The perils with the misuse of predictive power
The perils with the misuse of predictive power
复制标题
滥用预测能力的危险
DOI：
10.1002/pst.467
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发表时间：
2011
影响因子：
1.5
通讯作者：
P. Fina
中科院分区：
文献类型：
--
作者：
N. Dallow;P. Fina
在早期药物开发中，特别是在研究新的作用机制或新的疾病领域时，对靶向或预期的治疗效果或变异性估计知之甚少。适应性设计允许提前停止，但也使用临时数据来适应样本量已被提出作为处理这些不确定性的实用方法。预测能力和条件能力是两种经常提到的技术，它们可以根据中期数据预测试验结束时将发生什么。然后可以根据这些预测来决定是否停止或继续试验。然而，除非这些统计数据的使用者对其特征有深刻的理解，否则可能会遇到重要的陷阱，特别是在使用预测能力时。本文的目的是强调这些潜在的缺陷。统计学家理解预测能力和条件能力之间的根本区别是至关重要的，因为它们可以在过渡阶段对决策产生巨大影响，特别是当用于重新评估样本量时。使用预测能力可以得到比条件能力或标准样本量计算更大的样本量。一个关键的区别是，预测能力考虑了所有的不确定性，其中一部分被标准样本量计算和条件能力所忽略。通过比较这些统计数据的特征，我们强调了实验人员在使用这种方法时需要注意的预测能力的重要特征。版权所有©2010 John Wiley & Sons， Ltd
In early drug development, especially when studying new mechanisms of action or in new disease areas, little is known about the targeted or anticipated treatment effect or variability estimates. Adaptive designs that allow for early stopping but also use interim data to adapt the sample size have been proposed as a practical way of dealing with these uncertainties. Predictive power and conditional power are two commonly mentioned techniques that allow predictions of what will happen at the end of the trial based on the interim data. Decisions about stopping or continuing the trial can then be based on these predictions. However, unless the user of these statistics has a deep understanding of their characteristics important pitfalls may be encountered, especially with the use of predictive power. The aim of this paper is to highlight these potential pitfalls. It is critical that statisticians understand the fundamental differences between predictive power and conditional power as they can have dramatic effects on decision making at the interim stage, especially if used to re‐evaluate the sample size. The use of predictive power can lead to much larger sample sizes than either conditional power or standard sample size calculations. One crucial difference is that predictive power takes account of all uncertainty, parts of which are ignored by standard sample size calculations and conditional power. By comparing the characteristics of each of these statistics we highlight important characteristics of predictive power that experimenters need to be aware of when using this approach. Copyright © 2010 John Wiley & Sons, Ltd.