Duke Eye Center and Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina.
Hamilton Glaucoma Center and Department of Ophthalmology, University of California, San Diego, La Jolla, California.
Ophthalmol Glaucoma. 2019 Mar-Apr;2(2):72-77. doi: 10.1016/j.ogla.2019.01.004. Epub 2019 Jan 17.
There have been concerns that short-term clinical trials for evaluating new treatments in glaucoma would require prohibitively large sample sizes when using visual field endpoints, given that glaucoma is often a slowly progressive disease. This study sought to determine the required sample size for such trials using event-based analyses, and whether it can be reduced using trend-based analyses.
Longitudinal, observational study.
321 eyes of 240 glaucoma participants followed under routine clinical care using 242 visual field for an average of 10 years.
Sample size requirements were derived using computer simulations that reconstructed "real-world" visual fields by combining estimates of point-wise variability according to different threshold levels and rates of change obtained from the clinical glaucoma cohort. A clinical trial lasting 2 years with testing every 3 months was simulated, assuming that the new treatment halted visual field change in various percentages of participants (or "responders"). Treatment efficacy was evaluated by: (a) Difference in incidence of point-wise event-based progression (similar to the commercially available Guided Progression Analysis), and (b) Difference in rate of visual field mean deviation (MD) change between groups using linear mixed models (LMMs).
Sample size to detect a statistically significance difference between groups.
Between-group trend-based analyses using LMMs reduced sample size requirements by 85-90% across the range of new treatment effects when compared to the conventional point-wise event-based analysis. To detect the effect of a new treatment that halted progression in 30% of the participants under routine clinical care (equal to a 30% reduction in average rate of MD change) with 90% power, for example, 1924 participants would be required per group using event-based analysis, but only 277 participants per group if LMMs were used.
The feasibility of future glaucoma clinical trials can be substantially improved by evaluating differences in the rate of visual field change between groups.
由于青光眼通常是一种进展缓慢的疾病，因此使用视野终点评估新疗法的短期临床试验可能需要大量的样本量，这引起了人们的关注。本研究旨在通过基于事件的分析确定此类试验所需的样本量，以及是否可以通过基于趋势的分析来减少。
纵向观察性研究。
240 名青光眼患者的 321 只眼，在常规临床护理下使用 242 个视野平均随访 10 年。
使用计算机模拟从临床青光眼队列中获得的不同阈值水平和变化率的点估计的变异性来重建“真实世界”视野，从而得出样本量要求。模拟了为期 2 年的临床试验，每 3 个月进行一次测试，假设新疗法使各种比例的参与者（或“应答者”）的视野变化停止。通过以下两种方法评估治疗效果：（a）基于点的事件进展的发生率差异（类似于市售的 Guided Progression Analysis），以及（b）使用线性混合模型（LMMs）比较两组之间视野平均偏差（MD）变化率的差异。
检测组间统计学差异的样本量。
与传统的基于点的事件分析相比，基于 LMM 的组间趋势分析在新治疗效果的整个范围内减少了 85-90%的样本量要求。例如，要以 90%的功效检测到新疗法在常规临床护理下使 30%的参与者（相当于 MD 平均变化率降低 30%）的进展停止的效果，每组需要 1924 名参与者进行基于事件的分析，但如果使用 LMM，则每组只需 277 名参与者。
通过评估组间视野变化率的差异，可以大大提高未来青光眼临床试验的可行性。
