Cost-effectiveness of genome sequencing for diagnosing patients with undiagnosed rare genetic diseases.

PURPOSE: To estimate the cost-effectiveness of genome sequencing (GS) for diagnosing critically ill infants and noncritically ill pediatric patients (children) with suspected rare genetic diseases from a United States health sector perspective. METHODS: A decision-analytic model was developed to simulate the diagnostic trajectory of patients. Parameter estimates were derived from a targeted literature review and meta-analysis. The model simulated clinical and economic outcomes associated with 3 diagnostic pathways: (1) standard diagnostic care, (2) GS, and (3) standard diagnostic care followed by GS. RESULTS: For children, costs of GS ($7284) were similar to that of standard care ($7355) and lower than that of standard care followed by GS pathways ($12,030). In critically ill infants, when cost estimates were based on the length of stay in the neonatal intensive care unit, the lowest cost pathway was GS ($209,472). When only diagnostic test costs were included, the cost per diagnosis was $17,940 for standard, $17,019 for GS, and $20,255 for standard care followed by GS. CONCLUSION: The results of this economic model suggest that GS may be cost neutral or possibly cost saving as a first line diagnostic tool for children and critically ill infants.

The Effect of Dose Escalation on the Cost-Effectiveness of Etanercept and Adalimumab with Methotrexate Among Patients with Moderate to Severe Rheumatoid Arthritis.

BACKGROUND: Patients with moderate to severe rheumatoid arthritis (RA) occasionally increase their doses of tumor necrosis factor (TNF) inhibitors, especially the monoclonal antibody origin drugs such as adalimumab and infliximab, after inadequate response to the initial dose. Previous studies have evaluated the cost-effectiveness of various sequences of treatment for RA in the United States but have not considered the effect of dose escalation. OBJECTIVE: To assess the cost-effectiveness of etanercept and adalimumab by incorporating the effect of dose escalation in moderate to severe RA patients. METHODS: We adapted the open-source Innovation and Value Initiative - Rheumatoid Arthritis model, version 1.0 to separately simulate the magnitude and time to dose escalation among RA patients taking adalimumab plus methotrexate or etanercept plus methotrexate from a societal perspective and lifetime horizon. An important assumption in the model was that dose escalation would increase treatment costs through its effect on the number of doses but would have no effect on effectiveness. We estimated the dose escalation parameters using the IBM MarketScan Commercial and Medicare Supplemental Databases. We fit competing parametric survival models to model time to dose escalation and used model diagnostics to compare the fit of the competing models. We measured the magnitude of dose escalation as the percentage increase in the number of doses conditional on dose escalation. Finally, we used the parameterized model to simulate treatment sequences beginning with a TNF inhibitor (adalimumab, etanercept) followed by nonbiologic treatment. RESULTS: In baseline models without dose escalation, the incremental cost per quality-adjusted life-year of the etanercept treatment sequence relative to the adalimumab treatment sequence was $85,593. Incorporating dose escalation increased treatment costs for each sequence, but costs increased more with adalimumab, lowering the incremental cost-effectiveness ratio to $9,001. At willingness-to-pay levels of $100,000, the etanercept sequence was more cost-effective compared with the adalimumab sequence, with probability 0.55 and 0.85 in models with and without dose escalation, respectively. CONCLUSIONS: Dose escalation has important effects on cost-effectiveness and should be considered when comparing biologic medications for the treatment of RA. DISCLOSURES: Funding for this study was contributed by Amgen. When this work was conducted, Incerti and Jansen were employees of Precision Health Economics, which received financial support from Amgen. Maksabedian Hernandez, Collier, Gharaibeh, and Stolshek were employees and stockholders of Amgen, and Tkacz and Moore-Schiltz were employees of IBM Watson Health, which received financial support from Amgen. Some of the results of this work were previously presented as a poster at the 2019 AMCP Managed Care & Specialty Pharmacy Annual Meeting, March 25-28, 2019, in San Diego, CA.

An empirical investigation of time-varying cost-effectiveness across the product life cycle.

