Direct oral anticoagulants versus low-molecular-weight heparin in patients with cancer-associated venous thrombosis: a cost-effectiveness analysis.

Background: Direct oral anticoagulants (DOACs) have demonstrated clinical benefits and better patient adherence over low-molecular-weight heparin (LMWH) in treating patients with cancer-associated venous thrombosis (CAT). We aimed to compare the cost-effectiveness of DOACs against LMWH in patients with CAT from the perspective of the Hong Kong healthcare system. Methods: A Markov state-transition model was performed to estimate the incremental cost-effectiveness ratio (ICER) per quality-adjusted life years (QALYs) for DOACs and LMWH in a hypothetical cohort of 10,000 patients with CAT over a 5-year lifetime horizon. The model was primarily based on the health states of no event, recurrent venous thromboembolism, bleeding, and death. Transition probabilities, relative risks, and utilities were derived from the literature. Resource cost data were obtained from the Hong Kong Hospital Authority. Deterministic and probabilistic sensitivity analyses tested the robustness of the results. Results: Relative to LMWH, DOACs were associated with increased QALYs (1.52 versus 1.50) at a lower medical cost of USD 2,232 versus 8,224 in five years. The cost of LMWH was the main contributor to the outcome. Out of 10,000 simulated cases, DOACs were dominant in 15.8% and cost-effective in 42.1%, at the willingness-to-pay threshold of USD 148,392 per additional QALY. Conclusions: DOACs were associated with greater QALY improvements and lower overall costs compared to LMWH. Accounting for uncertainty, DOACs were between cost-effective and dominant in 57.9% of cases. DOACs are a cost-effective alternative to LMWH in the management of CAT in Hong Kong.

Challenges, considerations, and approaches for developing a cost-effectiveness model for the adjuvant treatment of muscle-invasive urothelial carcinoma: with a spotlight on nivolumab versus placebo.

AIMS: To present alternative approaches related to both structural assumptions and data sources for the development of a decision analytic model for evaluating the cost-effectiveness of adjuvant nivolumab compared with surveillance in patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection. METHODS AND RESULTS: Alternative approaches related to both structural assumptions and data sources are presented to address challenges and data gaps, as well as discussion of strengths and limitations of each approach. Specifically, challenges and considerations related to the following are presented: (1) selection of a modeling approach (partitioned survival model or state transition model) given the available evidence, (2) choice of health state structure (three- or four-state) to model disease progression and subsequent therapy, (3) modeling of outcomes from subsequent therapy using tunnel states to account for time-dependent transition probabilities or absorbing health states with one-off costs and outcomes applied, and (4) methods for modeling health-state transitions in a setting where treatment has curative intent and available survival data are immature. CONCLUSIONS: Multiple considerations must be taken into account when developing an economic model for new, emerging oncology treatments in early lines of therapy, all of which can affect the model's overall ability to estimate (quality-adjusted) survival benefits over a lifetime horizon. This paper identifies a series of key structural and analytic considerations regarding modeling of nivolumab treatment in the adjuvant MIUC setting. Several alternative approaches with regard to structure and data have been included in a flexible cost-effectiveness model so the impact of the alternative approaches on model results can be explored. The impact of these alternative approaches on cost-effectiveness results are presented in a companion article. Our findings may also help inform the development of future models for other treatments and settings in early-stage cancer.

Improved estimation of overall survival and progression-free survival for state transition modeling.

Aim: National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an STM are optimized to provide closer estimates of the within-trial endpoints. Materials & methods: An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model selected based on AIC/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan-Meier curves. Sensitivity analyses were conducted to assess uncertainty. Results: The novel approach resulted in closer estimations to the OS and PFS Kaplan-Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed. Conclusion: A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.

Markov Cohort State-Transition Model: A Multinomial Distribution Representation.

HIGHLIGHTS: A Markov model simulates the average experience of a cohort of patients.Monte Carlo simulation, the standard approach for estimating the variance, is computationally expensive.A multinomial distribution provides an exact representation of a Markov model.Using the known formulas of a multinomial distribution, the mean and variance of a Markov model can be readily calculated.

Modeling Disease Trajectories for Castration-resistant Prostate Cancer Using Nationwide Population-based Data.

