Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Global Biometrics & Data Sciences, Bristol Myers Squibb, Lawrenceville, New Jersey, USA.
Stat Med. 2023 Nov 10;42(25):4582-4601. doi: 10.1002/sim.9877. Epub 2023 Aug 20.
The Glasgow outcome scale-extended (GOS-E), an ordinal scale measure, is often selected as the endpoint for clinical trials of traumatic brain injury (TBI). Traditionally, GOS-E is analyzed as a fixed dichotomy with favorable outcome defined as GOS-E ≥ 5 and unfavorable outcome as GOS-E < 5. More recent studies have defined favorable vs unfavorable outcome utilizing a sliding dichotomy of the GOS-E that defines a favorable outcome as better than a subject's predicted prognosis at baseline. Both dichotomous approaches result in loss of statistical and clinical information. To improve on power, Yeatts et al proposed a sliding scoring of the GOS-E as the distance from the cutoff for favorable/unfavorable outcomes, and therefore used more information found in the original GOS-E to estimate the probability of favorable outcome. We used data from a published TBI trial to explore the ramifications to trial operating characteristics by analyzing the sliding scoring of the GOS-E as either dichotomous, continuous, or ordinal. We illustrated a connection between the ordinal data and time-to-event (TTE) data to allow use of Bayesian software that utilizes TTE-based modeling. The simulation results showed that the continuous method with continuity correction offers higher power and lower mean squared error for estimating the probability of favorable outcome compared to the dichotomous method, and similar power but higher precision compared to the ordinal method. Therefore, we recommended that future severe TBI clinical trials consider analyzing the sliding scoring of the GOS-E endpoint as continuous with continuity correction.
格拉斯哥结局量表扩展版(GOS-E)是一种ordinal 量表,常用于创伤性脑损伤(TBI)临床试验的终点。传统上,GOS-E 作为固定二分法进行分析,有利结局定义为 GOS-E≥5,不利结局定义为 GOS-E<5。最近的研究利用 GOS-E 的滑动二分法定义有利和不利结局,将有利结局定义为优于基线时的预测预后。这两种二分法都会导致统计和临床信息的损失。为了提高效能,Yeatts 等人提出了 GOS-E 的滑动评分,作为有利/不利结局的分界点,因此利用了原始 GOS-E 中更多的信息来估计有利结局的概率。我们使用发表的 TBI 试验数据,通过分析 GOS-E 的滑动评分作为二分类、连续或有序数据,探讨了其对试验操作特征的影响。我们说明了有序数据与时间到事件(TTE)数据之间的关系,以允许使用基于 TTE 的建模的贝叶斯软件。模拟结果表明,与二分类方法相比,连续性校正的连续方法在估计有利结局概率方面具有更高的效能和更低的均方误差,与有序方法相比,具有相似的效能和更高的精度。因此,我们建议未来的严重 TBI 临床试验考虑将 GOS-E 终点的滑动评分分析为连续的,并进行连续性校正。