Cost-effectiveness is traditionally treated as a static estimate driven by clinical trial efficacy and drug price at launch. Prior studies suggest that cost-effectiveness varies over the drug's lifetime. We examined the impact of "learning by doing," one of the least studied drivers of changes in cost-effectiveness across the product life cycle. We combined time-series trends in effectiveness over time by cancer regimen using the Surveillance, Epidemiology, and End Results-Medicare database. We estimated the time-varying effects of treatments in colorectal and pancreatic cancer over their life cycle, including FOLFOX (leucovorin, 5-fluorouracil, and oxaliplatin) and gemcitabine, on survival of patients. Mean prices over time by strength and dosage form were calculated using historical wholesale acquisition costs. We found consistent downward trends in the mortality hazard ratios, which suggest that effectiveness improves over time. In the case of first-line FOLFOX for colorectal cancer, the implied incremental cost-effectiveness ratio based on the observational data fell from $610,000 per life year gained in 2004 to $27,000 per life year gained in 2011. Cost-effectiveness estimated at launch is unlikely to be representative of cost-effectiveness over the drug's lifetime. In the drugs studied, the impact of time-varying clinical effectiveness dominated the impact of changing prices overtime.

The economic burden of kidney graft failure in the United States.

Despite improvements in outcomes for kidney transplant recipients in the past decade, graft failure continues to impose substantial burden on patients. However, the population-wide economic burden of graft failure has not been quantified. This study aims to fill that gap by comparing outcomes from a simulation model of kidney transplant patients in which patients are at risk for graft failure with an alternative simulation in which the risk of graft failure is assumed to be zero. Transitions through the model were estimated using Scientific Registry of Transplant Recipients data from 1987 to 2017. We estimated lifetime costs, overall survival, and quality-adjusted life-years (QALYs) for both scenarios and calculated the difference between them to obtain the burden of graft failure. We find that for the average patient, graft failure will impose additional medical costs of $78 079 (95% confidence interval [CI] $41 074, $112 409) and a loss of 1.66 QALYs (95% CI 1.15, 2.18). Given 17 644 kidney transplants in 2017, the total incremental lifetime medical costs associated with graft failure is $1.38B (95% CI $725M, $1.98B) and the total QALY loss is 29 289 (95% CI 20 291, 38 464). Efforts to reduce the incidence of graft failure or to mitigate its impact are urgently needed.

Developing Open-Source Models for the US Health System: Practical Experiences and Challenges to Date with the Open-Source Value Project.

The Innovation and Value Initiative started the Open-Source Value Project with the aim to improve the credibility and relevance of model-based value assessment in the context of the US healthcare environment. As a core activity of the Open-Source Value Project, the Innovation and Value Initiative develops and provides access to flexible open-source economic models that are developed iteratively based on public feedback and input. In this article, we describe our experience to date with the development of two currently released, Open-Source Value Project models, one in rheumatoid arthritis and one in epidermal growth factor receptor-positive non-small-cell lung cancer. We developed both Open-Source Value Project models using the statistical programming language R instead of spreadsheet software (i.e., Excel), which allows the models to capture multiple model structures, model sequential treatment with individual patient simulations, and improve integration with formal evidence synthesis. By developing the models in R, we were also able to use version control systems to manage changes to the source code, which is needed for iterative and collaborative model development. Similarly, Open-Source Value Project models are freely available to the public to provide maximum transparency and facilitate collaboration. Development of the rheumatoid arthritis and non-small-cell lung cancer model platforms has presented multiple challenges. The development of multiple components of the model platform tailored to different audiences, including web interfaces, required more resources than a cost-effectiveness analysis for a publication would. Furthermore, we faced methodological hurdles, in particular related to the incorporation of multiple competing model structures and novel elements of value. The iterative development based on public feedback also posed some challenges during the review phase, where methodological experts did not always understand feedback from clinicians and vice versa. Response to the Open-Source Value Project by the modeling community and patient organizations has been positive, but feedback from US decision makers has been limited to date. As we progress with this project, we hope to learn more about the feasibility, benefits, and challenges of an open-source and collaborative approach to model development for value assessment.

R You Still Using Excel? The Advantages of Modern Software Tools for Health Technology Assessment.

