Use In Cost-effectiveness Models Research Articles

Comparative effectiveness and cost-effectiveness of cardioprotective glucose-lowering therapies for type 2 diabetes in Brazil: a Bayesian network model

BackgroundThe escalating prevalence of type 2 diabetes (T2DM) poses an unparalleled economic catastrophe to developing countries. Cardiovascular diseases remain the primary source of costs among individuals with T2DM, incurring expenses for medications, hospitalizations, and surgical interventions. Compelling evidence suggests that the risk of cardiovascular outcomes can be reduced by three classes of glucose-lowering therapies (GLT), including SGLT2i, GLP-1A, and pioglitazone. However, an evidence-based and cost-effective protocol is still unavailable for many countries. The objective of the current study is to compare the effectiveness and cost-effectiveness of GLT in individuals with T2DM in Brazil.MethodsWe employed Bayesian Networks to calculate the incremental cost-effectiveness ratios (ICER), expressed in international dollars (Int$) per disease-adjusted life years [DALYs] averted. To determine the effectiveness of GLT, we conducted a systematic review with network meta-analysis (NMA) to provide insights for our model. Additionally, we obtained cardiovascular outcome incidence data from two real-world cohorts comprising 851 and 1337 patients in primary and secondary prevention, respectively. Our cost analysis took into account the perspective of the Brazilian public health system, and all values were converted to Int$.ResultsIn the NMA, SGLT2i [HR: 0.81 (95% CI 0.69–0.96)], GLP-1A [HR: 0.79 (95% CI 0.67–0.94)], and pioglitazone [HR: 0.73 (95% CI 0.59–0.91)] demonstrated reduced relative risks of non-fatal cardiovascular events. In the context of primary prevention, pioglitazone yielded 0.2339 DALYs averted, with an ICER of Int$7,082 (95% CI 4,521–10,770) per DALY averted when compared to standard care. SGLT2i and GLP-1A also increased effectiveness, resulting in 0.261 and 0.259 DALYs averted, respectively, but with higher ICERs of Int$12,061 (95% CI: 7,227–18,121) and Int$29,119 (95% CI: 23,811–35,367) per DALY averted. In the secondary prevention scenario, all three classes of treatments were deemed cost-effective at a maximum willingness-to-pay threshold of Int$26,700. Notably, pioglitazone consistently exhibited the highest probability of being cost-effective in both scenarios.ConclusionsIn Brazil, pioglitazone presented a higher probability of being cost-effective both in primary and secondary prevention, followed by SGLT2i and GLP-1A. Our findings support the use of cost-effectiveness models to build optimized and hierarchical therapeutic strategy in the management of T2DM.Trial registrationCRD42020194415.

POSB283 Approaches for Cost-Effectiveness Analyses in Adjuvant Oncology Indications and Their Acceptability By HTA Authorities

HTA submissions for adjuvant oncology indications typically occur before mature overall survival data are available, and therefore rely on intermediate surrogate endpoints as the primary measure of efficacy. A key challenge for HTA submissions is to estimate costs and benefits for patients beyond trial follow-up period, for use in cost-effectiveness models (CEM). This study assesses the methods used in CEMs for adjuvant oncology indications, including the approaches employed for long-term projections, and their acceptance by HTA authorities.

EQ-5D-Derived Health State Utility Values in Hematologic Malignancies: A Catalog of 796 Utilities Based on a Systematic Review.

We performed a systematic review of health state utility values (HSUVs) obtained using the EQ-5D questionnaire for patients with hematologic malignancies. The following databases were searched up to September 2018: MEDLINE, EMBASE, The Cochrane Library, and the EQ-5D publications database on the EuroQol website. Additional references were extracted from reviewed articles. Only studies presenting EQ-Index results were incorporated. In view of the heterogeneity across the included publications, we limited ourselves to a narrative synthesis of original HSUVs found. Fifty-nine studies (described in 63 articles) met the inclusion criteria. Data from 21 635 respondents provided 796 HSUV estimates for hematologic malignancy patients. EQ-Index scores ranged from -0.025 to 0.980. The most represented area was multiple myeloma (4 studies, 11 112 patients, and 249 HSUVs). In clinical areas such as chronic myeloid leukemia, acute myeloid leukemia, chronic lymphocytic leukemia, non-Hodgkin lymphoma, and mantle cell lymphoma, we described over 50 health utilities in each. In contrast, we identified only 13 HSUVs (based on 4 studies and the data of 166 patients) for Hodgkin lymphoma. Areas without EQ-5D-based health utilities comprised: polycythemia vera, primary myelofibrosis, essential thrombocythemia, mastocytosis, myeloid sarcoma, chronic myelomonocytic, eosinophilic leukemia, and neutrophilic leukemia. There is a wide range of HSUVs available for hematologic cancer patients with different indications. The review provides a catalog of utility values for use in cost-effectiveness models for hematologic malignancies.

