Paving the path for implementation of clinical genomic sequencing globally: Are we ready?

Despite the emerging evidence in recent years, successful implementation of clinical genomic sequencing (CGS) remains limited and is challenged by a range of barriers. These include a lack of standardized practices, limited economic assessments for specific indications, limited meaningful patient engagement in health policy decision-making, and the associated costs and resource demand for implementation. Although CGS is gradually becoming more available and accessible worldwide, large variations and disparities remain, and reflections on the lessons learned for successful implementation are sparse. In this commentary, members of the Global Economics and Evaluation of Clinical Genomics Sequencing Working Group (GEECS) describe the global landscape of CGS in the context of health economics and policy and propose evidence-based solutions to address existing and future barriers to CGS implementation. The topics discussed are reflected as two overarching themes: (1) system readiness for CGS and (2) evidence, assessments, and approval processes. These themes highlight the need for health economics, public health, and infrastructure and operational considerations; a robust patient- and family-centered evidence base on CGS outcomes; and a comprehensive, collaborative, interdisciplinary approach.

Implementing Multilevel Network Meta-Regression for Time-To-Event Outcomes: A Case Study in Relapsed Refractory Multiple Myeloma.

OBJECTIVES: Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code. METHODS: The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple-class exposed relapsed/refractory multiple myeloma in terms of overall survival. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. RESULTS: The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. CONCLUSIONS: We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments.

The Health Inequality Impact of a New Cancer Therapy Given Treatment and Disease Characteristics.

OBJECTIVES: This study aimed to perform a simulation study to quantify the health inequality impact of a cancer therapy given cancer and treatment characteristics using the distributional cost-effectiveness framework. METHODS: The following factors were varied in 10 000 simulations: lifetime risk of the disease, median overall survival (OS) with standard of care (SOC), difference in OS between non-Hispanic (NH)-Black and NH-White patients (prognostic effect), treatment effect of the new therapy relative to SOC, whether the treatment effect differs between NH-Black and NH-White patients (effect modification), health utility, drug costs, and preprogression and postprogression costs. Based on these characteristics, the incremental population net health benefits were calculated for the new therapy and applied to a US distribution of quality-adjusted life expectancy at birth. The health inequality impact was quantified as the difference in the degree of inequality in the "post-new therapy" versus "pre-new therapy" quality-adjusted life expectancy distributions. RESULTS: For cancer types characterized by relatively large lifetime risk, large median OS with SOC, large treatment effect, and large effect modification, the direction of the impact of the new therapy on inequality is easy to predict. When effect modification is minor or absent, which is a realistic scenario, the direction of the inequality impact is difficult to predict. Larger incremental drug costs have a worsening effect on health inequality. CONCLUSIONS: The findings provide a guide to help decision makers and other stakeholders make an initial assessment whether a new therapy with known treatment effects for a specific tumor type can have a positive or negative health inequality impact.

Unanchored Population-Adjusted Indirect Comparison Methods for Time-to-Event Outcomes Using Inverse Odds Weighting, Regression Adjustment, and Doubly Robust Methods With Either Individual Patient or Aggregate Data.

OBJECTIVES: Several methods for unanchored population-adjusted indirect comparisons (PAICs) are available. Exploring alternative adjustment methods, depending on the available individual patient data (IPD) and the aggregate data (AD) in the external study, may help minimize bias in unanchored indirect comparisons. However, methods for time-to-event outcomes are not well understood. This study provides an overview and comparison of methods using a case study to increase familiarity. A recent method is applied to marginalize conditional hazard ratios, which allows for the comparisons of methods, and a doubly robust method is proposed. METHODS: The following PAIC methods were compared through a case study in third-line small cell lung cancer, comparing nivolumab with standard of care based on a single-arm phase II trial (CheckMate 032) and real-world study (Flatiron) in terms of overall survival: IPD-IPD analyses using inverse odds weighting, regression adjustment, and a doubly robust method; IPD-AD analyses using matching-adjusted indirect comparison, simulated treatment comparison, and a doubly robust method. RESULTS: Nivolumab extended survival versus standard of care with hazard ratios ranging from 0.63 (95% CI 0.44-0.90) in naive comparisons (identical estimates for IPD-IPD and IPD-AD analyses) to 0.69 (95% CI 0.44-0.98) in the IPD-IPD analyses using regression adjustment. Regression-based and doubly robust estimates yielded slightly wider confidence intervals versus the propensity score-based analyses. CONCLUSIONS: The proposed doubly robust approach for time-to-event outcomes may help to minimize bias due to model misspecification. However, all methods for unanchored PAIC rely on the strong assumption that all prognostic covariates have been included.

