Value of Reducing Wait Times for Chimeric Antigen Receptor T-Cell Treatment: Evidence From Randomized Controlled Trial Data on Tisagenlecleucel for Diffuse Large B-Cell Lymphoma

ObjectivesThis study aimed to quantify the value of reducing chimeric antigen receptor T-cell (CAR-T) treatment wait times on patients with refractory and relapsed aggressive blood cancer who can newly gain access to treatment or access treatment earlier in their disease course.MethodsUsing data from the JULIET clinical trial, we first identified the number of additional patients with diffuse large B-cell lymphoma that would have been treated with tisagenlecleucel CAR-T therapy if wait times were shortened. For these patients, we estimated mortality benefits using literature estimates of CAR-T effectiveness. Next, among patients who already received CAR-T, we estimated tumor burden progression over time using a linear probability regression model. The primary outcome variable was an indicator for having above-normal lactate dehydrogenase, and we controlled for time, use of bridging therapy, and time-invariant patient characteristics. The regression results, along with literature estimates relating lactate dehydrogenase to CAR-T effectiveness, were used to compute the survival benefits of earlier CAR-T treatment.ResultsReducing wait times by 2 months increased the number of eligible patients receiving CAR-T by at least 10.7%. For patients already receiving tisagenlecleucel CAR-T, a 2-month reduction in wait times generated a 3.3% increase in survival gains per treated patient. Thus, among patients seeking treatment, the combined treatment efficacy increased by 14%, with approximately one-quarter of survival benefits accruing to existing patients receiving faster treatment.ConclusionsDelays affected not only access to CAR-T treatments but also treatment effectiveness. Our results highlight the survival benefits of expediting treatment access and may help explain some observed differences in CAR-T effectiveness across countries.     

A Systematic Review of Health Technology Assessments of Chimeric Antigen Receptor T-Cell Therapies in Young Compared With Older Patients

ObjectivesThe objective of this review was to identify sources of variability in cost-effectiveness analyses of chimeric antigen receptor T-cell (CAR-T) therapies, tisagenlecleucel and axicabtagene ciloleucel, evaluated by health technology assessment (HTA) agencies, focusing on young compared with older patients.MethodsHTA evaluations in pediatric acute lymphoblastic leukemia (ALL) and adult diffuse large B-cell lymphoma (DLBCL) were included from Australia, Canada, England, Norway, and the United States. Key clinical evidence, economic approach, and outcomes (costs, quality-adjusted life-years [QALYs] and incremental cost-effectiveness ratios) were summarized.ResultsFourteen HTA evaluations were identified (5 ALL, 9 DLBCL [4 tisagenlecleucel, 5 axicabtagene]). Analyses were naive comparisons of prospective single-arm studies for the CAR-Ts with retrospective cohort studies for the comparators. Key clinical evidence and economic model approaches were generally consistent by CAR-T and indication, although outcomes varied. Notably, incremental QALYs varied substantially in ALL (3.67-10.6 QALYs gained), whereas variation in DLBCL was less (1.21-1.97 [tisagenlecleucel], 1.97-3.40 [axicabtagene]). Discounting of costs and outcomes varied, with the highest QALYs generated for tisagenlecleucel in ALL (10.95) associated with the lowest discount rate (1.5%) and vice versa (4.97 QALYs; 5% discount rate). The approach to extrapolation of overall survival data varied, even where the same empirical data were used.ConclusionModeled, long-term treatment benefit in young patients may be associated with greater uncertainty compared with adults because of potential life-long benefits with cell and gene therapies. This reflects the methodological challenges identified by HTA agencies associated with single-arm, short-term studies.     

The Extrapolation Performance of Survival Models for Data With a Cure Fraction: A Simulation Study

ObjectivesCurative treatments can result in complex hazard functions. The use of standard survival models may result in poor extrapolations. Several models for data which may have a cure fraction are available, but comparisons of their extrapolation performance are lacking. A simulation study was performed to assess the performance of models with and without a cure fraction when fit to data with a cure fraction.MethodsData were simulated from a Weibull cure model, with 9 scenarios corresponding to different lengths of follow-up and sample sizes. Cure and noncure versions of standard parametric, Royston-Parmar, and dynamic survival models were considered along with noncure fractional polynomial and generalized additive models. The mean-squared error and bias in estimates of the hazard function were estimated.ResultsWith the shortest follow-up, none of the cure models provided good extrapolations. Performance improved with increasing follow-up, except for the misspecified standard parametric cure model (lognormal). The performance of the flexible cure models was similar to that of the correctly specified cure model. Accurate estimates of the cured fraction were not necessary for accurate hazard estimates. Models without a cure fraction provided markedly worse extrapolations.ConclusionsFor curative treatments, failure to model the cured fraction can lead to very poor extrapolations. Cure models provide improved extrapolations, but with immature data there may be insufficient evidence to choose between cure and noncure models, emphasizing the importance of clinical knowledge for model choice. Dynamic cure fraction models were robust to model misspecification, but standard parametric cure models were not.     

