Framework For Cost-effectiveness Analysis Research Articles

Evaluating Policies of Expanding Versus Restricting First-Line Treatment Choices: A Cost-Effectiveness Analysis Framework

ObjectivesHealthcare payers often implement coverage policies that restrict the utilization of costly new first-line treatments. Cost-effectiveness analysis can be conducted to inform these decisions by comparing the new treatment with an existing one. However, this approach may overlook important factors such as treatment effect heterogeneity and endogenous treatment selection, policy implementation costs, and diverse patient preferences across multiple treatment options. We aimed to develop a cost-effectiveness analysis framework that considers these real-world factors, facilitating the evaluation of alternative policies related to expanding or restricting first-line treatment choices. MethodsWe introduced a metric of incremental cost-effectiveness ratio (ICER) that compares an expanded choice set (CS) including the new first-line treatment with a restricted CS excluding the new treatment. ICER(CS) accounts for treatment selection influenced by heterogeneous treatment effects and policy implementation costs. We examined a basic scenario with 2 standard first-line treatment choices and a more realistic scenario involving diverse preferences toward multiple choices. To illustrate the framework, we conducted a retrospective evaluation of including versus excluding abiraterone acetate plus prednisone (AAP) (androgen deprivation therapy [ADT] + AAP) as a first-line treatment for metastatic hormone-sensitive prostate cancer. ResultsThe traditional ICERs for ADT + AAP versus ADT alone and ADT+ docetaxel were $104 269 and $206 324/quality-adjusted life-year, respectively. The ICER(CS) for comparing an expanded CS with ADT + AAP with a restricted CS without ADT + AAP was $123 179/quality-adjusted life-year. ConclusionsThe proposed framework provides decision makers with policy-relevant tools, enabling them to assess the cost-effectiveness of alternative policies of expanding versus restricting patients’ and physicians’ first-line treatment choices.

The Broader Opportunity Costs in the Broader Cost-Effectiveness Analysis Framework.

The traditional cost-effectiveness analysis framework usually takes a healthcare system perspective, where the aim is to maximise population health from a fixed budget allocated to healthcare. Extensions to this framework have been suggested, including: (i) incorporating impacts that fall outside the healthcare sector; (ii) accounting for outcomes beyond health; and (iii) assessing equity considerations. Several alternatives have been proposed that serve these purposes, for example, the extended "impact inventory", the "beyond-the-QALY" approach and the distributional cost-effectiveness analysis. This paper aims to develop a comprehensive framework that incorporates into the cost-effectiveness analysis framework some of its most advocated extensions and provides a means of arriving at a unidimensional cost-effectiveness analysis result measure. Building on previous work, I proposed a framework that explicitly incorporates the full extent of the opportunity costs that arise when new dimensions and distributional concerns are included in cost-effectiveness analyses. A hypothetical example is provided as a way of illustration. Operationalising the proposed framework requires system-wide representative values and/or robust estimates concerning: (i) selecting dimensions; (ii) measuring opportunity costs associated with each dimension; (iii) quantifying equity weights and percentages of beneficiaries and losers meeting equity considerations; and (iv) attaching monetary values to dimensions measured using a non-monetary metric. Extending the cost-effectiveness analysis framework entails extending the measurement of the opportunity costs of funding decisions. This implies populating an ambitious puzzle that in some cases poses fundamental conceptual and empirical questions. Potential routes of further research that might facilitate such undertaking are proposed.