Background: Little is known about disease trajectories for men with castration-resistant prostate cancer (CRPC). Objective: To create a state transition model that estimates time spent in the CRPC state and its outcomes. Design setting and participants: The model was generated using population-based prostate-specific antigen data from 40% of the Swedish male population, which were linked to nationwide population-based databases. We compared the observed and predicted cumulative incidence of transitions to and from the CRPC state. Outcome measurements and statistical analysis: We measured time spent in the CRPC state and the proportion of men who died of prostate cancer during follow-up by CRPC risk category. Results and limitations: Time spent in the CRPC state varied from 1.1 yr for the highest risk category to 3.9 yr for the lowest risk category. The proportion of men who died from prostate cancer within 10 yr ranged from 93% for the highest risk category to 54% for the lowest. There was good agreement between the model estimates and observed data. Conclusions: There is large variation in the time spent in the CRPC state, varying from 1 yr to 4 yr according to risk category. Patient summary: It is possible to accurately estimate the disease trajectory and duration for men with castration-resistant prostate cancer.

A two-phase approach to re-calibrating expensive computer simulation for sex-specific colorectal neoplasia development modeling.

BACKGROUND: Medical evidence from more recent observational studies may significantly alter our understanding of disease incidence and progression, and would require recalibration of existing computational and predictive disease models. However, it is often challenging to perform recalibration when there are a large number of model parameters to be estimated. Moreover, comparing the fitting performances of candidate parameter designs can be difficult due to significant variation in simulated outcomes under limited computational budget and long runtime, even for one simulation replication. METHODS: We developed a two-phase recalibration procedure. As a proof-of-the-concept study, we verified the procedure in the context of sex-specific colorectal neoplasia development. We considered two individual-based state-transition stochastic simulation models, estimating model parameters that govern colorectal adenoma occurrence and its growth through three preclinical states: non-advanced precancerous polyp, advanced precancerous polyp, and cancerous polyp. For the calibration, we used a weighted-sum-squared error between three prevalence values reported in the literature and the corresponding simulation outcomes. In phase 1 of the calibration procedure, we first extracted the baseline parameter design from relevant studies on the same model. We then performed sampling-based searches within a proper range around the baseline design to identify the initial set of good candidate designs. In phase 2, we performed local search (e.g., the Nelder-Mead algorithm), starting from the candidate designs identified at the end of phase 1. Further, we investigated the efficiency of exploring dimensions of the parameter space sequentially based on our prior knowledge of the system dynamics. RESULTS: The efficiency of our two-phase re-calibration procedure was first investigated with CMOST, a relatively inexpensive computational model. It was then further verified with the V/NCS model, which is much more expensive. Overall, our two-phase procedure showed a better goodness-of-fit than the straightforward employment of the Nelder-Mead algorithm, when only a limited number of simulation replications were allowed. In addition, in phase 2, performing local search along parameter space dimensions sequentially was more efficient than performing the search over all dimensions concurrently. CONCLUSION: The proposed two-phase re-calibration procedure is efficient at estimating parameters of computationally expensive stochastic dynamic disease models.

Estimating and Extrapolating Survival Using a State-Transition Modeling Approach: A Practical Application in Multiple Myeloma.

OBJECTIVES: State-transition models (STMs) applied in oncology have given limited considerations to modeling postprogression survival data. This study presents an application of an STM focusing on methods to evaluate the postprogression transition and its impact on survival predictions. METHODS: Data from the lenalidomide plus dexamethasone arm of the ASPIRE trial was used to estimate transition rates for an STM. The model accounted for the competing risk between the progression and preprogression death events and included an explicit structural link between the time to progression and subsequent death. The modeled transition rates were used to simulate individual disease trajectories in a discrete event simulation framework, based on which progression-free survival and overall survival over a 30-year time horizon were estimated. Survival predictions were compared with the observed trial data, matched external data, and estimates obtained from a more conventional partitioned survival analysis approach. RESULTS: The rates of progression and preprogression death were modeled using piecewise exponential functions. The rate of postprogression mortality was modeled using an exponential function accounting for the nonlinear effect of the time to progression. The STM provided survival estimates that closely fitted the trial data and gave more plausible long-term survival predictions than the best-fitting Weibull model applied in a partitioned survival analysis. CONCLUSIONS: The fit of the STM suggested that the modeled transition rates accurately captured the underlying disease process over the modeled time horizon. The considerations of this study may apply to other settings and facilitate a wider use of STMs in oncology.

