes whether published CVD-related CEAs have incorporated mortality benefits, and the methodology among those that did.

MATERIALS AND METHODS

A systematic literature review was conducted of CEAs of lipid-lowering therapies published between 2000-2017. Health technology assessments (HTA) and full-length manuscripts were included, and sources of mortality data and methods of applying mortality benefits were extracted. Results were summarized as proportions of articles to articulate common practices in CEAs of CVD.

RESULTS

This review identified 100 studies for inclusion, comprising 93 full-length manuscripts and seven HTA reviews. Among these, 99% assumed a mortality benefit in the model. However, 87 of these studies that incorporated mortality differences did so despite the trials used to inform model parameters not demonstrating statistically significant differences in mortality. None of the 12 studies that used statistically significant findings from an individual RCT were based on active control studies. In a sub-group analysis considering the 60 CEAs that incorporated a direct mortality benefit, 48 (80%) did not have RCT evidence for statistically significant benefit in CVD mortality.

LIMITATIONS AND CONCLUSIONS

The finding that few CEA models included mortality inputs from individual RCTs of lipid-lowering therapy may be surprising, as one might expect that treatment efficacy should be based on robust clinical evidence. However, regulatory requirements in CVD-related RCTs often lead to insufficient sample sizes and observation periods for detecting a difference in CVD mortality, which results in the use of intermediate outcomes, composite end-points, or meta-analysis to extrapolate long-term mortality benefit in a lifetime CEA.

A Systematic Review of Cardiovascular Outcomes-Based Cost-Effectiveness Analyses of Lipid-Lowering Therapies

BACKGROUND

Previous reviews have evaluated economic analyses of lipid-lowering therapies using lipid levels as surrogate markers for cardiovascular disease. However, drug approval and health technology assessment agencies have stressed that surrogates should only be used in the absence of clinical endpoints.

OBJECTIVE

The aim of this systematic review was to identify and summarise the methodologies, weaknesses and strengths of economic models based on atherosclerotic cardiovascular disease event rates.

METHODS

Cost-effectiveness evaluations of lipid-lowering therapies using cardiovascular event rates in adults with hyperlipidaemia were sought in Medline, Embase, Medline In-Process, PubMed and NHS EED and conference proceedings. Search results were independently screened, extracted and quality checked by two reviewers.

RESULTS

Searches until February 2016 retrieved 3443 records, from which 26 studies (29 publications) were selected. Twenty-two studies evaluated secondary prevention (four also assessed primary prevention), two considered only primary prevention and two included mixed primary and secondary prevention populations. Most studies (18) based treatment-effect estimates on single trials, although more recent evaluations deployed meta-analyses (5/10 over the last 10 years). Markov models (14 studies) were most commonly used and only one study employed discrete event simulation. Models varied particularly in terms of health states and treatment-effect duration. No studies used a systematic review to obtain utilities. Most studies took a healthcare perspective (21/26) and sourced resource use from key trials instead of local data. Overall, reporting quality was suboptimal.

CONCLUSIONS

This review reveals methodological changes over time, but reporting weaknesses remain, particularly with respect to transparency of model reporting.

A novel cost-effectiveness model of prescription eicosapentaenoic acid extrapolated to secondary prevention of cardiovascular diseases in the United States

OBJECTIVE

Given the substantial economic and health burden of cardiovascular disease and the residual cardiovascular risk that remains despite statin therapy, adjunctive therapies are needed. The purpose of this model was to estimate the cost-effectiveness of high-purity prescription eicosapentaenoic acid (EPA) omega-3 fatty acid intervention in secondary prevention of cardiovascular diseases in statin-treated patient populations extrapolated to the US.

METHODS

The deterministic model utilized inputs for cardiovascular events, costs, and utilities from published sources. Expert opinion was used when assumptions were required. The model takes the perspective of a US commercial, third-party payer with costs presented in 2014 US dollars. The model extends to 5 years and applies a 3% discount rate to costs and benefits. Sensitivity analyses were conducted to explore the influence of various input parameters on costs and outcomes.

RESULTS

Using base case parameters, EPA-plus-statin therapy compared with statin monotherapy resulted in cost savings (total 5-year costs $29,393 vs $30,587 per person, respectively) and improved utilities (average 3.627 vs 3.575, respectively). The results were not sensitive to multiple variations in model inputs and consistently identified EPA-plus-statin therapy to be the economically dominant strategy, with both lower costs and better patient utilities over the modeled 5-year period.

LIMITATIONS

The model is only an approximation of reality and does not capture all complexities of a real-world scenario without further inputs from ongoing trials. The model may under-estimate the cost-effectiveness of EPA-plus-statin therapy because it allows only a single event per patient.

