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.

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 To address these challenges for analyses of time-to-event outcomes, we developed a meta-analytic framework that uses historical reference studies to adjust a log hazard ratio estimate in a new external control study for its additional bias and variability.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.

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.

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.

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.

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.

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.