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癌症治疗中比较疗效设定下的确认性预测驱动 RCT。

Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment.

机构信息

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

出版信息

Br J Cancer. 2023 Mar;128(7):1278-1285. doi: 10.1038/s41416-023-02144-x. Epub 2023 Jan 23.

DOI:10.1038/s41416-023-02144-x
PMID:36690722
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10050232/
Abstract

BACKGROUND

Medical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative effectiveness, using predictive biomarkers is becoming more common. RCTs that incorporate predictive biomarkers into the study design, called prediction-driven RCTs, are needed to rigorously evaluate these treatment strategies. Although researched extensively in the experimental treatment setting, literature is lacking in providing guidance about prediction-driven RCTs in the comparative effectiveness setting.

METHODS

Realistic simulations with time-to-event endpoints are used to compare contrasts of clinical utility and provide examples of simulated prediction-driven RCTs in the comparative effectiveness setting.

RESULTS

Our proposed contrast for clinical utility accurately estimates the true clinical utility in the comparative effectiveness setting while in some scenarios, the contrast used in current literature does not.

DISCUSSION

It is important to properly define contrasts of interest according to the treatment setting. Realistic simulations should be used to choose and evaluate the RCT design(s) able to directly estimate that contrast. In the comparative effectiveness setting, our proposed contrast for clinical utility should be used.

摘要

背景

癌症治疗方面的医学进步使许多类型的癌症有了多种已批准的治疗方法和预后及预测生物标志物。在已批准的治疗方案中,通过预测生物标志物来确定改进的治疗策略(称为比较有效性研究)越来越常见。需要将预测生物标志物纳入研究设计的 RCT(称为预测驱动 RCT),以严格评估这些治疗策略。虽然在实验治疗环境中进行了广泛研究,但在比较有效性环境中提供关于预测驱动 RCT 的指导文献仍然缺乏。

方法

使用具有时间事件终点的现实模拟来比较临床效用的对比,并提供比较有效性环境中模拟预测驱动 RCT 的示例。

结果

我们提出的临床效用对比能够在比较有效性环境中准确估计真实的临床效用,而在某些情况下,当前文献中使用的对比则不能。

讨论

根据治疗环境正确定义感兴趣的对比很重要。应使用现实模拟来选择和评估能够直接估计该对比的 RCT 设计。在比较有效性环境中,应使用我们提出的临床效用对比。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fda/10050232/73f5919bfe38/41416_2023_2144_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fda/10050232/73f5919bfe38/41416_2023_2144_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7fda/10050232/73f5919bfe38/41416_2023_2144_Fig1_HTML.jpg

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Molecular Landscape and Actionable Alterations in a Genomically Guided Cancer Clinical Trial: National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH).基于基因组的癌症临床试验的分子图谱和可操作的改变:美国国立癌症研究所分子分析用于治疗选择(NCI-MATCH)。
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具有预测生物标志物的临床试验中精准医学设计的相对效率。
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