Introduction
Five chimeric antigen receptor (CAR) T cell therapies have been approved by the United States (US) Food and Drug Administration (FDA) for relapsed or refractory (R/R) diffuse large B cell lymphoma (DLBCL) (axicabtagene ciloleucel [“axi-cel”; Yescarta] [
1], tisagenlecleucel [“tis-cel”; Kymriah] [
2], lisocabtagene maraleucel [Breyanzi] [
3]), B cell precursor acute lymphoblastic leukemia (ALL) (tis-cel) [
2], R/R mantle cell lymphoma (brexucabtagene autoleucel [Tecartus] [
4]), and most recently idecabtagene vicleucel (Abecma) for R/R multiple myeloma [
5]. Additionally, one further CAR-T therapy is in development for R/R multiple myeloma (ciltacabtagene autoleucel [“cilta-cel”]) [
6,
7], while several others are in earlier stage development for hematologic cancers and solid tumors [
8]. The approved CAR-T therapies as well as those therapies in development have the potential to cure a proportion of patients receiving these novel agents, with “cure” statistically defined as the observed plateauing of survival data (i.e., progression-free survival [PFS], overall survival [OS]) some years after diagnosis [
9‐
11].
Industry stakeholders including public and private payers, manufacturers, academics, health service researchers, and health technology assessment (HTA) agencies have a vested interest in monitoring the value of new CAR-T therapies over time. For anticancer therapies, clinical value is often measured in terms of survival benefits defined by incremental gains in life expectancy (i.e., life years [LYs]) compared to alternative sources of care. To estimate clinical value, industry researchers rely on the best available clinical data (i.e., PFS, OS, response rates) at the time of analysis. Mature clinical data with years of follow-up duration, obtained either through clinical trial extensions or real-world data following product launch and uptake, offer a robust method for estimating survival benefits given the observational nature of the long-term data. In particular, findings from phase 3 clinical trials are important for confidently confirming the survival benefit identified in short-term and/or single cohort phase 2 clinical trials. However, in the absence of mature or confirmatory clinical data, industry researchers project survival benefits beyond the clinical trial duration by using various statistical methods to extrapolate short-term survival data to longer periods including the patient’s lifetime.
Partitioned-survival modeling, via standard parametric survival functions, has often been used to extrapolate short-term survival data (PFS, OS) of anticancer therapies [
12]. Traditional techniques for estimating survival assume the same mortality rate for all patients, which is appropriate for anticancer therapies that extend life but do not necessarily offer the potential for cure. However, assuming one shared value for mortality across “cured” and noncured patients may lead to an underestimation of the effectiveness of treatment for potentially curative therapies [
13]. In response to this potential limitation to standard parametric survival estimation, researchers have explored several advanced extrapolation techniques to account for the potentially curative nature of novel treatments, including CAR-T therapies. For instance, cure fraction models, including the mixture cure fraction model (or mixture cure model [MCM]) and the non-mixture cure fraction model (or non-mixture cure model [NMCM], have been used as an alternative approach to modeling the heterogeneity between long-term survivors (i.e., cured patients) and those who are not (i.e., non-cured patients) [
14,
15]. MCMs assume that a proportion of patients are cured (i.e., long-term survivors) and thus are not at risk of experiencing the event of interest (e.g., progression or death), while assuming that the remaining proportion of non-cured patients will eventually experience the event of interest [
9,
10]. In this regard, cured patients are assigned background mortality rates similar or equal to the general population while non-cured patients are assigned an additional disease-specific mortality risk. NMCMs are based on the assumption that, following anticancer therapy, cancer cells may remain in the body and may slowly grow over time producing a relapse of disease; techniques of this type allow the researcher to scale the survival curve until the plateau (e.g., cure fraction) is reached [
16]. Another example of an advanced extrapolation technique includes flexible parametric or spline models which do not explicitly introduce a cure fraction in the way that a cure approach would; instead, patients who are still alive after a particular point in time (e.g., 5 years) are assumed to be effectively cured and have a similar or equivalent mortality to the general population for the remainder of the model horizon [
17,
18]. The point at which patients are deemed to be cured are termed “knots.” Goodness-of-fit statistics (i.e., Akaike information criterion [AIC], Bayesian information criterion [BIC]) indicate how many knots, and at which time point, fit best to the primary intervention’s OS and PFS data.
Depending on the extrapolation technique used, an intervention’s survival benefit may substantially vary. In an analysis of long-term survival and cost-effectiveness (CE) associated with axi-cel for the treatment of B cell lymphoma, Whittington et al. used five different survival extrapolation techniques, including standard parametric, flexible parametric, two mixture cure, and flexible parametric mixture extrapolation techniques, to project short-term survival data from clinical trials to a lifetime horizon [
19]. The authors found that incremental LYs ranged from 1.89 to 5.82; the smallest difference in LYs was found using the standard parametric approach while the largest difference in LYs was found using an MCM approach, suggesting an underestimation of incremental LYs using traditional techniques. Similarly, Bansal et al. compared survival outcomes using standard parametric techniques to an MCM approach among patients treated with axi-cel for R/R large B cell lymphoma [
20]. The authors determined that the use of standard survival models yielded overall survival estimates of 2.0 years (Weibull distribution) and 3.0 years (generalized gamma distribution) compared to 15.7 years using MCMs, providing further evidence that traditional extrapolation techniques have the potential to underestimate projections on long-term survival.
