Dong Ruiqi, Ying Gui-Shuang
Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania.
Center for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Ophthalmol Sci. 2022 Dec 31;3(2):100266. doi: 10.1016/j.xops.2022.100266. eCollection 2023 Jun.
To evaluate the recent practice of design and statistical analysis of ophthalmic randomized clinical trials (RCTs).
Review of 96 ophthalmic RCTs.
Two authors (R.D., G.S.Y.) reviewed primary result papers published from January 2020 through December 2021 in , , , and . Data were extracted and analyzed for the characteristics of design (1-eye design, 2-eye design, paired-eye design, and subject design), sample size and power, and statistical analysis for intereye correlation adjustment, missing data, and correction for multiplicity.
Characteristics of trial design and statistical analysis.
Among 96 RCTs, 50 (52%) used 1-eye design, 21 (22%) 2-eye design, 10 (10%) paired-eye design, and 15 (16%) subject design. In 31 trials of 2-eye design or paired-eye design, 18 (58%) trials had suboptimal analysis of data from both eyes by analyzing data from 1 eye (n = 10), taking the average of 2 eyes (n = 2), analyzing 2 eyes separately (n = 1), ignoring intereye correlation (n = 3), or not specifying how 2-eye data were analyzed (n = 2), and 13 trials (42%) properly adjusted the intereye correlation by using the mixed-effects model (n = 6), paired test (n = 5), generalized estimating equations (n = 1), or marginal Cox regression model (n = 1). Among 96 trials, 75 (78%) provided both sample size and statistical power estimation, and 16 (17%) trials described statistical test for sample size or power estimation. Missing data in primary outcome occurred in 86 (90%) trials with a median missing data rate of 8%, 32 (37%) trials applied statistical methods for missing data, including last value carried forward (n = 10), multiple imputation (n = 14), or other approaches (n = 8). Among 25 trials with > 2 arms, 16 (64%) corrected for multiplicity using the Bonferroni procedure (n = 8), Hochberg procedure (n = 2), Gatekeeping procedure (n = 2), or hierarchical procedure (n = 4). Among 16 trials with multiple primary outcomes, 4 (25%) corrected for multiplicity by the Bonferroni procedure.
There are opportunities for improvement in the design and statistical analyses of ophthalmic trials, particularly in the aspects of adjustment for intereye correlation, missing data, and multiplicity. Continuing education in ophthalmology and vision research community may improve the quality of ophthalmic trials.
Proprietary or commercial disclosure may be found after the references.
评估近期眼科随机临床试验(RCT)的设计及统计分析情况。
对96项眼科RCT进行综述。
两位作者(R.D.,G.S.Y.)回顾了2020年1月至2021年12月在《 》《 》《 》和《 》上发表的主要结果论文。提取并分析数据,以了解设计特征(单眼设计、双眼设计、配对眼设计和受试者设计)、样本量和检验效能,以及对眼间相关性调整、缺失数据和多重性校正的统计分析。
试验设计和统计分析的特征。
在96项RCT中,50项(52%)采用单眼设计,21项(22%)采用双眼设计,10项(10%)采用配对眼设计,15项(16%)采用受试者设计。在31项双眼设计或配对眼设计的试验中,18项(58%)试验对双眼数据的分析欠佳,分析方法包括仅分析一眼的数据(n = 10)、取两眼的平均值(n = 2)、分别分析两眼(n = 1)、忽略眼间相关性(n = 3)或未说明如何分析双眼数据(n = 2);13项(42%)试验通过使用混合效应模型(n = 6)、配对检验(n = 5)、广义估计方程(n = 1)或边际Cox回归模型(n = 1)对眼间相关性进行了恰当调整。在96项试验中,75项(78%)提供了样本量和统计检验效能估计,16项(17%)试验描述了样本量或检验效能估计的统计检验方法。86项(90%)试验的主要结局存在缺失数据,缺失数据率中位数为8%,32项(37%)试验应用了缺失数据分析的统计方法,包括末次观察值结转(n = 10)、多重填补(n = 14)或其他方法(n = 8)。在25项有超过2个组的试验中,16项(64%)使用Bonferroni法(n = 8)、Hochberg法(n = 2)、把关法(n = 2)或分层法(n = 4)进行了多重性校正。在16项有多个主要结局的试验中,4项(25%)通过Bonferroni法进行了多重性校正。
眼科试验的设计和统计分析仍有改进空间,尤其是在眼间相关性调整、缺失数据和多重性方面。眼科及视觉研究领域的继续教育可能会提高眼科试验的质量。
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