Economic Evaluation of Sucrose Octasulfate Dressing for Treatment of Diabetic Foot Ulcers in Patients with Type 2 Diabetes.

OBJECTIVES: Patients with noninfected neuroischemic diabetic foot ulcers (DFUs) treated with sucrose octasulfate (SOS) dressing have been shown to have improved healing compared with patients wearing a similar type of dressing without SOS. In this study, we aimed to estimate the cost-effectiveness of SOS dressing compared with conventional dressings from a Canadian public payer's perspective. METHODS: We built a Markov model in a hypothetical cohort of 1,000 inpatients with type 2 diabetes with DFUs. The time horizon was 5 years, and the cycle length was 3 months. We incorporated effectiveness data from the Explorer trial and cohort studies, cost data (2020 Canadian dollars) from published Canadian studies and administrative databases, and utility parameters from the Alberta's Caring for Diabetes cohort. We used probabilistic analysis to calculate the incremental cost-effectiveness ratio of SOS dressing compared with conventional dressings. RESULTS: In the comparison with conventional dressings, use of SOS dressing resulted in an expected increase of 0.16 quality-adjusted life-year (QALY) and an expected $5,878 decrease in health-care costs over 5 years. Adding SOS dressing resulted in a cost savings of $37,061 for every QALY gained. The probability that adding SOS dressing is cost-saving and cost-effective compared with conventional dressings was 89% and 86%, respectively, at a $50,000/QALY willingness-to-pay threshold. CONCLUSIONS: SOS dressing accelerates ulcer healing and helps reduce the spending induced by persistent ulcer management and amputation. Therefore, SOS dressing is likely to be cost-effective and cost-saving, which is consistent with previous health technology assessments in other health-care systems.

Profiling Delirium Progression in Elderly Patients via Continuous-Time Markov Multi-State Transition Models.

Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from no-delirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM.

Exploring the Cost Effectiveness of Shared Decision Making for Choosing between Disease-Modifying Drugs for Relapsing-Remitting Multiple Sclerosis in the Netherlands: A State Transition Model.

BACKGROUND: Up to 31% of patients with relapsing-remitting multiple sclerosis (RRMS) discontinue treatment with disease-modifying drug (DMD) within the first year, and of the patients who do continue, about 40% are nonadherent. Shared decision making may decrease nonadherence and discontinuation rates, but evidence in the context of RRMS is limited. Shared decision making may, however, come at additional costs. This study aimed to explore the potential cost-effectiveness of shared decision making for RRMS in comparison with usual care, from a (limited) societal perspective over a lifetime. METHODS: An exploratory economic evaluation was conducted by adapting a previously developed state transition model that evaluates the cost-effectiveness of a range of DMDs for RRMS in comparison with the best supportive care. Three potential effects of shared decision making were explored: 1) a change in the initial DMD chosen, 2) a decrease in the patient's discontinuation in using the DMD, and 3) an increase in adherence to the DMD. One-way and probabilistic sensitivity analyses of a scenario that combined the 3 effects were conducted. RESULTS: Each effect separately and the 3 effects combined resulted in higher quality-adjusted life years (QALYs) and costs due to the increased utilization of DMD. A decrease in discontinuation of DMDs influenced the incremental cost-effectiveness ratio (ICER) most. The combined scenario resulted in an ICER of €17,875 per QALY gained. The ICER was sensitive to changes in several parameters. CONCLUSION: This study suggests that shared decision making for DMDs could potentially be cost-effective, especially if shared decision making would help to decrease treatment discontinuation. Our results, however, may depend on the assumed effects on treatment choice, persistence, and adherence, which are actually largely unknown.

Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

OBJECTIVES: Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs). METHODS: In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice. RESULTS: PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states. CONCLUSIONS: The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.

Alternative Conversion Methods for Transition Probabilities in State-Transition Models: Validity and Impact on Comparative Effectiveness and Cost-Effectiveness.