A Pragmatic Guide to Assessing Real Option Value for Medical Technologies

ObjectivesThis study aimed to provide recommendations for identifying and implementing real option value (ROV) calculations in value assessment. MethodsWe identified the primary mechanisms through which ROV can be created based on a theoretical framework for ROV, assessed approaches for predicting future innovations and improvements in health, and described the steps for estimating ROV in a cost-effectiveness analysis framework. ResultsThe 3 primary mechanisms by which ROV can be created are when a current treatment (1) prolongs survival to increase the proportion of patients who can receive future innovations, (2) slows disease progression to increase patients’ eligibility for future innovations, and (3) directly affects the efficacy of future innovations. We provide 5 recommendations for implementing ROV in value assessment. First, the decision to quantify ROV should be based on a qualitative evaluation of whether the treatment can enable greater benefits from future innovations. Second, ROV should be quantified in the same value assessment framework (eg, cost-effectiveness analysis using quality-adjusted life-year) as the conventional value. Third, method for quantifying ROV should consider data availability, rate of innovation, and sources of future health improvements. Fourth, ROV estimate should be presented alongside the conventional value as a separate element due to its inherently large uncertainty. Finally, generalizability of ROV estimate should be evaluated, and local data should be used when available. ConclusionsROV can arise from a variety of mechanisms that should be considered before investing in an ROV analysis. Calculating ROV includes exploring different approaches for forecasting future innovations and future improvements in health.

The economic case for precision medicine

ABSTRACTIntroduction: The advancement of precision medicine into routine clinical practice has been highlighted as an agenda for national and international health care policy. A principle barrier to this advancement is in meeting requirements of the payer or reimbursement agency for health care. This special report aims to explain the economic case for precision medicine, by accounting for the explicit objectives defined by decision-makers responsible for the allocation of limited health care resources.Areas covered: The framework of cost-effectiveness analysis, a method of economic evaluation, is used to describe how precision medicine can, in theory, exploit identifiable patient-level heterogeneity to improve population health outcomes and the relative cost-effectiveness of health care. Four case studies are used to illustrate potential challenges when demonstrating the economic case for a precision medicine in practice.Expert commentary: The economic case for a precision medicine should be considered at an early stage during its research and development phase. Clinical and economic evidence can be generated iteratively and should be in alignment with the objectives and requirements of decision-makers. Programmes of further research, to demonstrate the economic case of a precision medicine, can be prioritized by the extent that they reduce the uncertainty expressed by decision-makers.

Combining Time-Driven Activity-Based Costing with Clinical Outcome in Cost-Effectiveness Analysis to Measure Value in Treatment of Depression.

BackgroundA major challenge of mental health care is to provide safe and effective treatment with limited resources. The main purpose of this study was to examine a value-based approach in clinical psychiatry when evaluating a process improvement initiative. This was accomplished by using the relatively new time driven activity based costing (TDABC) method within the more widely adopted cost-effectiveness analysis framework for economic evaluation of healthcare technologies. The objective was to evaluate the cost-effectiveness of allowing psychologists to perform post-treatment assessment previously performed by psychiatrists at an outpatient clinic treating depression using internet-based cognitive-behavioral therapy (ICBT).MethodsData was collected from 568 adult patients treated with ICBT for depression during 2013–2014. The TDABC methodology was used to estimate total healthcare costs, including development of process maps for the complete cycle of care and estimation of resource use and minute costs of staff, hospital space and materials based on their relative proportions used. Clinical outcomes were measured using the Patient Health Questionnaire depression scale (PHQ-9) before and after treatment and at 6-month follow-up. Cost-effectiveness analyses (CEA) was performed and the results presented as incremental net benefits (INB), cost-effectiveness acceptability curves (CEACs) and confidence ellipses to demonstrate uncertainty around the value of the organizational intervention.OutcomesTaking into account the complete healthcare process (from referral to follow-up assessment), treatment costs decreased from $709 (SD = $130) per patient in 2013 to $659 (SD = $134) in 2014 while treatment effectiveness was maintained; 27% had achieved full remission from depression after treatment (PHQ-9 < 5) during both 2013 and 2014 and an additional 35% and 33% had achieved partial remission in 2013 and 2014, respectively. At follow-up, 42% were in full remission after treatment during both 2013 and 2014; an additional 35% and 33% were in partial remission during 2013 and 2014, respectively. Confidence ellipses occupied the south-east (SE) and south-west (SW) quadrants of the incremental cost-effectiveness plane at both post-treatment and at follow-up, indicating that the ICBT treatment was less costly and equally effective after staff reallocation.ConclusionTreating patients to the target of full remission using psychologists instead of medical specialists for post-treatment assessment is cost-saving and consequently a more valuable use of limited resources. TDABC may be a useful tool for measuring resource costs, identifying quality improvement opportunities and evaluating the consequences of such initiatives. Combining TDABC with clinical outcome measures in CEA is potentially a useful approach in mental healthcare to estimate the value of process improvement initiatives.

