Friede T, Schmidli H
Abteilung Medizinische Statistik, Universitätsmedizin Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
Methods Inf Med. 2010;49(6):618-24. doi: 10.3414/ME09-02-0060. Epub 2010 Aug 5.
In the planning of clinical trials with count outcomes such as the number of exacerbations in chronic obstructive pulmonary disease (COPD) often considerable uncertainty exists with regard to the overall event rate and the level of overdispersion which are both crucial for sample size calculations.
To develop a sample size reestimation strategy that maintains the blinding of the trial, controls the type I error rate and is robust against misspecification of the nuisance parameters in the planning phase in that the actual power is close to the target.
The operation characteristics of the developed sample size reestimation procedure are investigated in a Monte Carlo simulation study.
Estimators of the overall event rate and the overdispersion parameter that do not require unblinding can be used to effectively adjust the sample size without inflating the type I error rate while providing power values close to the target.
If only little information is available regarding the size of the overall event rate and the overdispersion parameter in the design phase of a trial, we recommend the use of a design with sample size reestimation as the one suggested here. Trials in COPD are expected to benefit from the proposed sample size reestimation strategy.
在诸如慢性阻塞性肺疾病（COPD）急性加重次数等计数结果的临床试验规划中，对于总体事件发生率和过度分散水平往往存在相当大的不确定性，而这两者对于样本量计算都至关重要。
制定一种样本量重新估计策略，该策略能保持试验的盲法状态，控制I型错误率，并且在规划阶段对干扰参数的错误设定具有稳健性，即实际检验效能接近目标值。
在蒙特卡洛模拟研究中考察所制定的样本量重新估计程序的操作特性。
无需破盲的总体事件发生率和过度分散参数估计量可用于有效调整样本量，而不会增加I型错误率，同时提供接近目标值的检验效能值。
如果在试验设计阶段关于总体事件发生率和过度分散参数的大小仅有很少信息，我们建议使用此处所建议的具有样本量重新估计的设计。预计COPD试验将从此处提出的样本量重新估计策略中受益。