Background. In state-transition models (STMs), decision problems are conceptualized using health states and transitions among those health states after predefined time cycles. The naive, commonly applied method (C) for cycle length conversion transforms all transition probabilities separately. In STMs with more than 2 health states, this method is not accurate. Therefore, we aim to describe and compare the performance of method C with that of alternative matrix transformation methods. Design. We compare 2 alternative matrix transformation methods (Eigenvalue method [E], Schure-Padé method [SP]) to method C applied in an STM of 3 different treatment strategies for women with breast cancer. We convert the given annual transition matrix into a monthly-cycle matrix and evaluate induced transformation errors for the transition matrices and the long-term outcomes: life years, quality-adjusted life-years, costs and incremental cost-effectiveness ratios, and the performance related to the decisions. In addition, we applied these transformation methods to randomly generated annual transition matrices with 4, 7, 10, and 20 health states. Results. In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation-induced discrepancies for generated annual transition matrices for 2 treatment strategies. E showed slightly smaller discrepancies than SP in the strategy, where one of the direct transitions between health states was excluded. For long-term outcomes, the largest discrepancy occurred for estimated costs applying method C. For higher dimensional models, E performs best. Conclusions. In our modeling examples, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should therefore be applied in sensitivity analyses.

The cost-utility of stepped-care algorithms according to depression guideline recommendations - Results of a state-transition model analysis.

BACKGROUND: Evidence-based clinical guidelines for major depressive disorder (MDD) recommend stepped-care strategies for sequencing evidence-based treatments conditional on treatment outcomes. This study aims to evaluate the cost-effectiveness of stepped care as recommended by the multidisciplinary clinical guideline vis-à-vis usual care in the Netherlands. METHODS: Guideline-congruent care as described in stepped-care algorithms for either mild MDD or moderate and severe MDD was compared with usual care in a health-economic state-transition simulation model. Incremental costs per QALY gained were estimated over five years from a healthcare perspective. RESULTS: For mild MDD, the cost-utility analysis showed a 67% likelihood of better health outcomes against lower costs, and 33% likelihood of better outcomes against higher costs, implying dominance of guideline-congruent stepped care. For moderate and severe MDD, the cost-utility analysis indicated a 67% likelihood of health gains at higher costs following the stepped-care approach and 33% likelihood of health gains at lower costs, with a mean ICER of about €3,200 per QALY gained. At a willingness to pay threshold of €20,000 per QALY, the stepped-care algorithms for both mild MDD and moderate or severe MDD is deemed cost-effective compared to usual care with a greater than 95% probability. LIMITATIONS: The findings of our decision-analytic modelling are limited by the accuracy and availability of the underlying evidence. This hampers taking into account all individual differences relevant to optimise treatment to individual needs. CONCLUSIONS: It is highly likely that guideline-congruent stepped care for MDD is cost-effective compared to usual care. Our findings support current guideline recommendations.

Impact of increasing treatment rates on cost-effectiveness of subcutaneous immunotherapy (SCIT) in respiratory allergy: a decision analytic modelling approach.

BACKGROUND: Specific immunotherapy is the only causal treatment in respiratory allergy. Due to high treatment cost and possible severe side effects subcutaneous immunotherapy (SCIT) is not indicated in all patients. Nevertheless, reported treatment rates seem to be low. This study aims to analyze the effects of increasing treatment rates of SCIT in respiratory allergy in terms of costs and quality-adjusted life years (QALYs). METHODS: A state-transition Markov model simulates the course of disease of patients with allergic rhinitis, allergic asthma and both diseases over 10 years including a symptom-free state and death. Treatment comprises symptomatic pharmacotherapy alone or combined with SCIT. The model compares two strategies of increased and status quo treatment rates. Transition probabilities are based on routine data. Costs are calculated from the societal perspective applying German unit costs to literature-derived resource consumption. QALYs are determined by translating the mean change in non-preference-based quality of life scores to a change in utility. Key parameters are subjected to deterministic sensitivity analyses. RESULTS: Increasing treatment rates is a cost-effective strategy with an incremental cost-effectiveness ratio (ICER) of 3484€/QALY compared to the status quo. The most influential parameters are SCIT discontinuation rates, treatment effects on the transition probabilities and cost of SCIT. Across all parameter variations, the best case leads to dominance of increased treatment rates while the worst case ICER is 34,315€/QALY. Excluding indirect cost leads to a twofold increase in the ICER. CONCLUSIONS: Measures to increase SCIT initiation rates should be implemented and also address improving adherence.