Spatially-Distributed Cost-Effectiveness Analysis Framework to Control Phosphorus from Agricultural Diffuse Pollution.

Best management practices (BMPs) for agricultural diffuse pollution control are implemented at the field or small-watershed scale. However, the benefits of BMP implementation on receiving water quality at multiple spatial is an ongoing challenge. In this paper, we introduce an integrated approach that combines risk assessment (i.e., Phosphorus (P) index), model simulation techniques (Hydrological Simulation Program–FORTRAN), and a BMP placement tool at various scales to identify the optimal location for implementing multiple BMPs and estimate BMP effectiveness after implementation. A statistically significant decrease in nutrient discharge from watersheds is proposed to evaluate the effectiveness of BMPs, strategically targeted within watersheds. Specifically, we estimate two types of cost-effectiveness curves (total pollution reduction and proportion of watersheds improved) for four allocation approaches. Selection of a ‘‘best approach” depends on the relative importance of the two types of effectiveness, which involves a value judgment based on the random/aggregated degree of BMP distribution among and within sub-watersheds. A statistical optimization framework is developed and evaluated in Chaohe River Watershed located in the northern mountain area of Beijing. Results show that BMP implementation significantly (p >0.001) decrease P loss from the watershed. Remedial strategies where BMPs were targeted to areas of high risk of P loss, deceased P loads compared with strategies where BMPs were randomly located across watersheds. Sensitivity analysis indicated that aggregated BMP placement in particular watershed is the most cost-effective scenario to decrease P loss. The optimization approach outlined in this paper is a spatially hierarchical method for targeting nonpoint source controls across a range of scales from field to farm, to watersheds, to regions. Further, model estimates showed targeting at multiple scales is necessary to optimize program efficiency. The integrated model approach described that selects and places BMPs at varying levels of implementation, provides a new theoretical basis and technical guidance for diffuse pollution management in agricultural watersheds.

The economic benefits of community health centers in lowering preventable hospitalizations: a cost-effectiveness analysis

Research has suggested that accessible primary healthcare may help control hospitalization for certain conditions that are often considered either preventable or treatable through primary healthcare. However, only limited research has tested this widely accepted hypothesis. We extend research on preventable hospitalization in the framework of cost-effectiveness analysis. Using hospitalization data from South Carolina, we show how access to primary healthcare may affect the volume and cost of hospitalizations for ambulatory care sensitive conditions. Our statistical models for effectiveness and cost take advantage of spatial methods to adjust for neighborhood characteristics. This adjustment also addresses correlations among variables describing each given area, providing appropriate results despite those correlations. We also use propensity scores to adjust for county level variation in access to primary healthcare. We observe that the mean differential volume and cost of lower extremity amputation associated with diabetes was significantly lower in areas served by community health centers than in other areas. This result provides support for the hypothesis that increasing access to primary healthcare may help to control the volume and cost of preventable hospitalization for some conditions.

Distributional cost-effectiveness analysis of health care programmes--a methodological case study of the UK Bowel Cancer Screening Programme.