PCN371 A REVIEW OF SWEDISH HEALTH TECHNOLOGY ASSESSMENTS BASED ON INDIRECT COMPARISONS OF HOSPITAL DRUGS IN ONCOLOGY

Randomized controlled trials (RCTs) with head-to-head comparisons remain the gold standard for demonstrating relative treatment efficacy and for use in cost-effectiveness models (CEMs), which serve as an important part of health technology assessment (HTA) submissions in Sweden. However, such comparative evidence is not always available. Recent Swedish HTA submissions were thus reviewed to understand the acceptability of different types of comparative evidence with a focus on oncology. HTAs of hospital drugs subject to national managed introduction by the New Therapy (NT) Council in Sweden were identified. The methodology used in the accompanying CEMs and the NT Council decisions were assessed. The Dental and Pharmaceutical Benefits Agency (TLV) website was searched for published HTAs of hospital drugs in oncology between January 2015 and April 2019. HTAs in which CEMs included comparative efficacy estimates other than head-to-head clinical trials were reviewed, including the method(s) used, the corresponding comments on the method(s) and the NT Council recommendations associated with the HTA. Of the 32 HTAs identified, 23 were based on direct treatment comparisons, while nine were based on indirect comparisons. Of these, three utilized a network meta-analysis, with one positive, one restricted and one negative recommendation. The other six used alternative methods (e.g. matching-adjusted indirect comparison, naïve indirect comparison). Of these, four recommendations were positive, one was restricted, and one was negative. Of the 23 HTAs that were based on direct comparisons, 13 of the recommendations were positive, four were restricted, five were negative and one had no published recommendation. Although HTA evidence based on indirect comparisons are generally considered by TLV as having a high level of uncertainty, some still result in positive recommendations. In general, products with cost per quality-adjusted life year below one million SEK per TLV assessment were likely to receive positive recommendations.

Identification, Review, and Use of Health State Utilities in Cost-Effectiveness Models: An ISPOR Good Practices for Outcomes Research Task Force Report

Cost-effectiveness models that present results in terms of cost per quality-adjusted life-year for health technologies are used to inform policy decisions in many parts of the world. Health state utilities (HSUs) are required to calculate the quality-adjusted life-years. Even when clinical studies assessing the effectiveness of health technologies collect data on HSUs to populate a cost-effectiveness model, which rarely happens, analysts typically need to identify at least some additional HSUs from alternative sources. When possible, HSUs are identified by a systematic review of the literature, but, again, this rarely happens. In 2014, ISPOR established a Good Practices for Outcome Research Task Force to address the use of HSUs in cost-effectiveness models. This task force report provides recommendations for researchers who identify, review, and synthesize HSUs for use in cost-effectiveness models; analysts who use the results in models; and reviewers who critically appraise the suitability and validity of the HSUs selected for use in models. The associated Minimum Reporting Standards of Systematic Review of Utilities for Cost-Effectiveness checklist created by the task force provides criteria to judge the appropriateness of the HSUs selected for use in cost-effectiveness models and is suitable for use in different international settings.

A Review of Generic Preference-Based Measures for Use in Cost-Effectiveness Models

Generic preference-based measures (GPBMs) of health are used to obtain the quality adjustment weight required to calculate the quality-adjusted life year in health economic models. GPBMs have been developed to use across different interventions and medical conditions and typically consist of a self-complete patient questionnaire, a health state classification system, and preference weights for all states defined by the classification system. Of the six main GPBMs, the three most frequently used are the Health Utilities Index version 3, the EuroQol 5 dimensions (3 and 5 levels), and the Short Form 6 dimensions. There are considerable differences in GPBMs in terms of the content and size of descriptive systems (i.e. the numbers of dimensions of health and levels of severity within these), the methods of valuation [e.g. time trade-off (TTO), standard gamble (SG)], and the populations (e.g. general population, patients) used to value the health states within the descriptive systems. Although GPBMs are anchored at 1 (full health) and 0 (dead), they produce different health state utility values when completed by the same patient. Considerations when selecting a measure for use in a clinical trial include practicality, reliability, validity and responsiveness. Requirements of reimbursement agencies may impose additional restrictions on suitable measures for use in economic evaluations, such as the valuation technique (TTO, SG) or the source of values (general public vs. patients).

