Modelling the Effectiveness of Tepotinib in Comparison to Standard-of-Care Treatments in Patients with Advanced Non-small Cell Lung Cancer (NSCLC) Harbouring METex14 Skipping in the UK.

BACKGROUND: Patients with non-small cell lung cancer harbouring mesenchymal-epithelial transition exon 14 (METex14) skipping typically demonstrate poorer prognosis than overall non-small cell lung cancer. Until recently, no targeted treatments were available for patients with non-small cell lung cancer harbouring METex14 skipping in the UK, with limited treatments available. OBJECTIVE: This study estimates the long-term survival and quality-adjusted life-year benefit of MET inhibitor tepotinib versus current standard of care from a UK perspective. METHODS: A partitioned-survival model assessed the survival and quality-adjusted life-year benefits of tepotinib versus immunotherapy ± chemotherapy and chemotherapy for untreated and previously treated patients, respectively, using evidence from the single-arm VISION trial (NCT02864992). Two approaches were used to inform an indirect treatment comparison: (1) published clinical trials in overall non-small cell lung cancer and (2) real-world evidence in the METex14 skipping population. Results are presented as median and total quality-adjusted life-year gain and survival for progression-free survival and overall survival. Survival curves were validated against the external literature and uncertainty assessed using a probabilistic sensitivity analysis. RESULTS: Using the indirect treatment comparison against the published literature, tepotinib is estimated to have a median progression-free survival gain versus pembrolizumab ± chemotherapy (11.0 and 9.2 months) in untreated patients, and docetaxel ± nintedanib (5.1 and 6.4 months) in previously treated patients. Across the populations, tepotinib is estimated to have a median survival gain of 15.4 and 9.2 months versus pembrolizumab ± chemotherapy in untreated patients and 12.8 and 5.1 months versus docetaxel ± nintedanib in previously treated patients. The total quality-adjusted life-year gain ranges between 0.56 and 1.17 across the untreated and previously treated populations. Results from the real-world evidence of indirect treatment comparisons are consistent with these findings. CONCLUSIONS: Despite the limitations of the evidence base, the numerous analyses conducted have consistently indicated positive outcomes for tepotinib versus the current standard of care.

Modelling Health State Utilities as a Transformation of Time to Death in Patients with Non-Small Cell Lung Cancer.

BACKGROUND: When utilities are analyzed by time to death (TTD), this has historically been implemented by 'grouping' observations as discrete time periods to create health state utilities. We extended the approach to use continuous functions, avoiding assumptions around groupings. The resulting models were used to test the concept with data from different regions and different country tariffs. METHODS: Five-year follow-up in advanced non-small cell lung cancer (NSCLC) was used to fit six continuous TTD models using generalized estimating equations, which were compared with progression-based utilities and previously published TTD groupings. Sensitivity analyses were performed using only patients with a confirmed death, the last year of life only, and artificially censoring data at 24 months. The statistically best-fitting model was then applied to data subsets by region and different EQ-5D-3L country tariffs. RESULTS: Continuous (natural) [Formula: see text] and [Formula: see text] models outperformed other continuous models, grouped TTD, and progression-based models in statistical fit (mean absolute error and Quasi Information Criterion). This held through sensitivity and scenario analyses. The pattern of reduced utility as a patient approaches death was consistent across regions and EQ-5D tariffs using the preferred [Formula: see text] model. CONCLUSIONS: The use of continuous models provides a statistically better fit than TTD groupings, without the need for strong assumptions about the health states experienced by patients. Where a TTD approach is merited for use in modelling, continuous functions should be considered, with the scope for further improvements in statistical fit by both widening the number of candidate models tested and the therapeutic areas investigated.

Incorporating Prior Beliefs Into Meta-Analyses of Health-State Utility Values Using the Bayesian Power Prior.