Economic models are used in health technology assessments (HTAs) to evaluate the cost-effectiveness of competing medical technologies and inform the efficient use of healthcare resources. Historically, these models have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). Although these tools may be sufficient for relatively simple analyses, they put unnecessary constraints on the analysis that may ultimately limit its credibility and relevance. In contrast, modern programming languages such as R, Python, Matlab, and Julia facilitate the development of models that are (i) clinically realistic, (ii) capable of quantifying decision uncertainty, (iii) transparent and reproducible, and (iv) reusable and adaptable. An HTA environment that encourages use of modern software can therefore help ensure that coverage and pricing decisions confer greatest possible benefit and capture all scientific uncertainty, thus enabling correct prioritization of future research.

Toward Relevant and Credible Cost-Effectiveness Analyses for Value Assessment in the Decentralized U.S. Health Care System.

In the United States, there is an increased interest to understand the value of health technologies. Cost-effectiveness analysis is arguably the most appropriate framework to quantify value and to inform reimbursement decision making regarding medical interventions; however, a thorough analysis is resource intensive and complex. In many countries, the cost-effectiveness of medical interventions is evaluated by expert agencies at the national level, but in the United States, reimbursement decision making occurs at the local level. This raises the question of how we can provide a means to transparent cost-effectiveness analysis that reflects the local context and patient population and is based on the latest evidence and scientific insights. In other words, how can we maximize the relevance and credibility of cost-effectiveness evaluations in the context of a decentralized decision-making environment? Published cost-effectiveness analyses typically fail on these dimensions. Access to transparent open-source models that can be adapted to reflect the local setting in a relatively straightforward manner is an essential step toward such a goal. However, no model for cost-effectiveness analysis is ever truly "right" or "complete," and it must evolve along with clinical evidence and improvements in scientific methodology to ensure that its credibility remains. We propose a transparent approach of iterative development and collaboration between content and methodology experts to produce up-to-date, open-source consensus-based cost-effectiveness models that account for parameter and structural uncertainty to help local decision makers understand the confidence with which they might make a decision. Our proposed approach provides a way to adapt formal assessments of value-long the province of centralized health care systems-into the decentralized U.S. health care landscape. DISCLOSURES: This research was funded through the Innovation and Value Initiative, a nonprofit multistakeholder research organization. The Innovation and Value Initiative contracted with Precision Medicine Group for research activities related to this article. Jansen and Incerti are salaried employees and shareholders of Precision Medicine Group. Curtis is a paid consultant for the Innovation and Value Initiative. Curtis also reports consulting fees and grants from Amgen, AbbVie, BMS, Corrona, Janssen, Lilly, Myriad, Pfizer, Roche/Genentech, Radius, and UCB, unrelated to this article.

A Flexible Open-Source Decision Model for Value Assessment of Biologic Treatment for Rheumatoid Arthritis.

OBJECTIVE: The nature of model-based cost-effectiveness analysis can lead to disputes in the scientific community. We propose an iterative and collaborative approach to model development by presenting a flexible open-source simulation model for rheumatoid arthritis (RA), accessible to both technical and non-technical end-users. METHODS: The RA model is a discrete-time individual patient simulation with 6-month cycles. Model input parameters were estimated based on currently available evidence and treatment effects were obtained with Bayesian network meta-analysis techniques. The model contains 384 possible model structures informed by previously published models. The model consists of the following components: (i) modifiable R and C++ source code available in a GitHub repository; (ii) an R package to run the model for custom analyses; (iii) detailed model documentation; (iv) a web-based user interface for full control over the model without the need to be well-versed in the programming languages; and (v) a general audience web-application allowing those who are not experts in modeling or health economics to interact with the model and contribute to value assessment discussions. RESULTS: A primary function of the initial version of RA model is to help understand and quantify the impact of parameter uncertainty (with probabilistic sensitivity analysis), structural uncertainty (with multiple competing model structures), the decision framework (cost-effectiveness analysis or multi-criteria decision analysis), and perspective (healthcare or limited societal) on estimates of value. CONCLUSION: In order for a decision model to remain relevant over time it needs to evolve along with its supporting body of clinical evidence and scientific insight. Multiple clinical and methodological experts can modify or contribute to the RA model at any time due to its open-source nature.

