A Comparison of Generic and Condition-Specific Preference-Based Measures Using Data From Nivolumab Trials: EQ-5D-3L, Mapping to the EQ-5D-5L, and European Organisation for Research and Treatment of Cancer Quality of Life Utility Measure-Core 10 Dimensions.

OBJECTIVES: There is growing interest in condition-specific preference measures, including the European Organisation for Research and Treatment of Cancer Quality of Life Utility Measure-Core 10 Dimensions (QLU-C10D). This research assessed the implications of using utility indices on the basis of the EQ-5D-3L, a mapping of EQ-5D-3L to the EQ-5D-5L, and the QLU-C10D, and compared their psychometric properties. METHODS: Data were taken from 8 phase 3 randomized controlled trials of nivolumab with or without ipilimumab for the treatment of solid tumors. Utilities for progression-related states were calculated using the UK and English value sets and incremental quality-adjusted life-years (QALYs) derived from established UK cost-effectiveness models. The psychometric properties of the utility indices were assessed using pooled trial data. RESULTS: Compared with the EQ-5D-3L index, the mapped EQ-5D-5L index yielded an average of 6% more and the QLU-C10D index an average of 2% fewer incremental QALYs for nivolumab versus comparators. All indices could differentiate between groups defined by performance status, cancer stage, or self-reported health status at baseline and detect meaningful changes in performance status, tumor response, health status, and quality of life over approximately 12 weeks of treatment. CONCLUSIONS: The lower QALY yield of the QLU-C10D was balanced by evidence of greater validity and responsiveness. Benefits gained from using the QLU-C10D may be apparent when treatments affect targeted symptoms and functional aspects, including sleep, bowel function, appetite, nausea, and fatigue. The observed differences in QALYs may not be sufficiently large to affect health technology assessment decisions.

Nivolumab in gastric/gastroesophageal junction cancer: real-world data from UK Early Access to Medicines Scheme.

Aim: This study provides real-world insight into patient profile, clinical effectiveness and health-related quality of life among patients with advanced gastric/gastroesophageal junction (GEJ) adenocarcinoma treated with nivolumab. Materials & methods: Data were collected from medical records of patients with advanced GEJ adenocarcinoma treated with nivolumab in a UK Early Access to Medicines Scheme and from the patient-reported EuroQoL five dimensions questionnaire. Results: Evaluable patients (n = 113; median age 62 years) were predominantly male (76.1%), White (87.4%) and with GEJ adenocarcinoma (61.9%). Median follow-up was 2.8 months. The 6-month progression-free survival and overall survival were 31.6 and 56.7%, respectively. Mean EuroQoL five dimensions questionnaire index utility scores at baseline, 8, 16 and 24 weeks were 0.795, 0.831, 0.870 and 0.793, respectively. Conclusion: Progression-free survival was consistent with trial results and health-related quality of life remained stable over time.

Lay abstract This study looked at the characteristics and quality of life (QoL) of patients who were taking the drug, nivolumab, and how well it works for advanced gastric/gastroesophageal junction (GEJ) adenocarcinoma. GEJ adenocarcinoma is a rare type of cancer that starts in the GEJ, the area where the esophagus and stomach join. Information was collected from the medical records of patients who had advanced GEJ adenocarcinoma and were treated with nivolumab as part of a UK program that gives people access to new treatments that are not yet licensed. Patients also filled out a questionnaire called the EuroQoL five dimensions questionnaire that focuses on a patient's quality of life (QoL). In total, 113 patients were a part of the study. The midpoint of all patients' ages was 62 years and they were mostly males (76.1%), Whites (87.4%) and with GEJ adenocarcinoma (61.9%). The midpoint of follow-up time was 2.8 months. The percentages of patients meeting progression-free survival for 6 months, a period when a patient lives with GEJ adenocarcinoma but it does not get worse, and overall survival were 31.6 and 56.7%, respectively. Mean EuroQoL five dimensions questionnaire index scores (comprised between zero and one, the higher the better) at treatment start, 8, 16 and 24 weeks were 0.795, 0.831, 0.870 and 0.793, respectively. Progression-free survival was similar to clinical trial results and QoL was constant over time.

What Did Time Tell Us? A Comparison and Retrospective Validation of Different Survival Extrapolation Methods for Immuno-Oncologic Therapy in Advanced or Metastatic Renal Cell Carcinoma.