OBJECTIVES: Health-state utility values (HSUVs) directly affect estimates of Quality-Adjusted Life-Years and thus the cost-utility estimates. In practice a single preferred value (SPV) is often selected for HSUVs, despite meta-analysis being an option when multiple (credible) HSUVs are available. Nevertheless, the SPV approach is often reasonable because meta-analysis implicitly considers all HSUVs as equally relevant. This article presents a method for the incorporation of weights to HSUV synthesis, allowing more relevant studies to have greater influence. METHODS: Using 4 case studies in lung cancer, hemodialysis, compensated liver cirrhosis, and diabetic retinopathy blindness, a Bayesian Power Prior (BPP) approach is used to incorporate beliefs on study applicability, reflecting the authors' perceived suitability for UK decision making. Older studies, non-UK value sets, and vignette studies are thus downweighted (but not disregarded). BPP HSUV estimates were compared with a SPV, random effects meta-analysis, and fixed effects meta-analysis. Sensitivity analyses were conducted iteratively updating the case studies, using alternative weighting methods, and simulated data. RESULTS: Across all case studies, SPVs did not accord with meta-analyzed values, and fixed effects meta-analysis produced unrealistically narrow CIs. Point estimates from random effects meta-analysis and BPP models were similar in the final models, although BPP reflected additional uncertainty as wider credible intervals, particularly when fewer studies were available. Differences in point estimates were seen in iterative updating, weighting approaches, and simulated data. CONCLUSIONS: The concept of the BPP can be adapted for synthesizing HSUVs, incorporating expert opinion on relevance. Because of the downweighting of studies, the BPP reflected structural uncertainty as wider credible intervals, with all forms of synthesis showing meaningful differences compared with SPVs. These differences would have implications for both cost-utility point estimates and probabilistic analyses.

Health Utility Analysis of Tepotinib in Patients With Non-Small Cell Lung Cancer Harboring MET Exon 14 Skipping.

OBJECTIVES: The VISION trial showed durable activity of tepotinib in MET exon 14 (METex14) skipping non-small cell lung cancer. We analyzed health state utilities using patient-reported outcomes from VISION. METHODS: 5-level version of EQ-5D (EQ-5D-5L) and European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 responses were collected at baseline, every 6 to 12 weeks during treatment, and at the end of treatment and safety follow-up. EQ-5D-5L and European Organisation for Research and Treatment of Cancer Quality of Life Utility Measure-Core 10 Dimensions (QLU-C10D) utilities were derived using United States, Canada, United Kingdom, and Taiwan value sets, where available. Utilities were analyzed with linear mixed models including covariates for progression or time-to-death (TTD). RESULTS: Utilities were derived for 273/291 patients (EQ-5D-5L, 1545 observations; QLU-C10D, 1546 observations). Mean (± SD) US EQ-5D-5L utilities increased after tepotinib initiation, from 0.687 ± 0.287 at baseline to 0.754 ± 0.250 before independently assessed progression, and decreased post progression (0.704 ± 0.288). US QLU-C10D utilities showed similar trends (0.705 ± 0.215, 0.753 ± 0.195, and 0.708 ± 0.209, respectively). Progression-based models demonstrated a statistically significant impact of progression on utilities and predicted higher utilities pre versus post progression. TTD-based models showed statistically significant associations of TTD with utilities and predicted declining utilities as TTD decreased. Prior treatment (yes/no) did not significantly predict utilities in progression- or TTD-based models. Utilities for Canada, United Kingdom, and Taiwan showed comparable trends. CONCLUSIONS: In this first analysis of health state utilities in patients with METex14 skipping non-small cell lung cancer, who received tepotinib, utilities were significantly associated with progression and TTD, but not prior treatment.

A 24-month updated analysis of the comparative effectiveness of ZUMA-5 (axi-cel) vs. SCHOLAR-5 external control in relapsed/refractory follicular lymphoma.

