Multi-state network meta-analysis of progression and survival data.

Multiple randomized controlled trials, each comparing a subset of competing interventions, can be synthesized by means of a network meta-analysis to estimate relative treatment effects between all interventions in the evidence base. Here we focus on estimating relative treatment effects for time-to-event outcomes. Cancer treatment effectiveness is frequently quantified by analyzing overall survival (OS) and progression-free survival (PFS). We introduce a method for the joint network meta-analysis of PFS and OS that is based on a time-inhomogeneous tri-state (stable, progression, and death) Markov model where time-varying transition rates and relative treatment effects are modeled with parametric survival functions or fractional polynomials. The data needed to run these analyses can be extracted directly from published survival curves. We demonstrate use by applying the methodology to a network of trials for the treatment of non-small-cell lung cancer. The proposed approach allows the joint synthesis of OS and PFS, relaxes the proportional hazards assumption, extends to a network of more than two treatments, and simplifies the parameterization of decision and cost-effectiveness analyses.

Left truncation in linked data: A practical guide to understanding left truncation and applying it using SAS and R.

Time-to-event data such as time to death are broadly used in medical research and drug development to understand the efficacy of a therapeutic. For time-to-event data, right censoring (data only observed up to a certain point of time) is common and easy to recognize. Methods that use right censored data, such as the Kaplan-Meier estimator and the Cox proportional hazard model, are well established. Time-to-event data can also be left truncated, which arises when patients are excluded from the sample because their events occur before a specific milestone, potentially resulting in an immortal time bias. For example, in a study evaluating the association between biomarker status and overall survival, patients who did not live long enough to receive a genomic test were not observed in the study. Left truncation causes selection bias and often leads to an overestimate of survival time. In this tutorial, we used a nationwide electronic health record-derived de-identified database to demonstrate how to analyze left truncated and right censored data without bias using example code from SAS and R.

A meta-analytic framework to adjust for bias in external control studies.

While randomized controlled trials (RCTs) are the gold standard for estimating treatment effects in medical research, there is increasing use of and interest in using real-world data for drug development. One such use case is the construction of external control arms for evaluation of efficacy in single-arm trials, particularly in cases where randomization is either infeasible or unethical. However, it is well known that treated patients in non-randomized studies may not be comparable to control patients-on either measured or unmeasured variables-and that the underlying population differences between the two groups may result in biased treatment effect estimates as well as increased variability in estimation. To address these challenges for analyses of time-to-event outcomes, we developed a meta-analytic framework that uses historical reference studies to adjust a log hazard ratio estimate in a new external control study for its additional bias and variability. The set of historical studies is formed by constructing external control arms for historical RCTs, and a meta-analysis compares the trial controls to the external control arms. Importantly, a prospective external control study can be performed independently of the meta-analysis using standard causal inference techniques for observational data. We illustrate our approach with a simulation study and an empirical example based on reference studies for advanced non-small cell lung cancer. In our empirical analysis, external control patients had lower survival than trial controls (hazard ratio: 0.907), but our methodology is able to correct for this bias. An implementation of our approach is available in the R package ecmeta.

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.

Penalized regression for left-truncated and right-censored survival data.

High-dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high-throughput screening, electronic health records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left-truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left-truncated and right-censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high-dimensional, real-world clinico-genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.

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.

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.

Maternal and Infant Adverse Outcomes Associated with Mild and Severe Preeclampsia during the First Year after Delivery in the United States.

OBJECTIVE: The burden of preeclampsia severity on the health of mothers and infants during the first year after delivery is unclear, given the lack of population-based longitudinal studies in the United States. STUDY DESIGN: We assessed maternal and infant adverse outcomes during the first year after delivery using population-based hospital discharge information merged with vital statistics and birth certificates of 2,021,013 linked maternal-infant births in California. We calculated sampling weights using the National Center for Health Statistics data to adjust for observed differences in maternal characteristics between California and the rest of the United States. Separately, we estimated the association between preeclampsia and gestational age and examined collider bias in models of preeclampsia and maternal and infant adverse outcomes. RESULTS: Compared with women without preeclampsia, women with mild and severe preeclampsia delivered 0.66 weeks (95% confidence interval [CI]: 0.64, 0.68) and 2.74 weeks (95% CI: 2.72, 2.77) earlier, respectively. Mild preeclampsia was associated with an increased risk of having any maternal adverse outcome (relative risk [RR] = 1.95; 95% CI: 1.93, 1.97), as was severe preeclampsia (RR = 2.80; 95% CI: 2.78, 2.82). The risk of an infant adverse outcome was increased for severe preeclampsia (RR = 2.15; 95% CI: 2.14, 2.17) but only marginally for mild preeclampsia (RR = 0.99; 95% CI: 0.98, 1). Collider bias produced an inverse association for mild preeclampsia and attenuated the association for severe preeclampsia in models for any infant adverse outcome. CONCLUSION: Using multiple datasets, we estimated that severe preeclampsia is associated with a higher risk of maternal and infant adverse outcomes compared with mild preeclampsia, including an earlier preterm delivery.

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.

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.

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.

Real-World Evidence to Supplement Randomized Clinical Trials: Tocilizumab for Severe COVID-19 Pneumonia vs. a Cohort Receiving Standard of Care.