BACKGROUND: The immuno-oncologic (IO) mechanism of action may lead to an overall survival (OS) hazard that changes over time, producing shapes that standard parametric extrapolation methods may struggle to reflect. Furthermore, selection of the most appropriate extrapolation method for health technology assessment is often based on trial data with limited follow-up. OBJECTIVE: To examine this problem, we fitted a range of extrapolation methods to patient-level survival data from CheckMate 025 (NCT01668784, CM-025), a phase III trial comparing nivolumab with everolimus for previously treated advanced renal cell carcinoma (aRCC), to assess their predictive accuracy over time. METHODS: Six extrapolation methods were examined: standard parametric models, natural cubic splines, piecewise models combining Kaplan-Meier data with an exponential or non-exponential distribution, response-based landmark models, and parametric mixture models. We produced three database locks (DBLs) at minimum follow-ups of 15, 27, and 39 months to align with previously published CM-025 data. A three-step evaluation process was adopted: (1) selection of the distribution family for each method in each of the three DBLs, (2) internal validation comparing extrapolation-based landmark and mean survival with the latest CM-025 dataset (minimum follow-up, 64 months), and (3) external validation of survival projections using clinical expert opinion and long-term follow-up data from other nivolumab studies in aRCC (CheckMate 003 and CheckMate 010). RESULTS: All extrapolation methods, with the exception of mixture models, underestimated landmark and mean OS for nivolumab compared with CM-025 long-term follow-up data. OS estimates for everolimus tended to be more accurate, with four of the six methods providing landmark OS estimates within the 95% confidence interval of observed OS as per the latest dataset. The predictive accuracy of survival extrapolation methods fitted to nivolumab also showed greater variation than for everolimus. The proportional hazards assumption held for all DBLs, and a dependent log-logistic model provided reliable estimates of longer-term survival for both nivolumab and everolimus across the DBLs. Although mixture models and response-based landmark models provided reasonable estimates of OS based on the 39-month DBL, this was not the case for the two earlier DBLs. The piecewise exponential models consistently underestimated OS for both nivolumab and everolimus at clinically meaningful pre-specified landmark time points. CONCLUSIONS: This aRCC case study identified marked differences in the predictive accuracy of survival extrapolation methods for nivolumab but less so for everolimus. The dependent log-logistic model did not suffer from overfitting to early DBLs to the same extent as more complex methods. Methods that provide more degrees of freedom may accurately represent survival for IO therapy, particularly if data are more mature or external data are available to inform the long-term extrapolations.

Cost Effectiveness of Nivolumab in Patients with Advanced, Previously Treated Squamous and Non-squamous Non-small-cell Lung Cancer in England.

OBJECTIVE: The aim of this study was to investigate the cost effectiveness of nivolumab versus docetaxel in previously treated, advanced non-small-cell lung cancer (NSCLC) in England and assess how conditional reimbursement within the Cancer Drugs Fund (CDF) can be used to ensure timely patient access to effective treatments. METHODS: Cost-effectiveness models developed for the National Institute for Health and Care Excellence (NICE) TA483 (squamous) and TA484 (non-squamous) technology appraisals were supplemented with updated overall survival (OS), progression-free survival (PFS), and time-to-treatment discontinuation data collected as part of the CDF data collection agreement. Both models were developed by using a partitioned-survival approach based on PFS and OS predictions from CheckMate 017 and CheckMate 057 to estimate the projected proportion of patients in each health state (progression free, progression, death) throughout the model's time horizon. The primary outcomes were estimated costs, quality-adjusted life-years (QALYs), and the resulting incremental cost-effectiveness ratio (ICER) expressed as cost/QALY gained. RESULTS: Base-case ICERs for treating patients with nivolumab versus docetaxel were £35,657/QALY and £38,703/QALY for squamous and non-squamous NSCLC patients, respectively, which are substantially lower than those obtained from what were deemed to be the most appropriate analyses for decision making in the original submissions when run with the same patient access scheme discount: £68,576/QALY and £73,189/QALY gained for squamous and non-squamous NSCLC, respectively. CONCLUSIONS: Nivolumab versus docetaxel is cost effective for treating locally advanced/metastatic NSCLC after prior chemotherapy in adults, regardless of tumour histology or programmed death-ligand 1 expression status.

A Case Study Examining the Usefulness of Cure Modelling for the Prediction of Survival Based on Data Maturity.