An empirical tool for estimating the share of unmet need due to healthcare inefficiencies, suboptimal access, and lack of effective technologies.

BACKGROUND: Although there has been growing attention to the measurement of unmet need, which is the overall epidemiological burden of disease, current measures ignore the burden that could be eliminated from technological advances or more effective use of current technologies. METHODS: We developed a conceptual framework and empirical tool that separates unmet need from met need and subcategorizes the causes of unmet need into suboptimal access to and ineffective use of current technologies and lack of current technologies. Statistical models were used to model the relationship between health-related quality of life (HR-QOL) and treatment utilization using data from the National Health and Wellness Survey (NHWS). Predicted HR-QOL was combined with prevalence data from the Global Burden of Disease Study (GBD) to estimate met need and the causes of unmet need due to morbidity in the US and EU5 for five diseases: rheumatoid arthritis, breast cancer, Parkinson's disease, hepatitis C, and chronic obstructive pulmonary disease (COPD). RESULTS: HR-QOL was positively correlated with adherence to medication and patient-perceived quality and negatively correlated with financial barriers. Met need was substantial across all disease and regions, although significant unmet need remains. While the majority of unmet need was driven by lack of technologies rather than ineffective use of current technologies, there was considerable variation across diseases and regions. Overall unmet need was largest for COPD, which had the highest prevalence of all diseases in this study. CONCLUSION: We developed a methodology that can inform decisions about which diseases to invest in and whether those investments should focus on improving access to currently available technologies or inventing new technologies.

Assessing Variation in the Cost of Palivizumab for Respiratory Syncytial Virus Prevention in Preterm Infants.

BACKGROUND: The variability in cost of palivizumab treatment, indicated for prevention of respiratory syncytial virus (RSV) infections in high-risk infants, has not been robustly estimated in prior studies. This study aimed to determine the cost variations of palivizumab from a US payer perspective for otherwise healthy preterm infants born 29-35 weeks gestational age (wGA) using infant characteristics and applied dosing regimens. METHODS: Fenton Growth Charts were merged with World Health Organization Child Growth Standards to estimate preterm infant growth patterns. The merged growth chart was applied to infants who received palivizumab from a prospective, observational registry to determine future body weight using each infant's wGA and birth weight. Using quarter 3 (Q3) 2016-Q2 2017 vial cost, treatment costs at monthly dosing intervals were estimated using expected weights and averaged by age to derive expected mean 2016-2017 RSV seasonal costs per infant under various dosing scenarios. RESULTS: Given different dosing scenarios (two to five doses), birth month, and growth patterns for preterm infants 29-35 wGA, the estimated average 2016-2017 seasonal cost of palivizumab treatment ranged from $3221 to $12,568. Outpatient-only cost (excluding first dose at hospital discharge) ranged from $1733 to $11,862. The main drivers of costs were dosing regimen (74% of variance), dosing interacted with birth month (17%), and wGA (6%). CONCLUSION: The considerable variability in the average cost of palivizumab treatment for preterm infants is driven by choice of dosing regimen, wGA, and birth month. Therefore, when estimating the cost of palivizumab, it is important to consider both infant characteristics at each dose and potential dosing regimens.

Short-term costs of preeclampsia to the United States health care system.

BACKGROUND: Preeclampsia is a leading cause of maternal morbidity and mortality and adverse neonatal outcomes. Little is known about the extent of the health and cost burden of preeclampsia in the United States. OBJECTIVE: This study sought to quantify the annual epidemiological and health care cost burden of preeclampsia to both mothers and infants in the United States in 2012. STUDY DESIGN: We used epidemiological and econometric methods to assess the annual cost of preeclampsia in the United States using a combination of population-based and administrative data sets: the National Center for Health Statistics Vital Statistics on Births, the California Perinatal Quality Care Collaborative Databases, the US Health Care Cost and Utilization Project database, and a commercial claims data set. RESULTS: Preeclampsia increased the probability of an adverse event from 4.6% to 10.1% for mothers and from 7.8% to 15.4% for infants while lowering gestational age by 1.7 weeks (P < .001). Overall, the total cost burden of preeclampsia during the first 12 months after birth was $1.03 billion for mothers and $1.15 billion for infants. The cost burden per infant is dependent on gestational age, ranging from $150,000 at 26 weeks gestational age to $1311 at 36 weeks gestational age. CONCLUSION: In 2012, the cost of preeclampsia within the first 12 months of delivery was $2.18 billion in the United States ($1.03 billion for mothers and $1.15 billion for infants), and was disproportionately borne by births of low gestational age.