Clinical validity and utility of circulating tumor DNA (ctDNA) testing in advanced non-small cell lung cancer (aNSCLC): a systematic literature review and meta-analysis.

Purpose: Circulating tumor DNA (ctDNA) testing has become a promising tool to guide first-line (1L) targeted treatment for advanced non-small cell lung cancer (aNSCLC). This study aims to estimate the clinical validity (CV) and clinical utility (CU) of ctDNA-based next-generation sequencing (NGS) for oncogenic driver mutations to inform 1L treatment decisions in aNSCLC through a systematic literature review and meta-analysis. Methods: A systematic literature search was conducted in PubMed/MEDLINE and Embase to identify randomized control trials or observational studies reporting CV/CU on ctDNA testing in patients with aNSCLC. Meta-analyses were performed using bivariate random-effects models to estimate pooled sensitivity and specificity. Progression-free/overall survival (PFS/OS) was summarized for CU studies. Results: Eighteen studies were identified: 17 CV only, 2 CU only, and 1 both. Thirteen studies were included for the meta-analysis on multi-gene detection. The overall sensitivity and specificity for ctDNA detection of any mutation were 0.69 (95% CI, 0.63-0.74) and 0.99 (95% CI, 0.97-1.00) respectively. However, sensitivity varied greatly by driver gene, ranging from 0.29 (95% CI, 0.13-0.53) for ROS 1 to 0.77 (95% CI, 0.63-0.86) for KRAS . Two studies compared PFS with ctDNA versus tissue-based testing followed by 1L targeted therapy found no significant differences. One study reported OS curves on ctDNA-matched and tissue-matched therapies but no hazard ratios were provided. Conclusion: ctDNA testing demonstrated an overall acceptable diagnostic accuracy in aNSCLC patients, however, sensitivity varied greatly by driver mutation. Further research is needed, especially for uncommon driver mutations, to better understand the CU of ctDNA testing in guiding targeted treatments for aNSCLC.

The Health Inequality Impact of Liquid Biopsy to Inform First-Line Treatment of Advanced Non-Small Cell Lung Cancer: A Distributional Cost-Effectiveness Analysis.

OBJECTIVES: To perform a distributional cost-effectiveness analysis of liquid biopsy (LB) followed by, if needed, tissue biopsy (TB) (LB-first strategy) relative to a TB-only strategy to inform first-line treatment of advanced non-small cell lung cancer (aNSCLC) from a US payer perspective by which we quantify the impact of LB-first on population health inequality according to race and ethnicity. METHODS: With a health economic model, quality-adjusted life-years (QALYs) and costs per patient were estimated for each subgroup. Given the lifetime risk of aNSCLC, and assuming equally distributed opportunity costs, the incremental net health benefits of LB-first were calculated, which were used to estimate general population quality-adjusted life expectancy at birth (QALE) by race and ethnicity with and without LB-first. The degree of QALYs and QALE differences with the strategies was expressed with inequality indices. Their differences were defined as the inequality impact of LB-first. RESULTS: LB-first resulted in an additional 0.21 (95% uncertainty interval: 0.07-0.39) QALYs among treated patients, with the greatest gain observed among Asian patients (0.31 QALYs [0.09-0.61]). LB-first resulted in an increase in relative inequality in QALYs among patients, but a minor decrease in relative inequality in QALE. CONCLUSIONS: LB-first to inform first-line aNSCLC therapy can improve health outcomes. With current diagnostic performance, the benefit is the greatest among Asian patients, thereby potentially widening racial and ethnic differences in survival among patients with aNSCLC. Assuming equally distributed opportunity costs and access, LB-first does not worsen and, in fact, may reduce inequality in general population health according to race and ethnicity.

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.

Indirect treatment comparison of lurbinectedin versus other second-line treatments for small-cell lung cancer.