Accounting for Heterogeneity in Relative Treatment Effects for Use in Cost-Effectiveness Models and Value-of-Information Analyses.

Cost-effectiveness analysis (CEA) models are routinely used to inform health care policy. Key model inputs include relative effectiveness of competing treatments, typically informed by meta-analysis. Heterogeneity is ubiquitous in meta-analysis, and random effects models are usually used when there is variability in effects across studies. In the absence of observed treatment effect modifiers, various summaries from the random effects distribution (random effects mean, predictive distribution, random effects distribution, or study-specific estimate [shrunken or independent of other studies]) can be used depending on the relationship between the setting for the decision (population characteristics, treatment definitions, and other contextual factors) and the included studies. If covariates have been measured that could potentially explain the heterogeneity, then these can be included in a meta-regression model. We describe how covariates can be included in a network meta-analysis model and how the output from such an analysis can be used in a CEA model. We outline a model selection procedure to help choose between competing models and stress the importance of clinical input. We illustrate the approach with a health technology assessment of intravenous immunoglobulin for the management of adult patients with severe sepsis in an intensive care setting, which exemplifies how risk of bias information can be incorporated into CEA models. We show that the results of the CEA and value-of-information analyses are sensitive to the model and highlight the importance of sensitivity analyses when conducting CEA in the presence of heterogeneity. The methods presented extend naturally to heterogeneity in other model inputs, such as baseline risk.

Human papillomavirus vaccine introduction in low-income and middle-income countries: guidance on the use of cost-effectiveness models

BackgroundThe World Health Organization (WHO) recommends that the cost effectiveness of introducing human papillomavirus (HPV) vaccination is considered before such a strategy is implemented. However, developing countries often lack the technical capacity to perform and interpret results of economic appraisals of vaccines. To provide information about the feasibility of using such models in a developing country setting, we evaluated models of HPV vaccination in terms of their capacity, requirements, limitations and comparability.MethodsA literature review identified six HPV vaccination models suitable for low-income and middle-income country use and representative of the literature in terms of provenance and model structure. Each model was adapted by its developers using standardised data sets representative of two hypothetical developing countries (a low-income country with no screening and a middle-income country with limited screening). Model predictions before and after vaccination of adolescent girls were compared in terms of HPV prevalence and cervical cancer incidence, as was the incremental cost-effectiveness ratio of vaccination under different scenarios.ResultsNone of the models perfectly reproduced the standardised data set provided to the model developers. However, they agreed that large decreases in type 16/18 HPV prevalence and cervical cancer incidence are likely to occur following vaccination. Apart from the Thai model (in which vaccine and non-vaccine HPV types were combined), vaccine-type HPV prevalence dropped by 75% to 100%, and vaccine-type cervical cancer incidence dropped by 80% to 100% across the models (averaging over age groups). The most influential factors affecting cost effectiveness were the discount rate, duration of vaccine protection, vaccine price and HPV prevalence. Demographic change, access to treatment and data resolution were found to be key issues to consider for models in developing countries.ConclusionsThe results indicated the usefulness of considering results from several models and sets of modelling assumptions in decision making. Modelling groups were prepared to share their models and expertise to work with stakeholders in developing countries.Please see related article: http://www.biomedcentral.com/1741-7007/9/55

A review of the methods used to estimate the cost of Alzheimer's disease in the United States.

BACKGROUN/RATIONALE: To determine the suitability of published estimates of the US cost of Alzheimer's disease (AD) for use in cost-effectiveness models for new AD treatments. A systematic literature review of published information on direct medical, direct nonmedical, indirect, and informal care costs for different levels of disease severity. Nineteen studies were included in the review. In studies presenting mean costs by disease severity, the change in different types of costs with increasing disease severity varied, depending on the data sources and characteristics of patients with AD. In studies presenting the results of regression analyses, costs were shown to be independently associated with cognition, functional status, behavioral symptoms, and dependence. Published US studies (1) did not include all the types of costs and AD populations, and (2) generally did not include all the measures of disease severity that are needed for cost-effectiveness models.