CONCLUSION

This novel model suggests that combining EPA with statin therapy for secondary prevention of cardiovascular disease in the US may be a cost-saving and more compelling intervention than statin monotherapy.

Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment

Background

Individualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke.

Methods

A decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data.

Results

The model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA.

Conclusions

We have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent.

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

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

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.

Estimating the payoffs from cardiovascular disease research in Canada: an economic analysis

BACKGROUND

Investments in medical research can result in health improvements, reductions in health expenditures and secondary economic benefits. These "returns" have not been quantified in Canada. Our objective was to estimate the return on cardiovascular disease research funded by public or charitable organizations.

METHODS

Our primary outcome was the internal rate of return on cardiovascular disease research funded by public or charitable sources. The internal rate of return is the annual monetary benefit to the economy for each dollar invested in cardiovascular disease research. Calculation of the internal rate of return involved the following: measuring expenditures on cardiovascular disease research, estimating the health gains accrued from new treatments for cardiovascular disease, determining the proportion of health gains attributable to cardiovascular disease research and the time lag between research expenditures and health gains, and estimating the spillovers from public- or charitable-sector investments to other sectors of the economy.

RESULTS

Expenditures by public or charitable organizations on cardiovascular disease research from 1981 to 1992 amounted to $392 million (2005 dollars). Health gains associated with new treatments from 1994 to 2005 (13-yr lag) amounted to 2.2 million quality-adjusted life-years. We calculated an internal rate of return of 20.6%.

CONCLUSION

Canadians obtain relatively high health and economic gains from investments in cardiovascular disease research. Every $1 invested in cardiovascular disease research by public or charitable sources yields a stream of benefits of roughly $0.21 to the Canadian economy per year, in perpetuity.

Do different methods of modeling statin treatment effectiveness influence the optimal decision?

PURPOSE

Modeling studies that evaluate statin treatment for the prevention of cardiovascular disease (CVD) use different methods to model the effect of statins. The aim of this study was to evaluate the impact of using different modeling methods on the optimal decision found in such studies.

METHODS

We used a previously developed and validated Monte Carlo-Markov model based on the Rotterdam study (RISC model). The RISC model simulates coronary heart disease (CHD), stroke, cardiovascular death, and death due to other causes. Transition probabilities were based on 5-year risks predicted by Cox regression equations, including (among others) total and high-density lipoprotein (HDL) cholesterol as covariates. In a cost-effectiveness analysis of implementing the ATP-III guidelines, we evaluated the impact of using 3 different modeling methods of statin effectiveness: 1) through lipid level modification: statins lower total cholesterol and increase HDL cholesterol, which through the covariates in the Cox regression equations leads to a lower incidence of CHD and stroke events; 2) fixed risk reduction of CVD events: statins decrease the odds of CHD and stroke with an associated odds ratio that is assumed to be the same for each individual; 3) risk reduction of CVD events proportional to individual change in low-density lipoprotein (LDL) cholesterol: the relative risk reduction with statin therapy on the incidence of CHD and stroke was assumed to be proportional to the absolute reduction in LDL cholesterol levels for each individual. The probability that the ATP-III strategy was cost-effective, compared to usual care as observed in the Rotterdam study, was calculated for each of the 3 modeling methods for varying willingness-to-pay thresholds.

RESULTS

Incremental cost-effectiveness ratios for the ATP-III strategy compared with the reference strategy were €56,642/quality-adjusted life year (QALY), €21,369/QALY, and €22,131/QALY for modeling methods 1, 2, and 3, respectively. At a willingness-to-pay threshold of €50,000/QALY, the probability that the ATP-III strategy was cost-effective was about 40% for modeling method 1 and more than 90% for both methods 2 and 3. Differences in results between the modeling methods were sensitive to both the time horizon modeled and age distribution of the target

POPULATION

CONCLUSIONS

Modeling the effect of statins on CVD through the modification of lipid levels produced different results and associated uncertainty than modeling it directly through a risk reduction of events. This was partly attributable to the modeled effect of cholesterol on the incidence of stroke.

[Secondary prevention of stroke according to PRoFESS and SPARCL]

Stroke is still the leading cause of long-term major disability in developed countries, although several improvements have been achieved in acute stroke therapy (e.g. thrombolysis, stroke-unit treatment,etc.). Therefore, an effective secondary prevention - guided by the etiology and risk factors of stroke - is of particular importance to reduce stroke morbidity. Recently, two important studies (SPARCL and PRoFESS) analyzed the significance of statin therapy as well as the efficacy of different antiplatelet therapies for secondary stroke prevention. In this review we summarize the results and discuss the implications of these findings - including the most recent data - for stroke prevention.