Aim: Compare lurbinectedin versus other second-line (2L) small-cell lung cancer (SCLC) treatments. Methods: An unanchored matching-adjusted indirect comparison connected the platinum-sensitive SCLC cohort of a single-arm lurbinectedin trial to a network of three randomized controlled trials (oral and intravenous [IV] topotecan, and platinum re-challenge) identified by systematic literature review. Network meta-analysis methods estimated relative treatment effects. Results: In platinum-sensitive patients, lurbinectedin demonstrated a survival benefit and favorable safety profile versus oral and IV topotecan and platinum re-challenge (overall survival, hazard ratio [HR]: 0.43; 95% credible interval [CrI]: 0.27, 0.67; HR: 0.43; 95% CrI: 0.26, 0.70; HR: 0.42; 95% CrI: 0.30, 0.58 respectively). Conclusion: Lurbinectedin showed a robust survival benefit and favorable safety versus other SCLC treatments in 2L platinum-sensitive SCLC.

A Comparison of Alternative Network Meta-Analysis Methods in the Presence of Nonproportional Hazards: A Case Study in First-Line Advanced or Metastatic Renal Cell Carcinoma.

OBJECTIVES: Network meta-analysis (NMA) of time-to-event outcomes based on constant hazard ratios can result in biased findings when the proportional hazards (PHs) assumption does not hold in a subset of trials. We aimed to summarize the published non-PH NMA methods for time-to-event outcomes, demonstrate their application, and compare their results. METHODS: The following non-PH NMA methods were compared through an illustrative case study in oncology of 4 randomized controlled trials in terms of progression-free survival and overall survival: (1) 1-step or (2) 2-step multivariate NMAs based on traditional survival distributions or fractional polynomials, (3) NMAs with restricted cubic splines for baseline hazard, and (4) restricted mean survival NMA. RESULTS: For progression-free survival, the PH assumption did not hold across trials and non-PH NMA methods better reflected the relative treatment effects over time. The most flexible models (fractional polynomials and restricted cubic splines) fit better to the data than the other approaches. Estimated hazard ratios obtained with different non-PH NMA methods were similar at 5 years of follow-up but differed thereafter in the extrapolations. Although there was no strong evidence of PH violation for overall survival, non-PH NMA methods captured this uncertainty in the relative treatment effects over time. CONCLUSIONS: When the PH assumption is questionable in a subset of the randomized controlled trials, we recommend assessing alternative non-PH NMA methods to estimate relative treatment effects for time-to-event outcomes. We propose a transparent and explicit stepwise model selection process considering model fit, external constraints, and clinical validity. Given inherent uncertainty, sensitivity analyses are suggested.

Matching-adjusted indirect comparison of axi-cel and liso-cel in relapsed or refractory large B-cell lymphoma.

In the absence of a randomized head-to-head trial, an unanchored matching-adjusted indirect comparison was performed to estimate the relative treatment effects of axicabtagene ciloleucel (axi-cel; ZUMA-1) versus lisocabtagene maraleucel (liso-cel; TRANSCEND-NHL-001) for treatment of relapsed/refractory (R/R) large B-cell lymphoma (LBCL) after at least two lines of therapy. After matching, axi-cel and liso-cel had comparable objective response rates and duration. Compared to liso-cel, axi-cel was associated with improvements in overall survival (hazard ratio [HR]: 0.53 [95% CI: 0.34-0.82]) and progression-free survival (HR: 0.61 [95% CI: 0.40-0.92]). Axi-cel was associated with a higher rate of grade ≥3 cytokine release syndrome (odds ratio [OR]: 3.64 [95% CI: 1.04-12.76]) and neurological events (OR: 3.45 [95% CI: 1.65-7.19]), with smaller differences estimated in scenario analyses including ZUMA-1 safety management cohorts. Results suggest axi-cel improved survival compared to liso-cel but with increased odds of specific adverse events.

Network Meta-analysis on Disconnected Evidence Networks When Only Aggregate Data Are Available: Modified Methods to Include Disconnected Trials and Single-Arm Studies while Minimizing Bias.