Cost-effectiveness of golimumab for the treatment of patients with moderate-to-severe ulcerative colitis in Quebec using a patient level state transition microsimulation.

OBJECTIVE: To conduct cost-effectiveness analyses comparing the addition of golimumab to the standard of care (SoC) for treatment of patients with moderate-to-severe ulcerative colitis (UC) who are refractory to conventional therapies in Quebec (Canada). METHODS: An individual patient state transition microsimulation model was developed to project health outcomes and costs over 10 years, using a payer perspective. The incremental benefit estimates for golimumab were driven by induction response and risk of a flare. Flare risks post-induction were derived for golimumab from the PURSUIT maintenance trial and extension study, while those for SoC were derived from the placebo arms of the Active Ulcerative Colitis Trials (ACT) 1 and 2. Other inputs were derived from multiple sources, including retrospective claims analyses and literature. Costs are reported in 2014 Canadian dollars. A 5% annual discount rate was applied to costs and quality-adjusted life-years (QALYs). RESULTS: Compared with SoC, golimumab was projected to increase the time spent in mild disease or remission states, decrease flare rates, and increase QALYs. These gains were achieved with higher direct medical costs. The incremental cost-effectiveness ratio for golimumab vs SoC was $63,487 per QALY. LIMITATIONS: The long-term flare projections for SoC were based on the data available from the ACT 1 and 2 placebo arms, as data were not available from the PURSUIT maintenance or extension trial. Additionally, the study was limited to only SoC and golimumab, due to the availability of individual patient data to analyze. CONCLUSION: This economic analysis concluded that treatment with golimumab is likely more cost-effective vs SoC when considering cost-effectiveness acceptability thresholds from $50,000-$100,000 per QALY.

Insights for the assessment of the economic impact of endemic diseases: specific adaptation of economic frameworks using the case of bovine viral diarrhoea.

Generic frameworks for the economic analysis of farm animal disease are now well established. The paper, therefore, uses bovine viral diarrhoea (BVD) as an example to explore how these frameworks need to be adapted to fit the characteristics of a particular disease and the specific objectives of the analysis. In the case of BVD, given the relative strength of tests available to correctly identify virus-positive animals, thus enabling them to be culled, the emphasis has been on cost-benefit analysis of regional and national certification/eradication schemes. Such analyses in turn raise interesting questions about farmer uptake and maintenance of certification schemes and the equity and cost-effective implementation of these schemes. The complex epidemiology of BVD virus infections and the long-term, widespread and often occult nature of BVD effects make economic analysis of the disease and its control particularly challenging. However, this has resulted in a wider whole-farm perspective that captures the influence of multiple decisions, not just those directly associated with disease prevention and control. There is a need to include management of reproduction, risk and enterprise mix in the research on farmer decision-making, as all these factors impinge on, and are affected by, the spread of BVD.

Les cadres généraux de l'analyse économique des maladies affectant les animaux d'élevage sont désormais bien établis. Les auteurs utilisent l'exemple de la diarrhée virale bovine pour définir les adaptations à apporter à ces cadres afin d'intégrer les caractéristiques d'une maladie donnée et les objectifs spécifiques de l'analyse. Dans le cas de la diarrhée virale bovine, compte tenu de la robustesse des tests disponibles pour détecter les animaux infectés (qui seront abattus), l'accent est mis sur l'analyse coûts-bénéfices des dispositifs régionaux et nationaux de certification sanitaire et d'éradication. Ces analyses soulèvent des questions intéressantes quant à l'engagement et à la persévérance des éleveurs à l'égard des dispositifs de certification et à la mise en oeuvre équitable et rentable de ces dispositifs. La complexité de l'infection due au virus de la diarrhée virale bovine et le caractère durable, répandu et souvent inapparent de ses effets rendent particulièrement difficiles les analyses économiques de cette maladie et de son contrôle. Ces analyses ont toutefois permis de mieux appréhender la situation dans la perspective d'une exploitation, en tenant compte des effets de décisions multiples qui ne se limitent pas à celles directement destinées à prévenir et à contrôler la maladie. La gestion de la reproduction, la gestion des risques et les choix de diversification doivent impérativement être intégrés dans la recherche sur les processus décisionnaires des éleveurs, car tous ces aspects affectent et sont affectés par la propagation de la diarrhée virale bovine.