BACKGROUND: In the ZUMA-5 trial (Clinical trials identification: NCT03105336), axicabtagene ciloleucel (axi-cel; a chimeric antigen receptor T-cell therapy) demonstrated high rates of durable response in relapsed/refractory (r/r) follicular lymphoma (FL) patients and clear superiority relative to the SCHOLAR-5 external control cohort. We update this comparison using the ZUMA-5 24-month data. RESEARCH DESIGN AND METHODS: The SCHOLAR-5 cohort is comprised of r/r FL patients who initiated ≥3rd line of therapy after July 2014 and meeting ZUMA-5 eligibility criteria. Groups were balanced for patient characteristics through propensity scoring on prespecified prognostic factors using standardized mortality ratio (SMR) weighting. The overall response rate was compared using a weighted logistic regression. Time-to-event outcomes were evaluated using a Cox regression. RESULTS: For SCHOLAR-5, the sum of weights for the 143 patients was 85 after SMR weighting, versus 86 patients in ZUMA-5. The median follow-up was 29.4 months and 25.4 months for ZUMA-5 and SCHOLAR-5, respectively. The hazard ratios for overall survival and progression-free survival were 0.52 (95% confidence interval (CI): 0.28-0.95) and 0.28 (95% CI: 0.17-0.45), favoring axi-cel. CONCLUSION: This updated analysis, using a longer minimum follow-up than a previously published analysis, shows that the improved efficacy of axi-cel, relative to available therapies, in r/r FL is durable. .

Treatment patterns and outcomes in relapsed/refractory follicular lymphoma: results from the international SCHOLAR-5 study.

The SCHOLAR-5 study examines treatment patterns and outcomes of real-world follicular lymphoma (FL) patients on 3rd line of treatment (LoT) or higher, for whom existing data are limited. SCHOLAR-5 is a retrospective cohort study using data from adults (≥ 18 years) with grade 1-3a FL, initiating ≥3rd LoT after June 2014 at major lymphoma centers in the US and Europe. Objective response rate (ORR), complete response (CR), progression-free survival (PFS) and overall survival (OS) were analyzed by LoT. Time-to-event outcomes were assessed using Kaplan-Meier methods. Of 128 patients, 87 initiated 3rd LoT, 63 initiated 4th LoT, and 47 initiated 5th LoT. At 1st eligible LoT, 31% progressed within 24-months of 1st LoT anti-CD20 combination therapy, 28% had prior autologous stem cell transplantation, and 31% were refractory to the previous LoT. The most common regimen in each LoT was chemoimmunotherapy; however, experimental drugs were increasingly used at later LoT. In the US, anti-CD20 monotherapy was more common at ≥3rd LoT compared to Europe, where stem cell transplants were more common. ORR at 3rd LoT was 68% (CR 44%), but decreased after each LoT to 37% (CR 22%) in ≥5 LoT. Median OS and PFS at 3rd LoT were 68 and 11 months, respectively, and reduced to 43 and 4 months at ≥5 LoT. Treatments were heterogenous at each LoT in both the US and Europe. Few FL patients achieved CR in later LoT, and duration of response and survival diminished with each subsequent line.

Comparative effectiveness of ZUMA-5 (axi-cel) vs SCHOLAR-5 external control in relapsed/refractory follicular lymphoma.

In the pivotal ZUMA-5 trial, axicabtagene ciloleucel (axi-cel; an autologous anti-CD19 chimeric antigen receptor T-cell therapy) demonstrated high rates of durable response in relapsed/refractory (r/r) follicular lymphoma (FL) patients. Here, outcomes from ZUMA-5 are compared with the international SCHOLAR-5 cohort, which applied key ZUMA-5 trial eligibility criteria simulating randomized controlled trial conditions. SCHOLAR-5 data were extracted from institutions in 5 countries, and from 1 historical clinical trial, for r/r FL patients who initiated a third or higher line of therapy after July 2014. Patient characteristics were balanced through propensity scoring on prespecified prognostic factors using standardized mortality ratio (SMR) weighting. Time-to-event outcomes were evaluated using weighted Kaplan-Meier analysis. Overall response rate (ORR) and complete response (CR) rate were compared using weighted odds ratios. The 143 ScHOLAR-5 patients reduced to an effective sample of 85 patients after SMR weighting vs 86 patients in ZUMA-5. Median follow-up time was 25.4 and 23.3 months for SCHOLAR-5 and ZUMA-5. Median overall survival (OS) and progression-free survival (PFS) in SCHOLAR-5 were 59.8 months and 12.7 months and not reached in ZUMA-5. Hazard ratios for OS and PFS were 0.42 (95% confidence interval [CI], 0.21-0.83) and 0.30 (95% CI, 0.18-0.49). The ORR and CR rate were 49.9% and 29.9% in SCHOLAR-5 and 94.2% and 79.1% in ZUMA-5, for odds ratios of 16.2 (95% CI, 5.6-46.9) and 8.9 (95% CI, 4.3-18.3). Compared with available therapies, axi-cel demonstrated an improvement in meaningful clinical endpoints, suggesting axi-cel addresses an important unmet need for r/r FL patients. This trial was registered at www.clinicaltrials.gov as #NCT03105336.