This paper presents an application of a new methodological framework for undertaking distributional cost-effectiveness analysis to combine the objectives of maximising health and minimising unfair variation in health when evaluating population health interventions. The National Health Service bowel cancer screening programme introduced in 2006 is expected to improve population health on average and to worsen population health inequalities associated with deprivation and ethnicity--a classic case of 'intervention-generated inequality'. We demonstrate the distributional cost-effectiveness analysis framework by examining two redesign options for the bowel cancer screening programme: (i) the introduction of an enhanced targeted reminder aimed at increasing screening uptake in deprived and ethnically diverse neighbourhoods and (ii) the introduction of a basic universal reminder aimed at increasing screening uptake across the whole population. Our analysis indicates that the universal reminder is the strategy that maximises population health, while the targeted reminder is the screening strategy that minimises unfair variation in health. The framework is used to demonstrate how these two objectives can be traded off against each other, and how alternative social value judgements influence the assessment of which strategy is best, including judgements about which dimensions of health variation are considered unfair and judgements about societal levels of inequality aversion.

A spatially-distributed cost-effectiveness analysis framework for controlling water pollution

With the aim of comparing various agro-environmental measures to control pesticide pollution in surface waters, we propose a methodological framework for spatially-distributed cost-effectiveness analysis. We use the Soil and Water Assessment Tool (SWAT) model to assess the effectiveness of the measures applied, and we develop an aggregated bio-economic model using the General Algebraic Modelling System (GAMS) to evaluate the costs of their implementation. Finally, we propose a ranking of these measures based on their cost-effectiveness ratios. This approach is applied to the Gers river basin in southwest France. The findings clearly demonstrate that of all the measures we considered, grass strips and mechanical weeding are the most cost-effective in reducing pesticide-related water pollution. On the other hand, measures such as longer rotation sequences and catch crops, although relatively inexpensive to implement, fall short in terms of reducing pesticide pollution. Furthermore, we found that the cost and effectiveness of measures largely depended on where they were implemented, as the framework proposed takes account of physical variables and farm activities scattered within the watershed. Possible uses of this integrated framework as a communication tool in participatory river basin management are also discussed.

Integrating Economic and Biophysical Data in Assessing Cost-Effectiveness of Buffer Strip Placement

The European Union Water Framework Directive (WFD) requires Member States to set water quality objectives and identify cost-effective mitigation measures to achieve "good status" in all waters. However, costs and effectiveness of measures vary both within and between catchments, depending on factors such as land use and topography. The aim of this study was to develop a cost-effectiveness analysis framework for integrating estimates of phosphorus (P) losses from land-based sources, potential abatement using riparian buffers, and the economic implications of buffers. Estimates of field-by-field P exports and routing were based on crop risk and field slope classes. Buffer P trapping efficiencies were based on literature metadata analysis. Costs of placing buffers were based on foregone farm gross margins. An integrated optimization model of cost minimization was developed and solved for different P reduction targets to the Rescobie Loch catchment in eastern Scotland. A target mean annual P load reduction of 376 kg to the loch to achieve good status was identified. Assuming all the riparian fields initially have the 2-m buffer strip required by the General Binding Rules (part of the WFD in Scotland), the model gave good predictions of P loads (345-481 kg P). The modeling results show that riparian buffers alone cannot achieve the required P load reduction (up to 54% P can be removed). In the medium P input scenario, average costs vary from £38 to £176 kg P at 10% and 54% P reduction, respectively. The framework demonstrates a useful tool for exploring cost-effective targeting of environmental measures.

Cost Analysis Comparing Computed Tomographic Pulmonary Angiography (CTPA) and Ventilation-Perfusion (V/Q) Scanning for the Diagnosis of Pulmonary Embolism

