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替代终点荟萃回归:监管者和研究者的有用统计学方法。

Surrogate endpoint metaregression: useful statistics for regulators and trialists.

机构信息

Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA.

School of Population Health, UNSW, St George Hospital, Sydney, Australia.

出版信息

J Clin Epidemiol. 2024 Nov;175:111508. doi: 10.1016/j.jclinepi.2024.111508. Epub 2024 Aug 31.

DOI:10.1016/j.jclinepi.2024.111508
PMID:39222723
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11609000/
Abstract

OBJECTIVES

The main purpose of using a surrogate endpoint is to estimate the treatment effect on the true endpoint sooner than with a true endpoint. Based on a metaregression of historical randomized trials with surrogate and true endpoints, we discuss statistics for applying and evaluating surrogate endpoints.

METHODS

We computed statistics from 2 types of linear metaregressions for trial-level data: simple random effects and novel random effects with correlations among estimated treatment effects in trials with more than 2 arms. A key statistic is the estimated intercept of the metaregression line. An intercept that is small or not statistically significant increases confidence when extrapolating to a new treatment because of consistency with a single causal pathway and invariance to labeling of treatments as controls. For a regulator applying the metaregression to a new treatment, a useful statistic is the 95% prediction interval. For a clinical trialist planning a trial of a new treatment, useful statistics are the surrogate threshold effect proportion, the sample size multiplier adjusted for dropouts, and the novel true endpoint advantage.

RESULTS

We illustrate these statistics with surrogate endpoint metaregressions involving antihypertension treatment, breast cancer screening, and colorectal cancer treatment.

CONCLUSION

Regulators and trialists should consider using these statistics when applying and evaluating surrogate endpoints.

摘要

目的

使用替代终点的主要目的是比真实终点更早地估计治疗对真实终点的效果。基于具有替代终点和真实终点的历史随机试验的荟萃回归,我们讨论了应用和评估替代终点的统计学方法。

方法

我们从 2 种类型的试验水平数据线性荟萃回归中计算了统计学数据:简单随机效应和具有超过 2 个臂的试验中估计的治疗效果之间相关性的新型随机效应。一个关键统计数据是荟萃回归线的估计截距。由于与单一因果途径一致并且治疗标签不变,截距较小或没有统计学意义的截距在推断新治疗时会增加信心。对于将荟萃回归应用于新治疗的监管机构来说,一个有用的统计数据是 95%预测区间。对于计划新治疗试验的临床试验人员,有用的统计数据是替代终点阈值效应比例、调整辍学的样本量乘数以及新型真实终点优势。

结果

我们用涉及抗高血压治疗、乳腺癌筛查和结直肠癌治疗的替代终点荟萃回归来说明这些统计数据。

结论

监管机构和试验人员在应用和评估替代终点时应考虑使用这些统计数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4e8/11609000/7c0ae095ffeb/nihms-2021834-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4e8/11609000/854d27ab92db/nihms-2021834-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4e8/11609000/6c676fb109ba/nihms-2021834-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4e8/11609000/7c0ae095ffeb/nihms-2021834-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4e8/11609000/854d27ab92db/nihms-2021834-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4e8/11609000/6c676fb109ba/nihms-2021834-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4e8/11609000/7c0ae095ffeb/nihms-2021834-f0003.jpg

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