Estimating Quality-Adjusted Life Year Losses Associated with Visual Field Deficits Using Methodological Approaches

Purpose: To develop a function to describe quality-adjusted life year (QALY) losses associated with visual field losses for use in cost-effectiveness models. Methods: We estimated the patient visual field defect in decibels (dBs) that corresponded to visual acuity values of 20/40, 20/70, 20/200 (US blindness), 20/400 (WHO blindness), and 20/1,000 (extreme impairment) using two methods. The first method categorized visual field defects by applying the logic of the Snellen minimum angle of resolution (MAR) acuity scale to the ability to perceive luminance adjusting for the differences in the visual acuity and luminance scales (MAR-based). The second method categorized visual field defects based on assumptions. We then assigned each visual field category the same QALY value associated with the analogous visual acuity category. Finally, for each method, we calculated a function that described QALY losses as a function of the absolute value of patient mean deviation (MD) values measured in dBs. Results: The MAR-based method estimated that MDs of –16, –18, –23, –24, and –30 or greater dBs corresponded to visual acuity values of 20/40, 20/70, 20/200, 20/400, and 20/1,000, respectively. For the same visual acuity values, the assumption-based method resulted in MD estimates of –13, –17, –22, –24, and –30. Our MAR-based method yielded the function, QALYs = 0.98991 + 0.0022 · dBs –0.00080518 · dBs, setting dBs equal to the absolute value of the mean deviation in the better-seeing eye. Our assumption-based method yielded the piece-wise function, QALYs = 1.0 – 0.0155 · dBs for dBs < 22, and 1.815 –0.0525· dBs for dBs ≥ 22. Conclusions: Results from two estimation methods suggest a convex and downward sloping relationship between dBs of visual field loss and QALY decrements. QALY losses accumulate slowly at first and then increase at an accelerating rate following an MD of approximately –10 dBs. This relationship is consistent with recent empirical findings reporting minimal QALY losses associated with small visual field defects.

Cost-effectiveness as an outcome in randomized clinical trials

Economic outcomes are now included in many contemporary randomized trials and provide an additional dimension to the assessment of interventions. Economic data collection and analysis pose several methodologic challenges, however. This paper reviews methods of incorporating economic outcomes in clinical trials. Data on medical resource utilization and cost can readily be collected along with data on clinical outcomes. The cost of planned interventions can be measured with reasonable accuracy, but costs due to unplanned clinical events are more difficult to measure reliably. The total cost depends critically on these relatively infrequent, yet costly, adverse outcomes, which may partially, or even completely, offset any difference between the planned costs of the randomized therapies. Newer therapies are typically more expensive than older therapies, so the most important question is whether patient outcomes are improved sufficiently to justify the added expense. Cost-effectiveness analysis helps gauge the value provided by a new therapy. The cost-effectiveness of an intervention compared with an alternative is defined as the ratio of the incremental costs and the incremental clinical benefits, measured as dollars per quality-adjusted life-year added. The follow-up period in most clinical trials is generally long enough to measure the added cost of therapy, but may not capture the full benefits of treatment. The limited time horizon of clinical trials makes it necessary to use a model to extrapolate the observed effect of treatment and project the increase in life expectancy. The resulting cost-effectiveness ratio is sensitive to assumptions about the long-term efficacy of treatment, particularly whether the treatment effect will continue or dissipate over time. Economic outcomes can be measured alongside clinical outcomes in randomized trial. While the use of cost-effectiveness models falls outside the strictly empirical, within-trial analysis framework that is embraced by most clinical trialists, it provides an explicit approach to assessing whether the intervention under study provides a clinically meaningful improvement in outcome that is worthwhile.

Testing the validity of cost-effectiveness models.