AbstractAbstract 567 Background:Accurate diagnosis of a pulmonary embolism remains problematic due to the nonspecific findings of the clinical presentation and the limitations of radiographic imaging. Computed tomography pulmonary angiography (CTPA) has surpassed ventilation-perfusion (V/Q) scanning as the primary imaging modality in the investigation of patients with suspected pulmonary emboli due to its superior diagnostic accuracy. Data from a large randomized controlled trial has indicated that while strategies using both CTPA and V/Q are equally safe at excluding the diagnosis, CTPA detects significantly more pulmonary emboli (1). The purpose of this study was to perform a cost analysis comparing CTPA and V/Q scanning for the investigation of patients with suspected pulmonary emboli based upon this large trial. Methods:A cost analysis was performed using a decision-analysis model. The costs and outcomes of CTPA and V/Q scanning in detecting or ruling out pulmonary embolism over a 90 day analysis horizon were incorporated into a decision tree where the probabilities for each outcome were taken from (1) and a systematic literature review. The decision tree incorporated the thromboembolic and major bleeding complications associated with the diagnosis and treatment of pulmonary embolism in 100,000 patients. Outcomes in the model were measured in terms of quality-adjusted life years (QALYs). The economic model took a direct-payer perspective, considering direct costs to the health care system and patients. All costs were based on 2009 Canadian dollars. The primary economic evaluation was conducted within a deterministic cost-effectiveness analysis framework in terms of incremental cost per QALY. If the clinical benefits were found to be statistically insignificant the primary evaluation would be collapsed to a cost-minimization analysis and a secondary analysis would be performed to using probabilistic methods to estimate the expected incremental costs and outcomes based upon probability distributions around mean point estimates. Sensitivity analyses around key parameters were also performed. Results:The primary deterministic analysis collapsed to a cost-minimization analysis on the grounds that no statistically differences in the proportion of false negative results or mortality were observed in the randomized trial comparing CTPA with VQ scanning for the diagnosis of pulmonary embolism (1). The cost minimization analysis demonstrated a strategy using V/Q scanning as the primary imaging modality was less costly. CTPA was associated with an incremental cost of $11.3 million per 100,000 patients compared to V/Q scanning. CTPA was associated with an additional 3760 additional diagnoses of pulmonary embolism and 111 major bleeding episodes compared to V/Q scanning. The secondary probabilistic cost-effectiveness analysis revealed that CTPA was associated with an incremental cost of $4.8 million per 100,000-person cohort and 3134 QALYs gained relative to V/Q scanning for a cost-effectiveness of $1543 per QALY gained. Furthermore, the cost-effectiveness acceptability curve demonstrated CTPA had an 89% likelihood of being cost-effective relative to the threshold of $50,000 per QALY gained. Sensitivity analyses demonstrated cost-effectiveness ratio was most impacted by varying prevalence of pulmonary embolism and the relative and absolute differences in the proportions of fatal pulmonary embolism from falsely negative scans between the two tests. Conclusions:The results of the cost-minimization analysis imply that V/Q scanning is a less costly alternative to CTPA resulting from less pulmonary emboli diagnosed while maintaining similar 3-month rate of thromboembolic complications relative to CTPA. Secondary analysis, however, concluded that diagnostic algorithms incorporating CTPA may be cost-effective under defined circumstances.(1) Anderson et al. JAMA 2007; 298:2743-2753 Disclosures:No relevant conflicts of interest to declare.

Complementing information from incremental net benefit: a Bayesian perspective

Cost-effectiveness analysis of a treatment is typically based on specific functions of the expectation of the effectiveness and cost of the treatment, and treatment comparisons are made in the same vein. The mathematical expectation has been the cornerstone for defining the incremental cost-effectiveness ratio and the incremental net benefit, the most popular tools for cost-effectiveness analysis for pairwise treatment comparisons. In this paper we propose a framework for cost-effectiveness analysis based on the whole posterior distribution of effectiveness and cost. We adopt a Bayesian perspective and use the predictive posterior distribution of the net benefit. The analysis based on the whole posterior distribution captures the uncertainty about the value of effectiveness and cost of the treatment and overcomes some limitations presented by the mean, as a summary measure, when skewed data are considered. Furthermore, it allows us to compare more than two treatments. An illustration with real data is provided.