Hoy en día ya existen modelos genéricos sobradamente contrastados para analizar en clave económica las enfermedades de los animales de granja. Partiendo de esta realidad, los autores utilizan el ejemplo de la diarrea viral bovina (DVB) para determinar el modo de adaptar esos modelos genéricos para que encajen con las características de una enfermedad en particular y con los objetivos específicos de un determinado análisis. En el caso de la DVB, teniendo en cuenta la relativa solidez de los ensayos existentes para identificar correctamente a los animales infectados (para su posterior sacrificio), los autores se centraron en analizar la relación costo-beneficio que presentan algunos dispositivos regionales y nacionales de certificación sanitaria o erradicación. Estos análisis, a su vez, abren interesantes interrogantes sobre el nivel de adhesión y perseverancia de los productores respecto de los programas de certificación y sobre el grado de equidad y rentabilidad con que se aplican esos dispositivos. La compleja epidemiología de las infecciones por el virus de la DVB y el carácter duradero, extendido y a menudo oculto de sus efectos dificultan especialmente el análisis en clave económica de la enfermedad y de las medidas para combatirla. Sin embargo, estos análisis han permitido aprehender desde una perspectiva más amplia la situación de la explotación en su conjunto, teniendo en cuenta la influencia de múltiples decisiones, y no solo de aquellas directamente relacionadas con la prevención y el control de la enfermedad. En toda investigación sobre el proceso decisorio de los productores es necesario tener en cuenta la gestión de la reproducción, la gestión de los riesgos y el tipo de actividades de la explotación, pues todos estos factores influyen en la propagación de la DVB y son influidos por ella.

Cost-effectiveness of pediatric bilateral cochlear implantation in Spain.

OBJECTIVES/HYPOTHESIS: To determine the incremental cost-effectiveness of bilateral versus unilateral cochlear implantation for 1-year-old children suffering from bilateral sensorineural severe to profound hearing loss from the perspective of the Spanish public health system. STUDY DESIGN: Cost-utility analysis. METHODS: We conducted a general-population survey to estimate the quality-of-life increase contributed by the second implant. We built a Markov influence diagram and evaluated it for a life-long time horizon with a 3% discount rate in the base case. RESULTS: The incremental cost-effectiveness ratio of simultaneous bilateral implantation with respect to unilateral implantation for 1-year-old children with severe to profound deafness is €10,323 per quality-adjusted life year (QALY). For sequential bilateral implantation, it rises to €11,733/QALY. Both options are cost-effective for the Spanish health system, whose willingness to pay is estimated at around €30,000/QALY. The probabilistic sensitivity analysis shows that the probability of bilateral implantation being cost-effective reaches 100% for that cost-effectiveness threshold. CONCLUSIONS: Bilateral implantation is clearly cost-effective for the population considered. If possible, it should be done simultaneously (i.e., in one surgical operation), because it is as safe and effective as sequential implantation, and saves costs for the system and for users and their families. Sequential implantation is also cost-effective for children who have received the first implant recently, but it is difficult to determine when it ceases to be so because of the lack of detailed data. These results are specific for Spain, but the model can easily be adapted to other countries. LEVEL OF EVIDENCE: 2C. Laryngoscope, 127:2866-2872, 2017.

State transition model: vorapaxar added to standard antiplatelet therapy to prevent thrombosis post myocardial infarction or peripheral artery disease.