BACKGROUND: Network meta-analysis (NMA) requires a connected network of randomized controlled trials (RCTs) and cannot include single-arm studies. Regulators or academics often have only aggregate data. Two aggregate data methods for analyzing disconnected networks are random effects on baseline and aggregate-level matching (ALM). ALM has been used only for single-arm studies, and both methods may bias effect estimates. METHODS: We modified random effects on baseline to separate RCTs connected to and disconnected from the reference and any single-arm studies, minimizing the introduction of bias. We term our modified method reference prediction. We similarly modified ALM and extended it to include RCTs disconnected from the reference. We tested these methods using constructed data and a simulation study. RESULTS: In simulations, bias for connected treatments for ALM ranged from -0.0158 to 0.051 and for reference prediction from -0.0107 to 0.083. These were low compared with the true mean effect of 0.5. Coverage ranged from 0.92 to 1.00. In disconnected treatments, bias of ALM ranged from -0.16 to 0.392 and of reference prediction from -0.102 to 0.40, whereas coverage of ALM ranged from 0.30 to 0.82 and of reference prediction from 0.64 to 0.94. Under fixed study effects for disconnected evidence, bias was similar, but coverage was 0.81 to 1.00 for reference prediction and 0.18 to 0.76 for ALM. Trends of similar bias but greater coverage for reference prediction with random study effects were repeated in constructed data. CONCLUSIONS: Both methods with random study effects seem to minimize bias in treatment connected to the reference. They can estimate treatment effects for disconnected treatments but may be biased. Reference prediction has greater coverage and may be recommended overall. HIGHLIGHTS: Two methods were modified for network meta-analysis on disconnected networks and for including single-arm observational or interventional studies in network meta-analysis using only aggregate data and for minimizing the bias of effect estimates for treatments only in trials connected to the reference.Reference prediction was developed as a modification of random effects on baseline that keeps analyses of trials connected to the reference separately from those disconnected from the reference and from single-arm studies. The method was further modified to account for correlation in trials with more than 2 arms and, under random study effects, to estimate variance in heterogeneity separately in connected and disconnected evidence.Aggregate-level matching was extended to include trials disconnected from the reference, rather than only single-arm studies. The method was further modified to separately estimate treatment effects and heterogeneity variance in the connected and disconnected evidence and to account for the correlation between arms in trials with more than 2 arms.Performance was assessed using a constructed data example and simulation study.The methods were found to have similar, and sometimes low, bias when estimating the relative effects for disconnected treatments, but reference prediction with random study effects had the greatest coverage.The use of reference prediction with random study effects for disconnected networks is recommended if no individual patient data or alternative real-world evidence is available.

Assessments of the Value of New Interventions Should Include Health Equity Impact.

A formal evaluation of the health equity impact of a new intervention is hardly ever performed as part of a health technology assessment to understand its value. This should change, in our view. An evidence-based quantitative assessment of the health equity impact can help decision makers develop coverage policies, programme designs, and quality initiatives focused on optimizing both total health and health equity given the treatment options available. We outline the conceptual basis of how a new intervention can impact health equity and adopt distributional cost-effectiveness analysis based on decision-analytic models to assess this quantitatively, using a newly US FDA-approved drug for Alzheimer's disease (aducanumab) as an example. We argue that gaps in the evidence base for the new intervention, for example, due to limited clinical research participation among racial and ethnic minority groups, do not preclude such an evaluation. Understanding these uncertainties has implications for fair pricing, decision making, and future research. If we are serious about population-level decision making that not only is focused on improving total health but also aims to improve health equity, we should consider routinely assessing the health equity impact of new interventions.

Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials.

BACKGROUND: There have been ongoing efforts to understand when and how data from observational studies can be applied to clinical and regulatory decision making. The objective of this review was to assess the comparability of relative treatment effects of pharmaceuticals from observational studies and randomized controlled trials (RCTs). METHODS: We searched PubMed and Embase for systematic literature reviews published between January 1, 1990, and January 31, 2020, that reported relative treatment effects of pharmaceuticals from both observational studies and RCTs. We extracted pooled relative effect estimates from observational studies and RCTs for each outcome, intervention-comparator, or indication assessed in the reviews. We calculated the ratio of the relative effect estimate from observational studies over that from RCTs, along with the corresponding 95% confidence interval (CI) for each pair of pooled RCT and observational study estimates, and we evaluated the consistency in relative treatment effects. RESULTS: Thirty systematic reviews across 7 therapeutic areas were identified from the literature. We analyzed 74 pairs of pooled relative effect estimates from RCTs and observational studies from 29 reviews. There was no statistically significant difference (based on the 95% CI) in relative effect estimates between RCTs and observational studies in 79.7% of pairs. There was an extreme difference (ratio < 0.7 or > 1.43) in 43.2% of pairs, and, in 17.6% of pairs, there was a significant difference and the estimates pointed in opposite directions. CONCLUSIONS: Overall, our review shows that while there is no significant difference in the relative risk ratios between the majority of RCTs and observational studies compared, there is significant variation in about 20% of comparisons. The source of this variation should be the subject of further inquiry to elucidate how much of the variation is due to differences in patient populations versus biased estimates arising from issues with study design or analytical/statistical methods.

