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首页> 外文期刊>Pharmaceutical statistics. >Blinded sample size re-estimation in superiority and noninferiority trials: Bias versus variance in variance estimation
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Blinded sample size re-estimation in superiority and noninferiority trials: Bias versus variance in variance estimation

机译:优越性和非劣效性试验中盲目样本大小的重新估计:方差估计中的偏倚与方差

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摘要

The internal pilot study design allows for modifying the sample size during an ongoing study based on a blinded estimate of the variance thus maintaining the trial integrity. Various blinded sample size re-estimation procedures have been proposed in the literature. We compare the blinded sample size re-estimation procedures based on the one-sample variance of the pooled data with a blinded procedure using the randomization block information with respect to bias and variance of the variance estimators, and the distribution of the resulting sample sizes, power, and actual type I error rate. For reference, sample size re-estimation based on the unblinded variance is also included in the comparison. It is shown that using an unbiased variance estimator (such as the one using the randomization block information) for sample size re-estimation does not guarantee that the desired power is achieved. Moreover, in situations that are common in clinical trials, the variance estimator that employs the randomization block length shows a higher variability than the simple one-sample estimator and in turn the sample size resulting from the related re-estimation procedure. This higher variability can lead to a lower power as was demonstrated in the setting of noninferiority trials. In summary, the one-sample estimator obtained from the pooled data is extremely simple to apply, shows good performance, and is therefore recommended for application.
机译:内部先导研究设计允许在正在进行的研究中根据方差的盲目估计来修改样本大小,从而保持试验的完整性。文献中已经提出了各种盲样本大小的重新估计程序。我们将基于合并数据的一样本方差的盲样本大小重新估计程序与使用方差估计信息的偏差和方差以及结果样本大小分布的随机分组信息的盲程序进行比较,功率和实际的I型错误率。作为参考,比较中还包括基于未盲法方差的样本量重新估计。结果表明,使用无偏方差估计器(例如使用随机块信息的方差估计器)进行样本大小重新估计并不能保证获得所需的功效。此外,在临床试验中常见的情况下,采用随机块长度的方差估计量显示出比简单的单样本估计量更高的可变性,进而导致由相关的重新估计过程产生的样本量更大。如非劣效性试验的结果所示,这种较高的变异性可能导致较低的功效。总之,从合并数据中获得的一样本估计量非常易于应用,表现出良好的性能,因此建议将其应用。

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