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使用贝叶斯方法推断生存数据:以利用来自西达基奥仑赛治疗多发性骨髓瘤的外部数据为例的案例研究

Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma.

作者信息

Palmer Stephen, Lin Yi, Martin Thomas G, Jagannath Sundar, Jakubowiak Andrzej, Usmani Saad Z, Buyukkaramikli Nasuh, Phelps Hilary, Slowik Rafal, Pan Feng, Valluri Satish, Pacaud Lida, Jackson Graham

机构信息

Center for Health Economics, University of York, York, UK.

Mayo Clinic, Rochester, MN, USA.

出版信息

Oncol Ther. 2023 Sep;11(3):313-326. doi: 10.1007/s40487-023-00230-x. Epub 2023 Jun 4.

Abstract

INTRODUCTION

Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation.

METHODS

The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data.

RESULTS

Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference.

CONCLUSIONS

Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data.

TRIAL REGISTRATION

CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285.

摘要

引言

从短期临床试验数据推断长期总生存期(OS)是肿瘤学卫生技术评估的关键。然而,使用传统方法进行推断往往存在不确定性。我们使用西达基奥仑赛(cilta-cel),一种用于治疗多发性骨髓瘤的嵌合抗原受体T细胞疗法,采用灵活的贝叶斯方法来证明如何利用外部长期数据减少长期推断中的不确定性。

方法

关键的CARTITUDE-1试验(NCT03548207)提供了西达基奥仑赛的主要疗效数据,包括OS的12个月中位随访快照。来自I期LEGEND-2研究(NCT03090659)的长期(48个月中位随访)生存数据也可用。CARTITUDE-1试验的12个月OS数据通过两种方式进行推断:(1)具有标准参数分布的传统生存模型(无信息),以及(2)贝叶斯生存模型,其形状先验由48个月的LEGEND-2数据提供。为了进行验证,将从CARTITUDE-1试验12个月数据得出的推断结果与观察到的CARTITUDE-1试验28个月数据进行比较。

结果

使用传统无信息参数模型对CARTITUDE-1试验12个月数据进行的推断差异很大。利用来自48个月LEGEND-2数据集的信息先验,不同时间点的预计OS范围始终更窄。除了差异最小的无信息对数正态模型外,在有信息的贝叶斯模型中,推断曲线与CARTITUDE-1试验28个月数据之间的面积差异通常较小。

结论

有信息的贝叶斯生存模型减少了长期预测的变异性,并提供了与无信息对数正态模型类似的预测。贝叶斯模型从12个月数据生成了更窄且更合理的OS预测范围,与观察到的28个月数据相符。

试验注册

CARTITUDE-1在ClinicalTrials.gov的标识符为NCT03548207。LEGEND-2在ClinicalTrials.gov的标识符为NCT03090659,于2017年3月27日进行追溯注册,以及ChiCTR-ONH-17012285。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2f4/10447673/ed684b9c3f1e/40487_2023_230_Fig1_HTML.jpg

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