What is the Impact of the Analysis Method Used for Health State Utility Values on QALYs in Oncology? A Simulation Study Comparing Progression-Based and Time-to-Death Approaches.

BACKGROUND: Health state utility values ('utilities') are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed. METHODS: Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a 'combination approach'. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area. RESULTS: %ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the 'combined' UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up. CONCLUSIONS: We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced.

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies.

BACKGROUND: While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. METHODS: We discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. RESULTS: A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. CONCLUSION: Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.

Real-world evidence use in assessments of cancer drugs by NICE.

OBJECTIVE: To establish how real-world evidence (RWE) has been used to inform single technology appraisals (STAs) of cancer drugs conducted by the National Institute for Health and Care Excellence (NICE). METHODS: STAs published by NICE from April 2011 to October 2018 that evaluated cancer treatments were reviewed. Information regarding the use of RWE to directly inform the company-submitted cost-effectiveness analysis was extracted and categorized by topic. Summary statistics were used to describe emergent themes, and a narrative summary was provided for key case studies. RESULTS: Materials for a total of 113 relevant STAs were identified and analyzed, of which nearly all (96 percent) included some form of RWE within the company-submitted cost-effectiveness analysis. The most common categories of RWE use concerned the health-related quality of life of patients (71 percent), costs (46 percent), and medical resource utilization (40 percent). While sources of RWE were routinely criticized as part of the appraisal process, we identified only two cases where the use of RWE was overtly rejected; hence, in the majority of cases, RWE was accepted in cancer drug submissions to NICE. DISCUSSION: RWE has been used extensively in cancer submissions to NICE. Key criticisms of RWE in submissions to NICE are seldom regarding the use of RWE in general; instead, these are typically concerned with specific data sources and the applicability of these to the decision problem. Within an appropriate context, RWE constitutes an extremely valuable source of information to inform decision making; yet the development of best practice guidelines may improve current reporting standards.

The Effects of Model Misspecification in Unanchored Matching-Adjusted Indirect Comparison: Results of a Simulation Study.

OBJECTIVES: To assess the performance of unanchored matching-adjusted indirect comparison (MAIC) by matching on first moments or higher moments in a cross-study comparisons under a variety of conditions. A secondary objective was to gauge the performance of the method relative to propensity score weighting (PSW). METHODS: A simulation study was designed based on an oncology example, where MAIC was used to account for differences between a contemporary trial in which patients had more favorable characteristics and a historical control. A variety of scenarios were then tested varying the setup of the simulation study, including violating the implicit or explicit assumptions of MAIC. RESULTS: Under ideal conditions and under a variety of scenarios, MAIC performed well (shown by a low mean absolute error [MAE]) and was unbiased (shown by a mean error [ME] of about zero). The performance of the method deteriorated where the matched characteristics had low explanatory power or there was poor overlap between studies. Only when important characteristics are not included in the matching did the method become biased (nonzero ME). Where the method showed poor performance, this was exaggerated if matching was also performed on the variance (ie, higher moments). Relative to PSW, MAIC provided similar results in most circumstances, although it exhibited slightly higher MAE and a higher chance of exaggerating bias. CONCLUSIONS: MAIC appears well suited to adjust for cross-trial comparisons provided the assumptions underpinning the model are met, with relatively little efficiency loss compared with PSW.

Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection.