Incremental net monetary benefit of ocrelizumab relative to subcutaneous interferon ß-1a.

AIM: Disease-modifying therapies (DMTs) impact the natural history of relapsing forms of multiple sclerosis (RRMS) by reducing annual relapse rates and slowing disability progression. The effect of DMTs on indirect costs has not been consistently explored in cost-effectiveness studies thus far. The value to patients of an emerging DMT, ocrelizumab, was quantified in comparison to subcutaneous interferon beta-1a (IFNßSC) for the prevalent RRMS population with mild-to-moderate disability in the US, based on two Phase 3 trials, OPERA I and OPERA II, of ocrelizumab vs IFNßSC in RRMS. MATERIALS AND METHODS: A Markov model was developed to compare disability progression as measured by Expanded Disability Status Scale (EDSS) and relapse outcomes over a 30-year horizon for ocrelizumab vs IFNßSC. Direct, indirect, and informal costs (2016 US dollars) and utilities for EDSS health states were obtained from the literature. Hazard ratios for disability progression and relapse rates were estimated from clinical trials. Value was assessed by calculating the net monetary benefit (NMB), defined as the monetary value of discounted quality-adjusted life years (QALYs) minus total costs, where the value of a QALY was $150,000. One-way sensitivity analyses were conducted. RESULTS: Ocrelizumab was associated with an incremental gain of 0.84 QALYs and cost savings of $287,713 relative to IFNßSC, resulting in an incremental NMB (INMB) of $413,611 per person over 30 years. The INMB increased by $151,763 for those initiating ocrelizumab at EDSS level 1 vs level 4. Influential parameters were QALY value, treatment costs, and disability progression; however, all sensitivity analyses indicated that the INMB for ocrelizumab relative to IFNßSC was ≥$300,000 per person. CONCLUSIONS: Ocrelizumab provides greater value to RRMS patients compared with IFNßSC. Initiating ocrelizumab at lower EDSS levels leads to a greater cumulative value due to slower disability progression, which extends years with higher quality-of-life.

Cost-effectiveness of sequenced treatment of rheumatoid arthritis with targeted immune modulators.

AIMS: To determine the cost-effectiveness of treatment sequences of biologic disease-modifying anti-rheumatic drugs or Janus kinase/STAT pathway inhibitors (collectively referred to as bDMARDs) vs conventional DMARDs (cDMARDs) from the US societal perspective for treatment of patients with moderately to severely active rheumatoid arthritis (RA) with inadequate responses to cDMARDs. MATERIALS AND METHODS: An individual patient simulation model was developed that assesses the impact of treatments on disease based on clinical trial data and real-world evidence. Treatment strategies included sequences starting with etanercept, adalimumab, certolizumab, or abatacept. Each of these treatment strategies was compared with cDMARDs. Incremental cost, incremental quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated for each treatment sequence relative to cDMARDs. The cost-effectiveness of each strategy was determined using a US willingness-to-pay (WTP) threshold of $150,000/QALY. RESULTS: For the base-case scenario, bDMARD treatment sequences were associated with greater treatment benefit (i.e. more QALYs), lower lost productivity costs, and greater treatment-related costs than cDMARDs. The expected ICERs for bDMARD sequences ranged from ∼$126,000 to $140,000 per QALY gained, which is below the US-specific WTP. Alternative scenarios examining the effects of homogeneous patients, dose increases, increased costs of hospitalization for severely physically impaired patients, and a lower baseline Health Assessment Questionnaire (HAQ) Disability Index score resulted in similar ICERs. CONCLUSIONS: bDMARD treatment sequences are cost-effective from a US societal perspective.