Agalsidase beta treatment slows estimated glomerular filtration rate loss in classic Fabry disease patients: results from an individual patient data meta-analysis.

BACKGROUND: Fabry disease is a rare, X-linked genetic disorder that, if untreated in patients with the Classic phenotype, often progresses to end-stage kidney disease. This meta-analysis determined the effect of agalsidase beta on loss of estimated glomerular filtration rate (eGFR) in the Classic phenotype using an expansive evidence base of individual patient-level data. METHODS: The evidence base included four Sanofi-Genzyme studies and six studies from a systematic literature review. These were restricted to Classic Fabry patients meeting the eligibility criteria from Phases III and IV agalsidase beta trials, including 315 patients (161 treated). Linear regression was first used to model annual change in eGFR for each patient and the resulting annualized eGFR slopes were modelled with treatment and covariates using quantile regression. These results were then used to estimate median annualized eGFR change in agalsidase beta treated versus untreated groups. RESULTS: Imbalances across treatment groups were found in baseline age, sex and proteinuria, but not in the use of renin-angiotensin system blockers. The adjusted model suggests that treated (agalsidase beta) patients experienced a slower median eGFR decrease [2.46 mL/min/1.73 m2/year slower; 95% confidence interval (CI) 0.63-4.29; P = 0.0087] than comparable untreated patients. The median eGFR decrease was 2.64 mL/min/1.73 m2/year slower (95% CI 0.53-4.78; P = 0.0141) in treated Classic males. CONCLUSIONS: Using an expansive evidence base and robust modelling approach, these data indicate that agalsidase beta-treated patients with the Classic phenotype conserve their renal function better than untreated patients.

Matching adjusted indirect comparisons of efficacy outcomes for idecabtagene vicleucel (ide-cel, bb2121) versus selinexor + dexamethasone and belantamab mafodotin in relapsed and refractory multiple myeloma.

Idecabtagene vicleucel (ide-cel, bb2121), a chimeric antigen receptor (CAR) T cell therapy, has been investigated in patients with relapsed and refractory multiple myeloma (RRMM) who have received an immunomodulatory drug, proteasome inhibitor, and anti-CD38 antibody in the single-arm phase 2 KarMMa clinical trial. Two therapies with distinct mechanisms of action - selinexor plus dexamethasone (Sd) and belantamab mafodotin (BM) - are currently approved in the United States for heavily pretreated patients, including those who are triple-class refractory. To compare ide-cel versus Sd and ide-cel versus BM, matching-adjusted indirect comparisons were performed. Ide-cel extended progression-free survival (PFS) and overall survival (OS) versus both Sd and BM (hazard ratio (HR); 95% confidence interval (CI)). PFS: ide-cel versus Sd, 0.46; 0.28-0.75; ide-cel versus BM, 0.45; 0.27-0.77. OS: ide-cel versus Sd, 0.23; 0.13-0.42; ide-cel versus BM, 0.35; 0.14-0.87. These results suggest ide-cel offers clinically meaningful improvements over currently approved regimens for patients with heavily pretreated RRMM.

Comparative effectiveness of nivolumab versus standard of care for third-line patients with small-cell lung cancer.

Aim: To estimate the comparative effectiveness of nivolumab versus standard of care (SOC) in terms of overall survival (OS) for small-cell lung cancer patients treated with two prior lines of chemotherapy, in other words, third line in the USA. Materials & methods: Data were from CheckMate 032, a single-arm trial of nivolumab, and real-world electronic patient records. Comparisons of OS were conducted using three different methods to adjust for differences (regression, weighting and doubly robust) between the populations. Results: Nivolumab was associated with longer survival compared with SOC (hazard ratio for OS: 0.58-0.70) across all methods for adjustment. Conclusion: Nivolumab was more efficacious in terms of OS as third-line treatment for small-cell lung cancer compared with current SOC in the USA.

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.