BACKGROUND: Due to limited duration of follow up in clinical trials of cancer treatments, estimates of lifetime survival benefits are typically derived using statistical extrapolation methods. To justify the method used, a range of approaches have been proposed including statistical goodness-of-fit tests and comparing estimates against a previous data cut (i.e. interim data collected). In this study, we extend these approaches by presenting a range of extrapolations fitted to four pre-planned data cuts from the JAVELIN Merkel 200 (JM200) trial. By comparing different estimates of survival and goodness-of-fit as JM200 data mature, we undertook an iterative process of fitting and re-fitting survival models to retrospectively identify early indications of likely long-term survival. METHODS: Standard and spline-based parametric models were fitted to overall survival data from each JM200 data cut. Goodness-of-fit was determined using an assessment of the estimated hazard function, information theory-based methods and objective comparisons of estimation accuracy. Best-fitting extrapolations were compared to establish which one provided the most accurate estimation, and how statistical goodness-of-fit differed. RESULTS: Spline-based models provided the closest fit to the final JM200 data cut, though all extrapolation methods based on the earliest data cut underestimated the 'true' long-term survival (difference in restricted mean survival time [RMST] at 36 months: - 1.1 to - 0.5 months). Goodness-of-fit scores illustrated that an increasingly flexible model was favored as data matured. Given an early data cut, a more flexible model better aligned with clinical expectations could be reasonably justified using a range of metrics, including RMST and goodness-of-fit scores (which were typically within a 2-point range of the statistically 'best-fitting' model). CONCLUSIONS: Survival estimates from the spline-based models are more aligned with clinical expectation and provided a better fit to the JM200 data, despite not exhibiting the definitively 'best' statistical goodness-of-fit. Longer-term data are required to further validate extrapolations, though this study illustrates the importance of clinical plausibility when selecting the most appropriate model. In addition, hazard-based plots and goodness-of-fit tests from multiple data cuts present useful approaches to identify when a more flexible model may be advantageous. TRIAL REGISTRATION: JAVELIN Merkel 200 was registered with ClinicalTrials.gov as NCT02155647 on June 4, 2014.

The validation of published utility mapping algorithms: an example of EORTC QLQ-C30 and EQ-5D in non-small cell lung cancer.

BACKGROUND: Mapping algorithms can be used to generate health state utilities when a preference-based instrument is not included in a clinical study. Our aim was to investigate the external validity of published mapping algorithms in non-small cell lung cancer (NSCLC) between the EORTC QLQ-C30 and EQ-5D instruments and to propose methodology for validating any mapping algorithms. METHODS: We conducted a targeted literature review to identify published mappings, then applied these to data from the osimertinib clinical trial programme. Performance of the algorithms was evaluated using the mean absolute error, root mean squared error, and graphical techniques for the observed versus predicted EQ-5D utilities. These statistics were also calculated across the range of utility values (as well as ordinary least squares and quantile regression), to investigate how the mappings fitted across all values, not simply around the mean utility. RESULTS: Three algorithms developed in NSCLC were identified. The algorithm based on response mapping (Young et al., 2015) fitted the validation dataset across the range of observed values with similar fit statistics to the original publication (overall MAE of 0.087 vs 0.134). The two algorithms based on beta-binomial models presented a poor fit to both the mean and distribution of utility values (MAE 0.176, 0.178). CONCLUSIONS: The validation of mapping algorithms is key to demonstrating their generalisability beyond the original dataset, particularly across the range of plausible utility values (not just the mean) - perceived patient similarity being insufficient. The identified algorithm from Young et al. performed well across the range of EORTC scores observed, and thus appears most suitable for use in other studies of NSCLC patients.

Creating historical controls using data from a previous line of treatment - Two non-standard approaches.

