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.

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.

Comparing Efficacy, Safety, and Preinfusion Period of Axicabtagene Ciloleucel versus Tisagenlecleucel in Relapsed/Refractory Large B Cell Lymphoma.

Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are autologous anti-CD19 chimeric antigen receptor T (CAR T) cell therapies for the treatment of patients with relapsed/refractory large B cell lymphoma (RR-LBCL). Both can induce durable responses; however, cross-trial comparisons are difficult due to differences in study design. In this study, the registration trials of axi-cel and tisa-cel were compared using a matching adjusted indirect comparison (MAIC). A MAIC was performed to adjust for differences in patient characteristics between trials. The estimates for the ZUMA-1 (axi-cel) trial were adjusted using patient-level data to match the study population in JULIET (tisa-cel) for key variables: International Prognostic Index), Eastern Cooperative Oncology Group score, stage, refractoriness or relapsed disease, double/triple hit status, cell of origin, and number of prior lines of therapy. The endpoints analyzed were response, overall survival (OS), and adverse events. After adjusting for differences in patient characteristics between trials, axi-cel was associated with a greater objective response rate (relative risk [RR]=1.61; 95% confidence interval [CI], 1.29 to 2.01) and complete response (RR = 1.62; 95% CI, 1.16 to 2.27) than tisa-cel among patients who underwent infusion. The OS from infusion onward comparing axi-cel to tisa-cel had a hazard ratio of 0.51 (95% CI, 0.31 to 0.83). The indirect comparison showed a higher rate of grade 1 to 2 cytokine release syndrome (CRS) in ZUMA-1 compared with JULIET (RR = 2.03; 95% CI, 1.55 to 2.65) and similar rates of grade ≥3 CRS and neurologic events. In the absence of a direct head-to-head study, the MAIC statistical technique suggests axi-cel may have superior efficacy but a greater risk of grade 1 to 2 CRS. Future real-world studies can further inform the relative efficacy and safety of CAR T therapies in RR-LBCL.

Multivariate network meta-analysis of survival function parameters.

BACKGROUND: Network meta-analysis (NMA) of survival data with a multidimensional treatment effect has been introduced as an alternative to NMA based on the proportional hazards assumption. However, these flexible models have some limitations, such as the use of an approximate likelihood based on discrete hazards, rather than a likelihood for individual event times. The aim of this article is to overcome the limitations and present an alternative implementation of these flexible NMA models for time-to-event outcomes with a two-step approach. METHODS: First, for each arm of every randomised controlled trial (RCT) connected in the network of evidence, reconstructed patient data are fit to alternative survival distributions, including the exponential, Weibull, Gompertz, log-normal, and log-logistic. Next, for each distribution, its scale and shape parameters are included in a multivariate NMA to obtain time-varying estimates of relative treatment effects between competing interventions. RESULTS: An illustrative analysis is presented for a network of RCTs evaluating multiple interventions for advanced melanoma regarding overall survival. Alternative survival distributions were compared based on model fit criteria. Based on the log-logistic distribution, the difference in shape and scale parameters for each treatment versus dacarbazine (DTIC) was identified and the corresponding log hazard and survival curves were presented. CONCLUSIONS: The presented two-step NMA approach provides an evidence synthesis framework for time-to-event outcomes grounded in standard practice of parametric survival analysis. The method allows for a more transparent and efficient model selection process.

Integrating expert opinion with clinical trial data to extrapolate long-term survival: a case study of CAR-T therapy for children and young adults with relapsed or refractory acute lymphoblastic leukemia.