Cost–Effectiveness Analysis in Pharmacogenomics

The existence of finite healthcare budgets drives the need to consider opportunity cost and demonstrate that pharmacogenomic interventions offer added value, in terms of the relative costs and benefits, compared with current practice. This is where the framework of cost-effectiveness analysis is useful. Existing systematic reviews of economic evaluations of genetic technologies have all highlighted the need to improve the quality of the economics evidence base. More recent cost-effectiveness analyses of pharmacogenomics are generally of higher quality. The future will see an increase in the number of published cost-effectiveness analyses. Critical appraisal of these analyses is necessary to ensure the evidence base is sufficiently robust to inform resource allocation decisions at local and national levels.

Can the concept of ecosystem services be practically applied to improve natural resource management decisions?

Applying ecosystem service valuation principles to natural resources management has the potential to encourage the efficient use of resources, but can decision support systems built on these principles be made both practical and robust? The limitations to building such systems are the practical limits on managers' time to develop or learn tools and the state of the science to support decision-making components. We address this question by applying a cost-effectiveness analysis framework and optimization model to support the targeting of restoration funds to control an invasive grass (Bromus tectorum) in agro-ecosystems. The optimization aims to maximize benefits derived from a suite of ecosystem services that may be enhanced through site restoration. The model combines a spatially-varying cost function with ecosystem service benefit functions that are risk-adjusted to capture the probability of successful restoration. We demonstrate that our approach generates roughly three times the level of ecosystem service benefits (as measured through indicators) compared to the current management strategy of selecting restoration sites that are superlative producers of one ecosystem service. The results showed that spatial (GIS) data and ecosystem understanding were sufficient to formally capture the managers' informal decisions and that cost-effectiveness of restoration could be improved by considering the ability of sites to jointly produce multiple ecosystem services and adjusting expected benefits by the probability of success.

Complementing the net benefit approach: A new framework for Bayesian cost-effectiveness analysis

The aim of cost-effectiveness analysis is to maximize health benefits from a given budget, taking a societal perspective. Consequently, the comparison of alternative treatments or technologies is solely based on their expected effectiveness and cost. However, the expectation, or mean, poses important limitations as it might be a poor summary of the underlying distribution, for instance when the effectiveness is a categorical variable, or when the distributions of either effectiveness or cost present a high degree of asymmetry. Clinical variables often present these characteristics. In this study, we present a framework for cost-effectiveness analysis based on the whole posterior distribution of effectiveness and cost. An application with real data is included to illustrate the analysis. Decision-making measures such as the incremental cost-effectiveness ratio, incremental net-benefit, and cost-effectiveness acceptability curves, can also be defined under the new framework. This framework overcomes limitations of the mean and offers complementary information for the decision maker.

The Role of Patient Preferences in Cost-Effectiveness Analysis

This paper reviews the role of patient preferences within the framework of cost-effectiveness analysis (CEA). CEA typically adopts a system-wide perspective by focusing upon efficiency across groups in the allocation of scarce healthcare resources, whereas treatment decisions are made over individuals. However, patient preferences have been shown to have a direct impact on the outcome of an intervention via psychological factors or indirectly via patient adherence/compliance rates. Patient values may also be in conflict with the results of CEA through the valuation of benefits. CEA relies heavily on the QALY model to reflect individual preferences, although the healthy year equivalent offers an alternative measure that may be better at taking individual preferences into account. However, both measures typically use mean general population or mean patient values and therefore create conflict with individual-level preferences. For CEA to reflect practice, it must take into account the impact of individual patient preferences even where general population preferences are used to value the benefits of interventions. Patient preferences have implications for cost effectiveness through costs and outcomes, and it is important that cost-effectiveness models incorporate these through its structure (e.g. allowing for differing compliance rates) and parameter values, including clinical effectiveness. It will also be necessary to try to predict patient preferences in order to estimate any impact on cost effectiveness through analyses of revealed and stated preference data. It is recognized that policy makers are concerned with making interventions available to patients and not forcing them to consume healthcare. One way of moving towards this would be to adopt a two-part decision process: the identification of the most cost-effective therapy using mean general population values (i.e. the current rule), then also making available those treatments that are cheaper than the most cost-effective therapy.