OBJECTIVE: To evaluate long-term health benefits and risks of adding vorapaxar (VOR) to the standard care antiplatelet therapy (SC) of aspirin and/or clopidogrel, among a population with a recent myocardial infarction (MI) and/or peripheral artery disease (PAD). RESEARCH DESIGN AND METHODS: In a state-transition model, patients transition between health states (event-free, recurrent MI, stroke, death), while at risk of experiencing non-transition-related revascularization and non-fatal bleeding events. Risk equations developed from the TRA 2°P-TIMI 50 trial's patient-level data were used to predict cardiovascular (CV) outcomes over longer time horizons. Additional sources, including trials and US-based observational studies, informed the inputs for short-term CV risk, non-CV death, and health-related quality of life. Survival and quality-adjusted life-years (QALYs) were estimated over a lifetime horizon, discounted at 3% per year. RESULTS: Within a cohort of 7361 patients with recent MI and/or PAD, VOR + SC relative to SC alone yielded 176 fewer CV events (MIs, strokes, or CV deaths), but 27 more major bleeding events. VOR + SC was associated with increased life expectancy and health benefits (19.93 undiscounted life-years [LYs], 9.57 discounted QALYs vs. 19.61 undiscounted LYs, 9.41 discounted QALYs). The results were most sensitive to scenarios varying time of vorapaxar initiation, and the assumptions in the 90 day period post-MI. Additional analyses showed that add-on vorapaxar provides consistent incremental benefits in high-risk subgroups. CONCLUSION: This study contributes to the growing literature on secondary prevention add-on therapy, as results from these modeling analyses suggest that adding vorapaxar to SC for patients at high atherothrombotic risk can provide long-term health benefits.

Lessons Learned from a Cross-Model Validation between a Discrete Event Simulation Model and a Cohort State-Transition Model for Personalized Breast Cancer Treatment.

OBJECTIVES: Breast cancer is the most common malignancy among women in developed countries. We developed a model (the Oncotyrol breast cancer outcomes model) to evaluate the cost-effectiveness of a 21-gene assay when used in combination with Adjuvant! Online to support personalized decisions about the use of adjuvant chemotherapy. The goal of this study was to perform a cross-model validation. METHODS: The Oncotyrol model evaluates the 21-gene assay by simulating a hypothetical cohort of 50-year-old women over a lifetime horizon using discrete event simulation. Primary model outcomes were life-years, quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). We followed the International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM) best practice recommendations for validation and compared modeling results of the Oncotyrol model with the state-transition model developed by the Toronto Health Economics and Technology Assessment (THETA) Collaborative. Both models were populated with Canadian THETA model parameters, and outputs were compared. RESULTS: The differences between the models varied among the different validation end points. The smallest relative differences were in costs, and the greatest were in QALYs. All relative differences were less than 1.2%. The cost-effectiveness plane showed that small differences in the model structure can lead to different sets of nondominated test-treatment strategies with different efficiency frontiers. We faced several challenges: distinguishing between differences in outcomes due to different modeling techniques and initial coding errors, defining meaningful differences, and selecting measures and statistics for comparison (means, distributions, multivariate outcomes). CONCLUSIONS: Cross-model validation was crucial to identify and correct coding errors and to explain differences in model outcomes. In our comparison, small differences in either QALYs or costs led to changes in ICERs because of changes in the set of dominated and nondominated strategies.

Estimation of Drug Effectiveness by Modeling Three Time-dependent Covariates: An Application to Data on Cardioprotective Medications in the Chronic Dialysis Population.

In a population of chronic dialysis patients with an extensive burden of cardiovascular disease, estimation of the effectiveness of cardioprotective medication in literature is based on calculation of a hazard ratio comparing hazard of mortality for two groups (with or without drug exposure) measured at a single point in time or through the cumulative metric of proportion of days covered (PDC) on medication. Though both approaches can be modeled in a time-dependent manner using a Cox regression model, we propose a more complete time-dependent metric for evaluating cardioprotective medication efficacy. We consider that drug effectiveness is potentially the result of interactions between three time-dependent covariate measures, current drug usage status (ON versus OFF), proportion of cumulative exposure to drug at a given point in time, and the patient's switching behavior between taking and not taking the medication. We show that modeling of all three of these time-dependent measures illustrates more clearly how varying patterns of drug exposure affect drug effectiveness, which could remain obscured when modeled by the more standard single time-dependent covariate approaches. We propose that understanding the nature and directionality of these interactions will help the biopharmaceutical industry in better estimating drug efficacy.