In addition to the availability of different statistical options for researchers, there appears to be little consensus in the literature, along with a lack of statistical guidelines from professional societies, regarding which statistical method should be used to extrapolate survival of potentially curative therapies. As a result, it is important to ascertain research trends in survival extrapolation methods for the purpose of advancing the field of comparative effectiveness research of CAR-T therapies. Doing so will aid in creating a consistent model framework for use and interpretation by industry stakeholders when evaluating a CAR-T therapy’s long-term value. Consistency in statistical analytics is a critical consideration since different extrapolation techniques yield different model results and different product valuations, which could have downstream impacts on payer formulary decisions and patient access to care. While some publications have reviewed and compared survival extrapolation techniques for immuno-oncologic therapies [
13,
21‐
31], none have evaluated survival extrapolation trends across the full range of publications (i.e., peer-reviewed or conference proceedings) nor global HTA agencies specifically for CAR-T therapies. To this end, the goal of this research was to conduct a systematic literature review of analyses projecting the survival benefits of CAR-T therapies over time, in an effort to ascertain research trends in survival extrapolation techniques used and the rationale for selecting advanced techniques.
Methods
A global systematic literature search was conducted to identify trends in survival extrapolation techniques associated with estimating the survival benefits of CAR-T therapies. The review was conducted in accordance with PRISMA and CHEERS guidelines for systematic literature reviews [
32,
33]. Publications were sourced from MEDLINE (PubMed), scientific conference databases and publications, and health technology assessment (HTA) agencies. A comprehensive description of the full set of search terms used for this study can be found in Tables 1–3 in the supplementary material.
PubMed was searched from January 1, 2015 to December 14, 2020; the start date of January 1, 2015 was selected to allow for inclusion of all relevant survival extrapolation-based analyses published in support of CAR-T therapies as the first CAR-T therapy was approved in the USA in 2017. The search terms implemented in PubMed included a combination of keywords and MeSH terms related to CAR-T therapies and analyses that predominantly utilize survival extrapolation techniques; for this reason, focus was given to comparative effectiveness research relating to CE analyses. Included studies were limited to those written in the English language (or with an available English translation) and with the availability of full text via PubMed filters. Overall inclusion criteria required studies to evaluate CAR-T therapies in a human population, incorporate a modeling component such as Markov cycles or survival curve extrapolation, be an economic evaluation and/or use LYs or QALYs as part of the primary outcome (e.g., incremental cost per LY gained). Additionally, CE analyses that do not extrapolate clinical trial data beyond the maximum follow-up duration were deemed inapplicable to this qualitative evaluation of extrapolation techniques used.
In addition, hematologic and health economic and outcomes research conferences were searched from 2018 to 2020. Conferences included the American Society of Hematology (ASH) Annual Meeting, European Hematology Association (EHA) Annual Meeting, American Society of Clinical Oncology (ASCO) Annual Meeting and Virtual Scientific Meetings, and all International Society of Pharmacoeconomics and Outcomes Research (ISPOR) meetings. Abstracts and posters were included in our detailed review provided that information concerning the survival extrapolation technique (e.g., MCM, spline-based, standard parametric) was specified.
Finally, targeted searches of the HTAs were conducted to identify the most prominent and relevant CE models from agencies such as the National Institute for Health and Care Excellence (NICE) (UK), Canadian Agency for Drugs and Technologies in Health (CADTH) (Canada), Medical Services Advisory Committee (MSAC) (Australia), Independent Institute for Quality and Efficiency in Health Care (IQWIG) (Germany), and Institute for Clinical and Economic Review (ICER) (US), as well as any other CE models that were recommended by industry experts. Global HTA bodies are often at the forefront of methodological techniques for the purpose of comparative effectiveness, and CE, analyses since reimbursement decisions often rely on their guidance. Evaluating the methods employed and/or endorsed by global HTA bodies is critical to identifying methodological trends in this area. Assessments were included if they evaluated a CAR-T therapy, regardless of publication date. Key search terms included “chimeric antigen”, “tisagenlecleucel”, “axicabtagene”, “lisocabtagene”, and “brexucabtagene”; ongoing assessments of these therapies were excluded since the extrapolation technique(s) applied in the analyses have not yet been described. To avoid the risk of including studies twice, peer-reviewed publications were excluded if an HTA was available.
Detailed data were extracted regarding the survival extrapolation approach (e.g., MCM, spline-based), OS parametric distribution (e.g., Weibull, exponential, log-normal), clinical trial used for extrapolation and the corresponding follow-up duration, model time horizon, interventions, comparators, and how treatment efficacy translates to clinical measures (i.e., incremental LYs). These findings were organized and assessed to determine commonalities in methodological choices and innovations; results are presented across the compilation of peer-reviewed (i.e., publications, conference proceedings) and non-peer-reviewed studies (i.e., HTA evaluations) as well as separately for each.
This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors; the article therefore did not require ethics committee approval or patient consent.