Value of External Data in the Extrapolation of Survival Data: A Study Using the NJR Data Set

Background

Extrapolation of time-to-event data can be a critical component of cost-effectiveness analysis.

Parametric Survival Extrapolation of Early Survival Data in Economic Analyses: A Comparison of Projected Versus Observed Updated Survival

ObjectivesTo establish the value of cancer drugs by cost-effectiveness analysis, lifetime parametric survival extrapolations are often fitted to early data. Recent literature suggests that the benefit of cancer agents in primary publications is often different compared with updated data. This study aimed to examine the projected survival based on parametric extrapolations compared with observed survival based on updated data.MethodsUS Food and Drug Administration oncology approvals from January 2006 to December 2015 were reviewed to identify randomized controlled trials, with updated overall survival (OS) or progression-free survival (PFS) data within 5 years. Individual patient data were reconstructed using established methods on initial and updated publications. Projected survival was calculated as the best-fit parametric restricted mean survival time (RMST) based on extrapolated initial Kaplan-Meier curves whereas observed survival was calculated as observed RMST based on updated Kaplan-Meier curves. Mean deviations, mean absolute error (MAE), mean absolute percentage error, and linear regressions were conducted to examine the relationship between projected and observed survival.ResultsIn total, 32 randomized controlled trials were included. The MAE between the projected RMST and observed RMST was 3.18 months (OS) and 2.84 months (PFS) and absolute percentage error of 100% (OS) and 23% (PFS), suggesting substantial imprecision of the projected RMST in predicting the updated RMST. The linear regression indicated MAE increased as time extrapolated and as the percentage of censored patients increased.ConclusionsThis study demonstrated substantial difference in projected survival between initial and updated publications. Health technology assessment committees need to be aware of the potential uncertainty of incremental effectiveness and resultant value-for-money assessment when making reimbursement decisions based on initial publications with immature survival data.     

Estimating and Extrapolating Survival Using a State-Transition Modeling Approach: A Practical Application in Multiple Myeloma

ObjectivesState-transition models (STMs) applied in oncology have given limited considerations to modeling postprogression survival data. This study presents an application of an STM focusing on methods to evaluate the postprogression transition and its impact on survival predictions.MethodsData from the lenalidomide plus dexamethasone arm of the ASPIRE trial was used to estimate transition rates for an STM. The model accounted for the competing risk between the progression and preprogression death events and included an explicit structural link between the time to progression and subsequent death. The modeled transition rates were used to simulate individual disease trajectories in a discrete event simulation framework, based on which progression-free survival and overall survival over a 30-year time horizon were estimated. Survival predictions were compared with the observed trial data, matched external data, and estimates obtained from a more conventional partitioned survival analysis approach.ResultsThe rates of progression and preprogression death were modeled using piecewise exponential functions. The rate of postprogression mortality was modeled using an exponential function accounting for the nonlinear effect of the time to progression. The STM provided survival estimates that closely fitted the trial data and gave more plausible long-term survival predictions than the best-fitting Weibull model applied in a partitioned survival analysis.ConclusionsThe fit of the STM suggested that the modeled transition rates accurately captured the underlying disease process over the modeled time horizon. The considerations of this study may apply to other settings and facilitate a wider use of STMs in oncology.     

Developing Markov Models From Real-World Data: A Case Study of Heart Failure Modeling Using Administrative Data

ObjectivesMarkov models characterize disease progression as specific health states based on clinical or biological measures. However, these measures are not always collected outside clinical trials. In this article, an alternative approach is presented that uses real-world data to define the health states and to model transitions between them, specific to a local setting, to estimate the cost-effectiveness of telemonitoring (TM) versus no TM for heart failure.MethodsThe incidence of hospitalization for usual care was estimated from hospital episode statistics (HES) data in the United Kingdom and converted into a monthly transition matrix with 5 health states (4 states are defined based on the number of hospitalizations in the previous year and death) to estimate the cost-effectiveness of TM in a local UK primary care trust (PCT) using probabilistic sensitivity analysis from a healthcare perspective.ResultsGeographical variation in hospitalization rates were present, which led to different health state transition matrices in different localities. In the PCT that was evaluated, TM accrued mean additional costs of £3610 and 0.075 additional quality-adjusted life-years (QALYs) compared with usual care per patient, resulting in a mean incremental cost effectiveness ratio of £48 172/QALY.ConclusionsThe use of administrative data to define health states and transition matrices based on health service events is feasible, and TM was not cost-effective in our analysis. Given the increasing emphasis on using real-world evidence, it is likely that these approaches will be used more in the future.     

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

ObjectivesThere 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.MethodsData 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.ResultsCompared 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.ConclusionsThe 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.