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何时能相信应答者?使用 50%应答率评估临床试验时存在严重关切。

When can we trust responders? Serious concerns when using 50% response rate to assess clinical trials.

机构信息

Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia.

Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

出版信息

Epilepsia. 2019 Sep;60(9):e99-e103. doi: 10.1111/epi.16321. Epub 2019 Aug 31.

Abstract

Individual seizure rates are highly volatile, with large fluctuations from month-to-month. Nevertheless, changes in individual mean seizure rates are used to measure whether or not trial participants successfully respond to treatment. This study aims to quantify the challenges in identifying individual treatment responders in epilepsy. A power calculation was performed to determine the trial duration required to detect a significant 50% decrease in seizure rates (P < .05) for individuals. Seizure rate simulations were also performed to determine the number of people who would appear to be 50% responders by chance. Seizure rate statistics were derived from long-term seizure counts recorded during a previous clinical trial for an implantable seizure monitoring device. We showed that individual variance in monthly seizure rates can lead to an unacceptably high false-positive rate in the detection of individual treatment responders. This error rate cannot be reduced by increasing the trial population; however, it can be reduced by increasing the duration of clinical trials. This finding suggests that some drugs may be incorrectly evaluated as effective; or, conversely, that helpful drugs could be rejected based on 50% response rates. It is important to pursue more nuanced approaches to measuring individual treatment response, which consider the patient-specific distributions of seizure rates.

摘要

个体发作率波动很大,每月都有很大的波动。然而,个体平均发作率的变化被用来衡量试验参与者是否对治疗有成功的反应。本研究旨在量化在癫痫中识别个体治疗反应者的挑战。进行了功效计算,以确定检测发作率显著降低 50%(P<.05)所需的试验持续时间(个体)。还进行了发作率模拟,以确定有多少人会偶然出现 50%的反应者。发作率统计数据来自以前的一项植入式发作监测设备临床试验中记录的长期发作计数。我们表明,每月发作率的个体差异可能导致个体治疗反应者检测中的不可接受的高假阳性率。通过增加试验人群不能降低这种错误率;然而,可以通过增加临床试验的持续时间来降低错误率。这一发现表明,一些药物可能被错误地评估为有效;或者,相反,一些有益的药物可能会因为 50%的反应率而被拒绝。重要的是要寻求更细致的方法来衡量个体治疗反应,这需要考虑发作率的患者特异性分布。

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