Where medical interventions are licensed based on only uncontrolled study data (for example a single-arm trial), a common approach for estimating the incremental benefit is to compare the treatment to a 'historical control'; data collected from patients who did not receive the intervention. We illustrate with motivating examples two methods for the creation of historical controls where disease progression and overall survival are typically the key clinically meaningful endpoints. The first method utilises information routinely collected in a clinical trial programme: patients' time to disease progression on their previous line of treatment against which outcomes can be compared. The second uses published clinical outcomes for the prior line of treatment which can be extrapolated to estimate outcomes at the next line. As examples we use two pharmaceuticals licensed on the basis of uncontrolled clinical studies - idelalisib for double-refractory follicular lymphoma and ofatumumab for double-refractory chronic lymphocytic leukemia. Although subject to limitations that should be considered on a case-by-case basis, the methods may be appropriate when trying to quantify the clinical benefit of treatment based on limited and uncontrolled trial data. As a result, the methods can be used to inform health technology adoption decisions.

Frequentist and Bayesian meta-regression of health state utilities for multiple myeloma incorporating systematic review and analysis of individual patient data.

This analysis presents the results of a systematic review for health state utilities in multiple myeloma, as well as analysis of over 9,000 observations taken from registry and trial data. The 27 values identified from 13 papers are then synthesised in a frequentist nonparametric bootstrap model and a Bayesian meta-regression. Results were similar between the frequentist and Bayesian models with low utility on disease diagnosis (approximately 0.55), raising to approximately 0.65 on first line treatment and declining slightly with each subsequent line. Stem cell transplant was also found to be a significant predictor of health-related quality of life in both individual patient data and meta-regression, with an increased utility of approximately 0.06 across different models. The work presented demonstrates the feasibility of Bayesian methods for utility meta-regression, whilst also presenting an internally consistent set of data from the analysis of registry data. To facilitate easy updating of the data and model, data extraction tables and model code are provided as Data S1. The main limitations of the model relate to the low number of studies available, particularly in highly pretreated patients.

Cost Effectiveness of Avelumab for Metastatic Merkel Cell Carcinoma.

BACKGROUND: Metastatic Merkel cell carcinoma (mMCC) is a rare and aggressive skin cancer. Until recently, there were no licensed treatment options for patients with mMCC, and prognosis was poor. A cost-effectiveness analysis was conducted for avelumab, a newly available treatment option for mMCC, versus standard care (SC), from a UK National Health Service perspective. METHODS: A partitioned survival model was developed to assess the lifetime costs and effects of avelumab versus SC. Data from the JAVELIN Merkel 200 trial (NCT02155647) were used to inform estimates of quality-adjusted life-years (QALYs). Unit costs and associated frequencies of use were informed by published literature and clinical expert opinion. Results were presented as incremental cost-effectiveness ratios (ICERs, i.e. the cost per QALY gained) for treatment-experienced (TE) and treatment-naïve (TN) patients. Uncertainty was explored through a range of sensitivity analyses. RESULTS: Discounting costs and QALYs at 3.5% per annum, avelumab was associated with ICERs of £35,274 (TE)/£39,178 (TN) per QALY gained. Probabilistic sensitivity analysis results demonstrated that avelumab was associated with an 88.3% (TE)/69.3% (TN) probability of being cost effective at a willingness-to-pay threshold for end-of-life treatments of £50,000 per QALY gained. Results were most sensitive to alternative survival extrapolations and dosing assumptions. CONCLUSIONS: The analysis results suggest that avelumab is likely to be a cost-effective treatment option for UK mMCC patients. The results for TN patients are subject to some uncertainty, and a confirmatory analysis will be conducted with more mature data.

Probabilistic Sensitivity Analysis in Cost-Effectiveness Models: Determining Model Convergence in Cohort Models.

Probabilistic sensitivity analysis (PSA) demonstrates the parameter uncertainty in a decision problem. The technique involves sampling parameters from their respective distributions (rather than simply using mean/median parameter values). Guidance in the literature, and from health technology assessment bodies, on the number of simulations that should be performed suggests a 'sufficient number', or until 'convergence', which is seldom defined. The objective of this tutorial is to describe possible outcomes from PSA, discuss appropriate levels of accuracy, and present guidance by which an analyst can determine if a sufficient number of simulations have been conducted, such that results are considered to have converged. The proposed approach considers the variance of the outcomes of interest in cost-effectiveness analysis as a function of the number of simulations. A worked example of the technique is presented using results from a published model, with recommendations made on best practice. While the technique presented remains essentially arbitrary, it does give a mechanism for assessing the level of simulation error, and thus represents an advance over current practice of a round number of simulations with no assessment of model convergence.