INTRODUCTION: Mixture modelling is increasingly being considered where a potential cure leads to a long life. Traditional methods use relative survival models for frail populations or cure models that have improper survival functions with theoretical infinite lifespans. Additionally, much of the work uses population data with long follow-up or theoretical data for method development. OBJECTIVE: This case study uses life table data to create a proper survival function in a real-world clinical trial context. In particular, we discuss the impact of the length of trial follow-up on the accuracy of model estimation and the impact of extrapolation to capture long-term survival. METHODS: A review of recent National Institute for Health and Clinical Excellence (NICE) immuno-oncological and chimeric antigen receptor (CAR) T-cell therapy submissions was performed to assess industry uptake and NICE acceptance of survival analysis methods incorporating the potential for long-term survivorship. The case study analysed a simulated trial-based dataset investigating a curative treatment with long-term mortality based on population life tables. The analysis examined three timepoints corresponding to early trial, end-of-trial follow-up and complete follow-up. Mixture modelling approaches were considered, including both cure modelling and relative survival approaches. The curves were evaluated based on the ability to estimate cure fractions and mean life in years within the time span the models are based on and when extrapolating to capture long-term behaviour. The survival curves were fitted with Weibull distributions using non-mixture and mixture cure models. RESULTS: The performance of the cure modelling methods depended on the relative maturity of the data, indicating that care is needed when deciding when the methods should be applied. For progression-free survival, the cure fraction simulated was 15%. The cure fractions estimated using the traditional mixture cure model were 43% (95% confidence interval [CI] 30-57) at the first analysis time point (40 months), 15% (95% CI 12-20) at the end-of-study follow-up (153 months) and 0% (95% CI 0-100) at the end of follow-up. Other standard cure modelling methods produced similar results. For overall survival, we observed a similar pattern of goodness of fit, with a good fit for the end-of-study follow-up and poor fit for the other two data cuts. However, in this case, the estimate of the cure fraction was below the true value in the first analysis data. CONCLUSIONS: This case study suggests cure modelling works well with data in which the disease-specific events have had time to occur. Care is needed when extrapolating from immature data, and further information should support the estimation rather than relying on statistical estimates based on the trial alone.

Early Access to Medicines Scheme: real-world data collection.

Real-world data (RWD) generated during the pre-approval phase could be supplementary to primary clinical trial outcomes; however, as we discuss here, a data collection framework is needed to ensure the validity and applicability of these data.

The impact of increased post-progression survival on the cost-effectiveness of interventions in oncology.

Purpose: Cost-effectiveness analyses (CEA) of new technologies typically include "background" costs (eg, all "related" health care costs other than the specific technology under evaluation) as well as drug costs. In oncology, these are often expensive. The marginal cost-effectiveness ratio (ie, the extra costs and QALYs associated with each extra period of survival) calculates the ratio of background costs to QALYs during post-progression. With high background costs, the incremental cost-effectiveness ratio (ICER) can become less favorable as survival increases and the ICER moves closer to the marginal cost-effectiveness ratio, making cost-effectiveness prohibitive. This study assessed different methods to determine whether high ICERs are caused by high drug costs, high "background costs" or a combination of both and how different approaches can alter the impact of background costs on the ICER where the marginal cost-effectiveness ratio is close to, or above, the cost-effectiveness threshold. Methods: The National Institute for Health and Care Excellence oncology technology appraisals published or updated between October 2012 and October 2017 were reviewed. A case study was selected, and the CEA was replicated. Three modeling approaches were tested on the case study model. Results: Applying one-off "transition" costs during post-progression reduced the ongoing "incremental" costs of survival, which meant that the marginal cost-effectiveness ratio was substantially reduced and problems associated with additional survival were less likely to impact the ICER. Similarly, the use of two methods of additional utility weighting for end-of-life cases meant that the marginal cost-effectiveness ratio was reduced proportionally, again lessening the impact of increased survival. Conclusion: High ICERs can be caused by factors other than the cost of the drug being assessed. The economic models should be correct and valid, reflecting the true nature of marginal survival. Further research is needed to assess how alternative approaches to the measurement and application of background costs and benefits may provide an accurate assessment of the incremental benefits of life-extending oncology drugs. If marginal survival costs are incorrectly calculated (ie, by summing total post-progressed costs and dividing by the number of baseline months in that state), then the costs of marginal survival are likely to be overstated in economic models.