Biostatistics Center, Massachusetts General Hospital, 50 Staniford Street, Suite 560, Boston, Massachusetts 02114, USA.
Muscle Nerve. 2012 Oct;46(4):506-11. doi: 10.1002/mus.23392.
In this study we explore several methods for incorporating survival information in the analysis of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) scores.
ALSFRS scores and patient survival times were simulated based on estimates from a recent clinical trial. Six analysis approaches were applied to the data. Each approach was based on ALSFRS scores, the survival time, or a combination of the 2. The power of each approach to detect potential treatment effects was estimated.
When the treatment acted solely on the change in ALSFRS, the shared parameter model provided the most power, although all of the models based on random effects were similar. As the effect on survival increased, rank-based analysis showed potential gains in power. Survival analysis was superior under a small effect on ALSFRS and a larger effect on mortality.
The shared parameter model and rank-based approach can offer improvements in power over traditional approaches.
在这项研究中,我们探索了几种方法,将生存信息纳入肌萎缩侧索硬化功能评定量表(ALSFRS)评分的分析中。
根据最近的一项临床试验的估计,模拟了 ALSFRS 评分和患者生存时间。对数据应用了六种分析方法。每种方法都基于 ALSFRS 评分、生存时间或两者的组合。估计了每种方法检测潜在治疗效果的功效。
当治疗仅作用于 ALSFRS 的变化时,共享参数模型提供了最大的功效,尽管基于随机效应的所有模型都相似。随着对生存的影响增加,基于等级的分析显示出潜在的功效增益。在 ALSFRS 疗效较小而死亡率较大的情况下,生存分析具有优势。
共享参数模型和基于等级的方法可以提高传统方法的功效。