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癌症临床试验中的入组成功率、影响因素及预测模型(2008-2019 年)。

Enrollment Success, Factors, and Prediction Models in Cancer Trials (2008-2019).

机构信息

Duke Cancer Institute, Duke University, Durham, NC.

Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC.

出版信息

JCO Oncol Pract. 2023 Nov;19(11):1058-1068. doi: 10.1200/OP.23.00147. Epub 2023 Oct 4.

Abstract

PURPOSE

To investigate the enrollment success rate of cancer clinical trials conducted in 2008-2019 and various factors lowering the enrollment success rate.

METHODS

This is a cross-sectional study with clinical trial information from the largest registration database ClinicalTrials.gov. Enrollment success rate was defined as actual enrollment greater or equal to 85% of the estimated enrollment goal. The association between trial characteristics and enrollment success was evaluated using the multivariable logistic regression.

RESULTS

A total of 4,004 trials in breast, lung, and colorectal cancers were included. The overall enrollment success rate was 49.1%. Compared with 2008-2010 (51.5%) and 2011-2013 (52.1%), the enrollment success rate is lower in 2014-2016 (46.5%) and 2017-2019 (36.4%). Regression analyses found trial activation year, phase I, phase I/phase II, and phase II ( phase III), sponsor agency of government ( industry), not requiring healthy volunteers, and estimated enrollment of 50-100, 100-200, 200, and >500 ( 0-50) were associated with a lower enrollment success rate ( < .05). However, trials with placebo comparator, ≥5 locations ( 1 location), and a higher number of secondary end points (eg, ≥5 0) were associated with a higher enrollment success rate ( < .05). The AUC for prediction of the final logistic regression models for all trials and specific trial groups ranged from 0.69 to 0.76.

CONCLUSION

This large-scale study supports a lower enrollment success rate over years in cancer clinical trials. Identified factors for enrollment success can be used to develop and improve recruitment strategies for future cancer trials.

摘要

目的

调查 2008-2019 年开展的癌症临床试验的入组成功率以及降低入组成功率的各种因素。

方法

这是一项使用最大注册数据库 ClinicalTrials.gov 中临床试验信息的横断面研究。入组成功率定义为实际入组人数大于或等于预计入组目标的 85%。使用多变量逻辑回归评估试验特征与入组成功率之间的关联。

结果

共纳入乳腺癌、肺癌和结直肠癌的 4004 项试验。总体入组成功率为 49.1%。与 2008-2010 年(51.5%)和 2011-2013 年(52.1%)相比,2014-2016 年(46.5%)和 2017-2019 年(36.4%)的入组成功率较低。回归分析发现试验启动年份、I 期、I/II 期和 II 期(III 期)、政府(工业)作为申办方、不招募健康志愿者以及预计入组人数为 50-100、100-200、200 和>500(0-50)与较低的入组成功率相关(<0.05)。然而,安慰剂对照、≥5 个地点(1 个地点)和更多次要终点(如,≥5 个)的试验与较高的入组成功率相关(<0.05)。所有试验和特定试验组的最终逻辑回归模型预测的 AUC 范围为 0.69 至 0.76。

结论

这项大规模研究表明,癌症临床试验的入组成功率多年来呈下降趋势。确定的入组成功因素可用于制定和改进未来癌症试验的招募策略。

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