Cost-effectiveness of Trifluridine/tipiracil for Previously Treated Metastatic Colorectal Cancer in England and Wales.

BACKGROUND: Treatment options at third-line and beyond for patients with late-line metastatic colorectal cancer (mCRC) are limited, and outcomes are poor with best supportive care (BSC). This study investigated the cost-effectiveness of trifluridine/tipiracil and regorafenib relative to BSC alone in patients with mCRC who have been previously treated with, or are not considered candidates for, standard chemotherapies. MATERIALS AND METHODS: A partitioned survival model was constructed to assess the lifetime costs and benefits accrued by patients. Clinical data were derived from the pivotal phase III (Randomized, Double-Blind, Phase 3 Study of TAS-102 plus Best Supportive Care [BSC] versus Placebo plus BSC in Patients with Metastatic Colorectal Cancer Refractory to Standard Chemotherapies [RECOURSE]) and supporting phase II (J003-10040030) randomized controlled trial of trifluridine/tipiracil + BSC versus placebo + BSC, as well as the phase III Colorectal Cancer Treated With Regorafenib or Placebo After Failure of Standard Therapy (CORRECT) randomized controlled trial of regorafenib, and were extrapolated to estimate lifetime outcomes. Costs were taken from published sources, and health effects sourced from previous mCRC studies. RESULTS: Trifluridine/tipiracil was associated with a 0.27 incremental life year versus BSC alone, which corresponds to a 0.17 quality-adjusted life year gain. The incremental cost of treatment with trifluridine/tipiracil was £8,479, resulting in an incremental cost-effectiveness ratio of £51,194 per quality-adjusted life year gained. Trifluridine/tipiracil was shown to dominate regorafenib (improve outcomes with reduced costs). Sensitivity analyses showed principal areas of uncertainty were survival estimates and patient utility. CONCLUSIONS: The results show that trifluridine/tipiracil is more clinically and cost-effective than regorafenib, with clinical outcomes greatly exceeding those for patients treated with BSC alone. Based on the results of the analysis, trifluridine/tipiracil offers an important new treatment option for patients with mCRC maintaining good performance status at the end of life.

Estimating outcomes and cost effectiveness using a single-arm clinical trial: ofatumumab for double-refractory chronic lymphocytic leukemia.

BACKGROUND: Ofatumumab (Arzerra®, Novartis) is a treatment for chronic lymphocytic leukemia refractory to fludarabine and alemtuzumab [double refractory (DR-CLL)]. Ofatumumab was licensed on the basis of an uncontrolled Phase II study, Hx-CD20-406, in which patients receiving ofatumumab survived for a median of 13.9 months. However, the lack of an internal control arm presents an obstacle for the estimation of comparative effectiveness. METHODS: The objective of the study was to present a method to estimate the cost effectiveness of ofatumumab in the treatment of DR-CLL. As no suitable historical control was available for modelling, the outcomes from non-responders to ofatumumab were used to model the effect of best supportive care (BSC). This was done via a Cox regression to control for differences in baseline characteristics between groups. This analysis was included in a partitioned survival model built in Microsoft® Excel with utilities and costs taken from published sources, with costs and quality-adjusted life years (QALYs) were discounted at a rate of 3.5% per annum. RESULTS: Using the outcomes seen in non-responders, ofatumumab is expected to add approximately 0.62 life years (1.50 vs. 0.88). Using published utility values this translates to an additional 0.30 QALYs (0.77 vs. 0.47). At the list price, ofatumumab had a cost per QALY of £130,563, and a cost per life year of £63,542. The model was sensitive to changes in assumptions regarding overall survival estimates and utility values. CONCLUSIONS: This study demonstrates the potential of using data for non-responders to model outcomes for BSC in cost-effectiveness evaluations based on single-arm trials. Further research is needed on the estimation of comparative effectiveness using uncontrolled clinical studies.