1 Department of Medical Statistics, University Medical Center Göttingen, Germany.
2 School of Mathematics and Statistics, University of Newcastle, UK.
Stat Methods Med Res. 2019 Jan;28(1):117-133. doi: 10.1177/0962280217715664. Epub 2017 Jun 21.
We consider modelling and inference as well as sample size estimation and reestimation for clinical trials with longitudinal count data as outcomes. Our approach is general but is rooted in design and analysis of multiple sclerosis trials where lesion counts obtained by magnetic resonance imaging are important endpoints. We adopt a binomial thinning model that allows for correlated counts with marginal Poisson or negative binomial distributions. Methods for sample size planning and blinded sample size reestimation for randomised controlled clinical trials with such outcomes are developed. The models and approaches are applicable to data with incomplete observations. A simulation study is conducted to assess the effectiveness of sample size estimation and blinded sample size reestimation methods. Sample sizes attained through these procedures are shown to maintain the desired study power without inflating the type I error. Data from a recent trial in patients with secondary progressive multiple sclerosis illustrate the modelling approach.
我们考虑了以纵向计数数据为结果的临床试验的建模和推断，以及样本量估计和重新估计。我们的方法是通用的，但植根于多发性硬化症试验的设计和分析，其中磁共振成像获得的病变计数是重要的终点。我们采用二项式稀疏模型，允许具有边缘泊松或负二项分布的相关计数。为具有此类结果的随机对照临床试验制定了样本量规划和盲法样本量重新估计的方法。这些模型和方法适用于具有不完全观测的数据集。进行了一项模拟研究，以评估样本量估计和盲法样本量重新估计方法的有效性。通过这些程序获得的样本量被证明可以保持所需的研究效力，而不会增加Ⅰ型错误。来自最近一项针对继发性进展性多发性硬化症患者的试验的数据说明了建模方法。