A growing body of recent work has identified several problems with economic evaluations undertaken alongside controlled trials that can have potentially serious impacts on the ability of decision makers to draw valid conclusions. At the same time, the use of cost-effectiveness models has been drawn into question, due to the alleged arbitrary nature of their construction. This has led researchers to try and identify ways of improving the quality of cost-effectiveness models through identifying 'best practice', producing guidelines for peer review and identifying tests of validity. This paper investigates the issue of testing the validity of cost-effectiveness models or, perhaps more appropriately, whether it is possible to objectively measure the quality of a cost-effectiveness model. A review of the literature shows that there is much confusion over the different aspects of modelling that should be assessed in respect to model quality, and how this should be done. We develop a framework for assessing model quality in terms of: (i) the structure of the model; (ii) the inputs of the model; (iii) the results of the model; and (iv) the value of the model to the decision maker. Quality assessment is investigated within this framework, and it is argued that it is doubtful that a set of objective tests of validity will ever be produced, or indeed that such an approach would be desirable. The lack of any clearly definable and objective tests of validity means that the other parts of the evaluation process need to be given greater emphasis. Quality assurance forms a small part of a broader process and is best implemented in the form of good practice guidelines. A set of key guidelines are presented.

Role of economic models in randomized clinical trials

Cost-effectiveness analysis (CEA) has a relatively short history in health and medical care, reflecting the fact that costs have only recently been considered an important factor in medical decisions. Not long ago, the only relevant question was whether a particular therapy worked (that is, whether it was effective); now, once a therapy has been shown to be effective, the next question is whether it provides sufficient value to be worthwhile (that is, whether it is cost-effective). It is this second question that CEA attempts to address, quantitatively and objectively, by measuring all the relevant financial costs and all the relevant health effects of a therapy, then weighing these results explicitly in a cost-effectiveness ratio. The pioneering studies that introduced CEA into the medical field applied mathematical models to data about the costs and outcomes of various therapies, data drawn from the literature; the models and data were combined to calculate the cost-effectiveness of the therapies.1-4 These early studies were limited by several factors.The synthesis of information from a wide array of data sources necessarily injected a degree of investigator judgment into the assessment of cost-effectiveness. Then, too, data on costs and data on outcomes were usually drawn from different sources, thus raising questions about the comparability and consistency of that data. Also, the reporting of data in the literature was often sketchy and incomplete, especially when compared with the very specific requirements of the economic model being applied; the investigator was therefore forced to exercise his or her judgment to fill in the gaps left by the literature.The models used to calculate costeffectiveness were often complicated and therefore difficult to describe completely in a journal article, which raised concerns that investigator bias might have crept into the evaluation process before the results were published. Cost-effectiveness models were thus regarded as less objective and less reliable than other sources of information.5 In the meantime, randomized clinical trials began to measure the costs of therapies in addition to determining standard clinical outcomes.6-8 Investigators could now evaluate both cost and effectiveness outcomes in the same population of patients, with the protection provided by randomization that treatment allocation bias had been controlled.As a result of the meticulous planning needed for randomized trials, all data relevant to the CEA could be collected prospectively and in sufficient detail to provide the basis for a complete analysis. The strengths of randomization, prospective data collection, and objective measurement of outcomes represented a major advance in CEA.These factors also seemed to provide an answer to critics concerned about the issue of investigator subjectivity that was associated with the use of economic models.With the advent of real data, it was surmised, perhaps the need for models in CEA would no longer exist. Including cost measurement in randomized trials has indeed brought about a major advance in the field of CEA in medicine.The data that have resulted, however, have limitations that suggest that mathematical models will continue to be important in medical cost-effectiveness studies. Some of these limitations relate specifically to the techniques of cost-effectiveness studies.They involve such methodological issues as the projecting of study results beyond the end of observed follow-up, the placing of confidence intervals on cost-effectiveness ratios, and the manner by which cost-effectiveness studies may fail to account sufficiently for the effects on the cost-effectiveness ratio of variations in practice patterns and patient responses to therapy. Other potential limitations of cost-effectiveness studies in randomized trials relate to both clinical and economic outcomes.They involve questions about the generalizability of trial results to broader patient populations, about the distinction between the efficacy of a therapy in ideal settings and its effectiveness when applied in routine clinical practice, and about accounting for the variable of the “moving target”of changing medical technology. Each of these limitations can be addressed by the use of costeffectiveness models based on the observations from randomized trials.The remainder of this article will discuss each of these issues in turn, then return to the larger question about the future of models for CEA.