Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

Vadgama, Sachin; Mann, Jess; Bashir, Zahid; Spooner, Clare; Collins, Graham P.; Bullement, Ash

doi:10.1016/j.jval.2021.10.015

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…This method can be used to evaluate the difference between the predicted and observed survival at one specific time point, whereas the MAE and RMSE used in this study account for the difference in the predicted and observed survival over the entire period for which data are available (e.g., from receiving CAR T-cell therapy to the date of the last event in the most mature data). Despite the differences between this study and the study by Vadgama et al [34], both studies concluded that cure-based models provided the most accurate extrapolations of long-term survival for patients with R/R LBCL treated with CAR T-cell therapies, which is also in line with studies of other immuno-oncology therapies [35,36]. Additionally, in the final NICE appraisal of axi-cel, the independent academic reviewer group agreed that mixture cure models estimated on the 60-month OS data of ZUMA-1 provided good fits and were rightfully selected to model OS.…”

Section: Discussioncontrasting

confidence: 76%

“…In a recent study, Vadgama et al [34] compared the performance of standard parametric models, mixture cure models, cubic spline models, and nonmixture cure models fitted to the first, second, and third DBLs of ZUMA-1. Our study is more comprehensive, as it uses data from JULIET, ZUMA-1, and TRANSCEND and also includes mixture models.…”

Section: Discussionmentioning

confidence: 99%

“…Our study is more comprehensive, as it uses data from JULIET, ZUMA-1, and TRANSCEND and also includes mixture models. Furthermore, Vadgama et al [34] used a different measure to evaluate the performance of the survival extrapolation methods, as they compared predicted survival at 48 months with 48-month survival observed in the most mature data. This method can be used to evaluate the difference between the predicted and observed survival at one specific time point, whereas the MAE and RMSE used in this study account for the difference in the predicted and observed survival over the entire period for which data are available (e.g., from receiving CAR T-cell therapy to the date of the last event in the most mature data).…”

Section: Discussionmentioning

confidence: 99%

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Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma

Peterse,

Verburg-Baltussen,

Stewart

et al. 2023

PharmacoEconomics Open

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Background Durable remission has been observed in patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) treated with chimeric antigen receptor (CAR) T-cell therapy. Consequently, hazard functions for overall survival (OS) are often complex, requiring the use of flexible methods for extrapolations. Objectives We aimed to retrospectively compare the predictive accuracy of different survival extrapolation methods and evaluate the validity of goodness-of-fit (GOF) criteria-based model selection for CAR T-cell therapies in R/R LBCL. Methods OS data were sourced from JULIET, ZUMA-1, and TRANSCEND NHL 001. Standard parametric, mixture cure, cubic spline, and mixture models were fit to multiple database locks (DBLs), with varying follow-up durations. GOF was assessed using the Akaike information criterion and Bayesian information criterion. Predictive accuracy was calculated as the mean absolute error (MAE) relative to OS observed in the most mature DBL. Results For all studies, mixture cure and cubic spline models provided the best predictive accuracy for the least mature DBL (MAE 0.013-0.085 and 0.014-0.128, respectively). The predictive accuracy of the standard parametric and mixture models showed larger variation (MAE 0.024-0.162 and 0.013-0.176, respectively). With increasing data maturity, the predictive accuracy of standard parametric models remained poor. Correlation between GOF criteria and predictive accuracy was low, particularly for the least mature DBL. Conclusions Our analyses demonstrated that mixture cure and cubic spline models provide the most accurate survival extrapolations of CAR T-cell therapies in LBCL. Furthermore, GOF should not be the only criteria used when selecting the optimal survival model.

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Section: Discussioncontrasting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma

Peterse,

Verburg-Baltussen,

Stewart

et al. 2023

PharmacoEconomics Open

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“…Time-to-event outcomes were updated based on mature ZUMA-7 data (primary OS analysis). EFS, time to next treatment (TTNT) and OS were fit independently and extrapolated using mixture cure models (MCMs); MCMs have previously been shown to be the most accurate approach when predicting outcomes in LBCL patients treated with axi-cel 7 . Functional forms for the extrapolation of time-to-event data were selected based on best statistical fit (using Akaike’s and Bayesian Information Criteria [AIC and BIC, respectively]), as well as expert validation for clinical plausibility.…”

Section: Methodsmentioning

confidence: 99%

An updated cost-effectiveness analysis of axicabtagene ciloleucel in second-line large B-cell lymphoma patients in the United States