Randomized controlled trials (RCTs) remain the gold standard for evaluating treatment efficacy, but real-world evidence can supplement RCT results. Tocilizumab was not found to reduce 28-day mortality in a phase III, double-blind, placebo-controlled trial (COVACTA) among hospitalized patients with severe coronavirus disease 2019 (COVID-19) pneumonia. We created a real-world external comparator arm mirroring the COVACTA trial to confirm findings and assess the feasibility of using an external comparator arm to supplement an RCT. Eligible COVACTA participants in both the tocilizumab treatment and placebo arms were matched 1:1 using propensity score matching to persons without tocilizumab exposure in an external comparator arm. Adjusted Cox proportional hazard models estimated differences in 28-day mortality comparing COVACTA participants to matched external comparator arm participants. Patients in the COVACTA tocilizumab treatment arm had a similar risk of death compared with patients in the external comparator arm (hazard ratio (HR): 1.09, 95% confidence interval (CI): 0.64-1.84) with similar estimated 28-day mortality in the COVACTA tocilizumab treatment arm compared with the external comparator arm (18%, 95% CI: 13-24 vs. 19%, 95% CI: 13-24, P > 0.9). COVACTA placebo treatment arm participants had a similar risk of mortality (adjusted HR: 0.69, 95% CI: 0.32-1.46) compared with the external comparator arm. Using an external comparator arm has the potential to supplement RCT data and support results of primary RCT analyses.

Temporal Patterns and Adoption of Germline and Somatic BRCA Testing in Ovarian Cancer.

OBJECTIVE: To describe the testing rate, patient characteristics, temporal trends, timing, and results of germline and somatic BRCA testing in patients with ovarian cancer using real-world data. METHODS: We included a cross-sectional subset of adult patients diagnosed with ovarian cancer between January 1, 2011, and November 30, 2018, who received frontline treatment and were followed for at least 1 year in a real-world database. The primary outcome was receipt of BRCA testing, classified by biosample source as germline (blood or saliva) or somatic (tissue). Lines of therapy (frontline, second line, third line) were derived based on dates of surgery and chemotherapy. Descriptive statistics were analyzed. RESULTS: Among 2,557 patients, 72.2% (n=1,846) had at least one documented BRCA test. Among tested patients, 62.5% (n=1,154) had only germline testing, 10.6% (n=197) had only somatic testing, and 19.9% (n=368) had both. Most patients had testing before (9.7%, n=276) or during (48.6%, n=1,521) frontline therapy, with 17.6% (n=273) tested during second-line and 12.7% (n=129) tested during third-line therapy. Patients who received BRCA testing, compared with patients without testing, were younger (mean age 63 years vs 66 years, P <.001) and were more likely to be treated at an academic practice (10.4% vs 7.0%, P =.01), with differences by Eastern Cooperative Oncology Group performance score ( P <.001), stage of disease ( P <.001), histology ( P <.001), geography ( P <.001), and type of frontline therapy ( P <.001), but no differences based on race or ethnicity. The proportion of patients who received BRCA testing within 1 year of diagnosis increased from 24.6% of patients in 2011 to 75.6% of patients in 2018. CONCLUSION: In a large cohort of patients with ovarian cancer, significant practice disparities existed in testing for actionable BRCA mutations. Despite increased testing over time, many patients did not receive testing, suggesting missed opportunities to identify patients appropriate for targeted therapy and genetic counseling.

Retrospective cohort study to assess the association between treatment with tocilizumab and mortality among mechanically ventilated patients with COVID-19.

OBJECTIVES: Assess the association between tocilizumab administration and clinical outcomes among mechanically ventilated patients with COVID-19 pneumonia. DESIGN: Retrospective cohort study. SETTING: Large integrated health system with 9 million members in California, USA. PARTICIPANTS: 4185 Kaiser Permanente members hospitalised with COVID-19 pneumonia requiring invasive mechanical ventilation (IMV). INTERVENTIONS: Receipt of tocilizumab within 10 days of initiation of IMV. OUTCOME MEASURES: Using a retrospective cohort of consecutive patients hospitalised with COVID-19 pneumonia who required IMV in a large integrated health system in California, USA, we assessed the association between tocilizumab administration and 28-day mortality, time to extubation from IMV and time to hospital discharge. RESULTS: Among 4185 patients, 184 received tocilizumab and 4001 patients did not receive tocilizumab within 10 days of initiation of IMV. After inverse probability weighting, baseline characteristics were well balanced between groups. Patients treated with tocilizumab had a similar risk of death in the 28 days after intubation compared with patients not treated with tocilizumab (adjusted HR (aHR), 1.21, 95% CI 0.98 to 1.50), but did have a significantly longer time-to-extubation (aHR 0.71; 95% CI 0.57 to 0.88) and time-to-hospital-discharge (aHR 0.66; 95% CI 0.50 to 0.88). However, patients treated with tocilizumab ≤2 days after initiation of IMV had a similar risk of mortality (aHR 1.47; 95% CI 0.96 to 2.26), but significantly shorter time-to-extubation (aHR 0.37; 95% CI 0.23 to 0.58) and time-to-hospital-discharge (aHR 0.31; 95% CI CI 0.17 to 0.56) compared with patients treated with tocilizumab 3-10 days after initiation of IMV. CONCLUSIONS: Among mechanically ventilated patients with COVID-19, the risk of death in the 28-day follow-up period was similar, but time-to-extubation and time-to-hospital-discharge were longer in patients who received tocilizumab within 10 days of initiation of IMV compared with patients who did not receive tocilizumab.

Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA.

OBJECTIVES: To develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19. DESIGN: Retrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set. SETTING: Optum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA. PARTICIPANTS: 17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020. MAIN OUTCOME MEASURE: All-cause mortality while hospitalised. RESULTS: The full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May. CONCLUSION: Age was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

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.

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.

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.

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.