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用于分析非线性肿瘤动力学和死亡动态预测的多元联合脆弱性模型。

Multivariate joint frailty model for the analysis of nonlinear tumor kinetics and dynamic predictions of death.

机构信息

INSERM U1219, Biostatistics team, University of Bordeaux, Bordeaux, France.

Hôpital Henri Mondor, Créteil, France.

出版信息

Stat Med. 2018 Jun 15;37(13):2148-2161. doi: 10.1002/sim.7640. Epub 2018 Mar 26.

Abstract

The Response Evaluation Criteria in Solid Tumors are used as standard guidelines for the clinical evaluation of cancer treatments. The assessment is based on the anatomical tumor burden: change in size of target lesions and evolution of nontarget lesions (NTL). Despite unquestionable advantages of this standard tool, Response Evaluation Criteria in Solid Tumors are subject to some limitations such as categorization of continuous tumor size or negligence of its longitudinal trajectory. In particular, it is of interest to capture its nonlinear shape and model it simultaneously with recurrent progressions of NTL and overall survival. We propose a multivariate nonlinear mechanistic joint frailty model for longitudinal data, recurrent events, and a terminal event. In the model, the tumor size trajectory is described using an ordinary differential equation that accounts for the natural growth and treatment-induced decline. We perform a simulation study to validate the method and apply the model to a phase III clinical trial in colorectal cancer. In the results of the analysis, we determine on which component, tumor size, NTL, or death the treatment acts mostly and perform dynamic predictions of death. We compare the model with other models that consider parametric functions or splines for the tumor size trajectory in terms of goodness of fit and predictive accuracy.

摘要

实体瘤反应评价标准被用作癌症治疗临床评估的标准指南。评估基于解剖肿瘤负担:目标病变大小的变化和非目标病变(NTL)的演变。尽管这种标准工具具有无可置疑的优势,但实体瘤反应评价标准也存在一些局限性,例如连续肿瘤大小的分类或忽略其纵向轨迹。特别是,捕捉其非线性形状并同时对 NTL 的反复进展和总生存进行建模很有意义。我们提出了一种用于纵向数据、复发事件和终端事件的多变量非线性机制联合脆弱性模型。在该模型中,使用常微分方程来描述肿瘤大小轨迹,该方程考虑了自然生长和治疗诱导的下降。我们进行了一项模拟研究来验证该方法,并将该模型应用于结直肠癌的 III 期临床试验。在分析结果中,我们确定治疗主要作用于哪个部分,肿瘤大小、NTL 或死亡,并进行死亡的动态预测。我们比较了该模型与其他模型,这些模型考虑了参数函数或样条函数用于肿瘤大小轨迹,以评估拟合优度和预测准确性。

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