From the Department of Ophthalmology, Baylor College of Medicine (Holladay, Koch, Wang), Houston, Texas, and Department of Psychology, University of Southern California, Los Angeles (Wilcox), Los Angeles, California, USA.
J Cataract Refract Surg. 2021 Jan 1;47(1):65-77. doi: 10.1097/j.jcrs.0000000000000370.
To provide a reference for study design comparing intraocular lens (IOL) power calculation formulas, to show that the standard deviation (SD) of the prediction error (PE) is the single most accurate measure of outcomes, and to provide the most recent statistical methods to determine P values for type 1 errors.
Baylor College of Medicine, Houston, Texas, and University of Southern California, Los Angeles, California, USA.
Retrospective consecutive case series.
Two datasets comprised of 5200 and 13 301 single eyes were used. The SDs of the PEs for 11 IOL power calculation formulas were calculated for each dataset. The probability density functions of signed and absolute PE were determined.
None of the probability distributions for any formula in either dataset was normal (Gaussian). All the original signed PE distributions were not normal, but symmetric and leptokurtotic (heavy tailed) and had higher peaks than a normal distribution. The absolute distributions were asymmetric and skewed to the right. The heteroscedastic method was much better at controlling the probability of a type I error than older methods.
(1) The criteria for patient and data inclusion were outlined; (2) the appropriate sample size was recommended; (3) the requirement that the formulas be optimized to bring the mean error to zero was reinforced; (4) why the SD is the single best parameter to characterize the performance of an IOL power calculation formula was demonstrated; and (5) and using the heteroscedastic statistical method was the preferred method of analysis was shown.
为比较人工晶状体(IOL)计算公式的研究设计提供参考,展示预测误差(PE)的标准差(SD)是衡量结果的最准确指标,并提供最新的统计方法来确定Ⅰ类错误的 P 值。
美国德克萨斯州休斯顿贝勒医学院和加利福尼亚州洛杉矶南加州大学。
回顾性连续病例系列。
使用包含 5200 只和 13301 只单眼的两个数据集。计算了每个数据集的 11 种 IOL 计算公式的 PE 的 SD。确定了符号和绝对 PE 的概率密度函数。
任何公式在任何一个数据集的概率分布都不正常(高斯分布)。所有原始符号 PE 分布都不正常,但对称且尖峰度较高(长尾),峰度高于正态分布。绝对值分布不对称,向右偏斜。异方差方法比旧方法更能控制Ⅰ类错误的概率。
(1)概述了患者和数据纳入的标准;(2)推荐了适当的样本量;(3)强调了将公式优化到平均误差为零的要求;(4)证明了 SD 是描述 IOL 计算公式性能的最佳单一参数;(5)并展示了使用异方差统计方法是首选分析方法。