BACKGROUND: Long-term clinical outcomes are necessary to assess the cost-effectiveness of new treatments over a lifetime horizon. Without long-term clinical trial data, current practice to extrapolate survival beyond the trial period involves fitting alternative parametric models to the observed survival. Choosing the most appropriate model is based on how well each model fits to the observed data. Supplementing trial data with feedback from experts may improve the plausibility of survival extrapolations. We demonstrate the feasibility of formally integrating long-term survival estimates from experts with empirical clinical trial data to provide more credible extrapolated survival curves. METHODS: The case study involved relapsed or refractory B-cell pediatric and young adult acute lymphoblastic leukemia (r/r pALL) regarding long-term survival for tisagenlecleucel (chimeric antigen receptor T-cell [CAR-T]) with evidence from the phase II ELIANA trial. Seven pediatric oncologists and hematologists experienced with CAR-T therapies were recruited. Relevant evidence regarding r/r pALL and tisagenlecleucel provided a common basis for expert judgments. Survival rates and related uncertainty at 2, 3, 4, and 5 years were elicited from experts using a web-based application adapted from Sheffield Elicitation Framework. Estimates from each expert were combined with observed data using time-to-event parametric models that accounted for experts' uncertainty, producing an overall distribution of survival over time. These results were validated based on longer term follow-up (median duration 24.2 months) from ELIANA following the elicitation. RESULTS: Extrapolated survival curves based on ELIANA trial without expert information were highly uncertain, differing substantially depending on the model choice. Survival estimates between 2 to 5 years from individual experts varied with a fair amount of uncertainty. However, incorporating expert estimates improved the precision in the extrapolated survival curves. Predictions from a Gompertz model, which experts believed was most appropriate, suggested that more than half of the ELIANA patients treated with tisagenlecleucel will survive up to 5 years. Expert estimates at 24 months were validated by longer follow-up. CONCLUSIONS: This study provides an example of how expert opinion can be elicited and synthesized with observed survival data using a transparent and formal procedure, capturing expert uncertainty, and ensuring projected long-term survival is clinically plausible.

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.

Indirect comparison of nivolumab ± ipilimumab (CheckMate 032) versus other treatments for recurrent small-cell lung cancer.

Aim: To estimate the comparative efficacy of nivolumab ± ipilimumab versus alternative treatments for small-cell lung cancer after at least one prior line of chemotherapy. Materials & methods: A systematic literature review identified six randomized controlled trials (RCTs) that could be connected in a network. The Kaplan-Meier survival curves from these RCTs were synthesized using network meta-analysis models. Aggregate-level matching was used to connect CheckMate 032 to the RCTs. Results: CheckMate 032 was connected to the network by Amrubicin Clinical Trial-1. Nivolumab ± ipilimumab had a more durable tumor response and more favorable long-term survival versus topotecan via intravenous and versus amrubicin. Conclusion: Compared with chemotherapies for recurrent small-cell lung cancer, nivolumab ± ipilimumab improves response duration, which may translate to long-term survival benefits.

Cost-effectiveness of liraglutide versus lixisenatide as add-on therapies to basal insulin in type 2 diabetes.

BACKGROUND: We assessed the cost-effectiveness of the glucagon-like peptide 1 receptor agonists liraglutide 1.8 mg and lixisenatide 20 µg (both added to basal insulin) in patients with type 2 diabetes (T2D) in Sweden. METHODS: The Swedish Institute for Health Economics cohort model for T2D was used to compare liraglutide and lixisenatide (both added to basal insulin), with a societal perspective and with comparative treatment effects derived by indirect treatment comparison (ITC). Drug prices were 2016 values, and all other costs 2015 values. The cost-effectiveness of IDegLira (fixed-ratio combination of insulin degludec and liraglutide) versus lixisenatide plus basal insulin was also assessed, under different sets of assumptions. RESULTS: From the ITC, decreases in HbA1c were -1.32% and -0.43% with liraglutide and lixisenatide, respectively; decreases in BMI were -1.29 and -0.65 kg/m2, respectively. An estimated 2348 cases of retinopathy, 265 of neuropathy and 991 of nephropathy would be avoided with liraglutide compared with lixisenatide in a cohort of 10,000 patients aged over 40 years. In the base-case analysis, total direct costs were higher with liraglutide than lixisenatide, but costs associated with complications were lower. The cost/quality-adjusted life-year (QALY) for liraglutide added to basal insulin was SEK30,802. Base-case findings were robust in sensitivity analyses, except when glycated haemoglobin (HbA1c) differences for liraglutide added to basal insulin were abolished, suggesting these benefits were driving the cost/QALY. With liraglutide 1.2 mg instead of liraglutide 1.8 mg (adjusted for efficacy and cost), liraglutide added to basal insulin was dominant over lixisenatide 20µg.IDegLira was dominant versus lixisenatide plus basal insulin when a defined daily dose was used in the model. CONCLUSIONS: The costs/QALY for liraglutide, 1.8 or 1.2 mg, added to basal insulin, and for IDegLira (all compared with lixisenatide 20 µg added to basal insulin) were below the threshold considered low by Swedish authorities. In some scenarios, liraglutide and IDegLira were cost-saving.