您的账号已在其他设备登录,您当前账号已强迫下线,
如非您本人操作,建议您在会员中心进行密码修改

确定
收藏 | 浏览0

We detail the design of a study to monitor the safety of including albendazole to an existing treatment regimen to eliminate lymphatic filariasis. We wish to show that this new regimen does not increase the rate of a rare serious adverse event (SAE) compared to the old regimen. Controlled but small clinical trials have not detected any increase in the SAE using albendazole, and it is known to have added benefits; therefore, it is unethical to randomize patients to the old regimen.A sample size for the new regimen is needed to test that the new rate of SAE is noninferior to the historic rate. If the new regimen does have an inferior rate of SAE then we wish to stop the study early. This setup is different from traditional early stopping for efficacy and futility. In that traditional case, the two stopping decisions are relative to the same null hypothesis of equality, while in our setup, we have two different null hypotheses: the noninferiority null and the equality null. When testing the former, we need not stop early if the new regimen appears better because no subjects are receiving the old regimen anymore anyway. When testing the equality of SAE rates, however, we want to stop early if the new regimen has a significantly higher rate of SAE.We create a design that uses an exact difference in proportions test for testing noninferiority, but calculates maximal sample size based on conditional power which treats the historical rates as true rates. The design allows for early stopping if the new treatment appears inferior with respect to SAE rate but makes no corrections for multiple testing. We explore the properties of this naive design without assuming the historical rates are known.For our example, we show that our naive design strategy bounds the type I error of the noninferiority hypothesis in all cases and bounds it for the equality hypothesis at 0.05, as long as the true SAE rate is <0.00015. The same design has unconditional power for the noninferiority hypothesis greater than the nominal 80

作者:Michael P, Fay;Chiung-Yu, Huang;Nana A Y, Twum-Danso

来源:Clinical trials (London, England) 2007 年 4卷 6期

知识库介绍

临床诊疗知识库该平台旨在解决临床医护人员在学习、工作中对医学信息的需求,方便快速、便捷的获取实用的医学信息,辅助临床决策参考。该库包含疾病、药品、检查、指南规范、病例文献及循证文献等多种丰富权威的临床资源。

详细介绍
热门关注
免责声明:本知识库提供的有关内容等信息仅供学习参考,不代替医生的诊断和医嘱。

收藏
| 浏览:0
作者:
Michael P, Fay;Chiung-Yu, Huang;Nana A Y, Twum-Danso
来源:
Clinical trials (London, England) 2007 年 4卷 6期
We detail the design of a study to monitor the safety of including albendazole to an existing treatment regimen to eliminate lymphatic filariasis. We wish to show that this new regimen does not increase the rate of a rare serious adverse event (SAE) compared to the old regimen. Controlled but small clinical trials have not detected any increase in the SAE using albendazole, and it is known to have added benefits; therefore, it is unethical to randomize patients to the old regimen.A sample size for the new regimen is needed to test that the new rate of SAE is noninferior to the historic rate. If the new regimen does have an inferior rate of SAE then we wish to stop the study early. This setup is different from traditional early stopping for efficacy and futility. In that traditional case, the two stopping decisions are relative to the same null hypothesis of equality, while in our setup, we have two different null hypotheses: the noninferiority null and the equality null. When testing the former, we need not stop early if the new regimen appears better because no subjects are receiving the old regimen anymore anyway. When testing the equality of SAE rates, however, we want to stop early if the new regimen has a significantly higher rate of SAE.We create a design that uses an exact difference in proportions test for testing noninferiority, but calculates maximal sample size based on conditional power which treats the historical rates as true rates. The design allows for early stopping if the new treatment appears inferior with respect to SAE rate but makes no corrections for multiple testing. We explore the properties of this naive design without assuming the historical rates are known.For our example, we show that our naive design strategy bounds the type I error of the noninferiority hypothesis in all cases and bounds it for the equality hypothesis at 0.05, as long as the true SAE rate is <0.00015. The same design has unconditional power for the noninferiority hypothesis greater than the nominal 80