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基于随机化后因素的亚组分析的肿瘤生长抑制-总生存(TGI-OS)模型:在IMpower150研究中用于阿替利珠单抗抗药物抗体(ADA)亚组分析的应用

Tumor Growth Inhibition-Overall Survival (TGI-OS) Model for Subgroup Analysis Based on Post-Randomization Factors: Application for Anti-drug Antibody (ADA) Subgroup Analysis of Atezolizumab in the IMpower150 Study.

作者信息

Yoshida Kenta, Chan Phyllis, Marchand Mathilde, Zhang Rong, Wu Benjamin, Ballinger Marcus, Sternheim Nitzan, Jin Jin Y, Bruno René

机构信息

Department of Clinical Pharmacology, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA.

Certara Strategic Consulting, Certara, Paris, France.

出版信息

AAPS J. 2022 Apr 28;24(3):58. doi: 10.1208/s12248-022-00710-4.

Abstract

Longitudinal changes of tumor size or tumor-associated biomarkers have been receiving growing attention as early markers of treatment benefits. Tumor growth inhibition-overall survival (TGI-OS) models represent mathematical frameworks used to establish a link from tumor size trajectory to survival outcome with the aim of predicting survival benefit with tumor data from a small number of subjects with a short follow-up time. In the present study, we applied the TGI-OS model to assess treatment benefit in the IMpower150 study for patients who exhibited development of anti-drug antibodies (ADA). Direct comparison between subgroups of the active arm [ADA positive (ADA +) and negative (ADA -) groups] to the entire control group is not appropriate, due to potential imbalances of baseline prognostic factors between ADA + and ADA - patients. Thus, the TGI-OS modeling framework was employed to adjust for differences in prognostic factors between the ADA subgroups to more accurately estimate the treatment benefits. After adjustment, the TGI-OS model predicted comparable hazard ratios (HRs) of OS between ADA + and ADA - subgroups, suggesting that the development of ADA does not have a clinically significant impact on the treatment benefit of atezolizumab.

摘要

肿瘤大小或肿瘤相关生物标志物的纵向变化作为治疗获益的早期标志物越来越受到关注。肿瘤生长抑制-总生存(TGI-OS)模型是一种数学框架,用于建立从肿瘤大小轨迹到生存结果的联系,目的是利用少量随访时间短的受试者的肿瘤数据预测生存获益。在本研究中,我们应用TGI-OS模型评估IMpower150研究中出现抗药抗体(ADA)的患者的治疗获益。由于ADA阳性(ADA +)和阴性(ADA -)患者之间基线预后因素可能存在不平衡,将活性组的亚组(ADA +组和ADA -组)与整个对照组进行直接比较并不合适。因此,采用TGI-OS建模框架来调整ADA亚组之间预后因素的差异,以更准确地估计治疗获益。调整后,TGI-OS模型预测ADA +和ADA -亚组之间的总生存风险比(HR)相当,这表明ADA的出现对阿特珠单抗的治疗获益没有临床显著影响。

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