Oluwole,

Patel,

Vadgama

et al. 2023

Journal of Medical Economics

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Aims: This economic evaluation of axicabtagene ciloleucel (axi-cel) versus previous standard of care (SOC; salvage chemotherapy followed by high-dose therapy with autologous stem cell rescue) in the second line (2L) large B-cell lymphoma population is an update of previous economic models that contained immature survival data. Methods: This analysis is based on primary overall survival (OS) ZUMA-7 clinical trial data (median follow-up of 47.2 months), from a United States (US) payer perspective, with a model time horizon of 50 years. Mixture cure models were used to extrapolate updated survival data; subsequent treatment data and costs were updated. Patients who remained in the event-free survival state by 5 years were assumed to have achieved long-term remission and not require subsequent treatment. Results: Substantial survival and quality of life benefits were observed despite 57% of patients in the SOC arm receiving subsequent cellular therapy: median model-projected (ZUMA-7 trial Kaplan–Meier estimated) OS was 78 months (median not reached) for axi-cel versus 25 months (31 months) for SOC, resulting in incremental quality-adjusted life year (QALY) difference of 1.63 in favor of axi-cel. Incrementally higher subsequent treatment costs were observed in the SOC arm due to substantial crossover to cellular therapies, thus, when considering the generally accepted willingness to pay threshold of $150,000 per QALY in the US, axi-cel was cost-effective with an incremental cost-effectiveness ratio of $98,040 per QALY. Conclusions: Results remained consistent across a wide range of sensitivity and scenario analysis, including a crossover adjusted analysis, suggesting that the mature OS data has significantly reduced the uncertainty of axi-cel’s cost-effectiveness in the 2L setting in the US. Deferring treatment with CAR T therapies after attempting a path to transplant may result in excess mortality, lower quality of life and would be an inefficient use of resources relative to 2L axi-cel.

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“…After full paper screening, 17 publications were included in the review. (5,(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) Additionally, 56 papers were identi ed through 'snowballing'. From these, 43 publications were included in the nal review.…”

Section: Overview Of Included Studiesmentioning

confidence: 99%

Beyond Hazard Ratios: Appropriate statistical methods for quantifying the clinical effectiveness of immune-oncology therapies – the example of the Netherlands

Ramos¹,

Qendri

2023

Preprint

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BACKGROUND The Dutch Committee for the Evaluation of Oncological Drugs evaluates the effectiveness of new oncological drugs. The committee compares survival endpoints to the so-called PASKWIL-2023 criteria for palliative treatments. A positive recommendation depends on whether the median overall survival (OS) is below or above 12 months in the comparator arm. If the former applies, an OS benefit of at least 12 weeks, and a hazard ratio (HR) smaller than 0.7 are required. If the latter applies, an OS or progression free survival (PFS) benefit of at least 16 weeks, and an HR smaller than 0.7 are required. Nonetheless, the median survival time may not be reached and the proportional hazards (PH) assumption, quantified by the HR, is likely violated for IO therapies, deeming these criteria inappropriate. METHODS We conducted a systematic literature review to identify statistical methods used to represent the clinical effectiveness of IO therapies based on trial data. We searched MEDLINE and EMBASE databases from inception to August 31, 2022, limited to English papers. Methodological studies, randomized controlled trials, and discussion papers recognising key issues of survival data analysis of IO therapies were eligible for inclusion. RESULTS A total of 1,035 unique references were identified. After full paper screening, 17 publications were included in the review. Additionally, 43 papers were identified through ‘snowballing’. We conclude the current PASKWIL-2023 criteria are methodologically incorrect under non-PH. In that case, single summary statistics fail to capture the treatment effect and any measure should be interpreted in combination with the Kaplan-Meier curves. We recommend ’parameter-free’ measures, such as the difference in restricted mean survival time, avoiding assumptions on the underlying survival. CONCLUSIONS The HR is commonly used to assess treatment effectiveness, without investigating the validity of the PH assumption. This happens with the application of the PASKWIL-2023 criteria for palliative oncology treatments, which can only be valid under a PH setting. Under non-PH, alternative treatment effect measures are suggested. We propose a step-by-step approach supporting the choice of the most appropriate methods to quantify treatment effectiveness that can be used to redefine the PASKWIL-2023 criteria, or similar criteria in other clinical areas.

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Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1

Cited by 9 publications

References 23 publications

Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma

Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma

An updated cost-effectiveness analysis of axicabtagene ciloleucel in second-line large B-cell lymphoma patients in the United States

Beyond Hazard Ratios: Appropriate statistical methods for quantifying the clinical effectiveness of immune-oncology therapies – the example of the Netherlands

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