Estimating production costs in the economic evaluation of health‐care programs

We propose a method for calculating the production costs of an intervention in a manner that accounts for differences in productive 'effort.' This method could be used within a cost-effectiveness analysis framework in the evaluation of new medical technologies, pharmaceuticals, treatment programs, or public health interventions. We apply it to show evidence in favor of implementing a newborn screening program to detect congenital hearing impairment.

Cost-effectiveness of surgery plus radiotherapy versus radiotherapy alone for metastatic epidural spinal cord compression

A recent randomized clinical trial has demonstrated that direct decompressive surgery plus radiotherapy was superior to radiotherapy alone for the treatment of metastatic epidural spinal cord compression. The current study compared the cost-effectiveness of the two approaches. In the original clinical trial, clinical effectiveness was measured by ambulation and survival time until death. In this study, an incremental cost-effectiveness analysis was performed from a societal perspective. Costs related to treatment and posttreatment care were estimated and extended to the lifetime of the cohort. Weibull regression was applied to extrapolate outcomes in the presence of censored clinical effectiveness data. From a societal perspective, the baseline incremental cost-effectiveness ratio (ICER) was found to be $60 per additional day of ambulation (all costs in 2003 Canadian dollars). Using probabilistic sensitivity analysis, 50% of all generated ICERs were lower than $57, and 95% were lower than $242 per additional day of ambulation. This analysis had a 95% CI of -$72.74 to 309.44, meaning that this intervention ranged from a financial savings of $72.74 to a cost of $309.44 per additional day of ambulation. Using survival as the measure of effectiveness resulted in an ICER of $30,940 per life-year gained. We found strong evidence that treatment of metastatic epidural spinal cord compression with surgery in addition to radiotherapy is cost-effective both in terms of cost per additional day of ambulation, and cost per life-year gained.

A framework for cost-effectiveness analysis from clinical trial data.

We present a general Bayesian framework for cost-effectiveness analysis (CEA) from clinical trial data. This framework allows for very flexible modelling of both cost and efficacy related trial data. A common CEA technique is established for this wide class of models through linking mean efficacy and mean cost to the parameters of any given model. Examples are given in which efficacy may be measured as a continuous, binary, ordinal or time-to-event outcome, and in which costs are modelled as distributed normally, log-normally, as a mixture or non-parametrically. A case study is presented, illustrating the methodology and illuminating the role of prior information.

Economic Evaluation of Enoxaparin as Postdischarge Prophylaxis for Deep Vein Thrombosis (DVT) in Elective Hip Surgery

Objectives Clinical trials indicate enoxaparin thromboprophylaxis (Clexane) can be effective and safe when used in an outpatient setting and that extending the length of thromboprophylaxis with enoxaparin to the postdischarge period may be more effective than inpatient thromboprophylaxis alone. This may increase the cost of thromboprophylaxis. The objective of the study was to estimate the expected cost-effectiveness of using enoxaparin for hospital admission only vs. enoxaparin for hospital admission and for 21 days postdischarge. Methods Decision analysis was used to combine probability, resource use and unit cost data, using the framework of cost-effectiveness analysis. The model used a societal perspective to estimate the expected costs of treatment and outcomes to patients undergoing orthopedic surgery for elective hip replacement. Incremental cost-effectiveness ratios were calculated to provide estimates of the cost per life gained, cost per year life year gained and cost per quality-adjusted life year gained with extended use of enoxaparin thromboprophylaxis. Results There was an expected cost per quality-adjusted life year gained of £5732 associated with extended enoxaparin thromboprophylaxis. The results were sensitive to the percentage of patients who could administer enoxaparin injections at home, the rate of DVT associated with standard enoxaparin thromboprophylaxis and the rate of PE associated with standard and extended enoxaparin thromboprophylaxis. Conclusion The analyses indicated that in most cases extended enoxaparin thromboprophylaxis resulted in increased costs for health care services. In all cases, extended thromboprophylaxis with enoxaparin was associated with improved survival and life-years gained.