Examination of different pointwise linear regression methods for determining visual field progression.
Examination of different pointwise linear regression methods for determining visual field progression.
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发表时间：
2002-05
影响因子：
4.4
通讯作者：
S. Gardiner;D. Crabb
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文献类型：
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作者：
S. Gardiner;D. Crabb
目的比较使用逐点线性回归（PLR）检测视野进展（恶化）的几种不同方法的特异性和灵敏度。方法首先，理论结果推导出预测的考虑PLR方法将是最具体的，因此最不敏感。然后，开发了一个“虚拟眼”仿真模型，该模型模拟了一段时间内某个点的一系列灵敏度读数。该模型将正态分布噪声（根据已发表的结果估计）添加到每个点的灵敏度中，以产生一系列要使用每种方法进行分析的场。模拟稳定和恶化的眼睛，后者定义为在该系列中的一个重要的点簇处具有2 dB/y的无噪声损失。最敏感的测试方法是标记一个视野进展，如果它有一个点，表现出一个统计学上显着的斜率（在1%的水平），在灵敏度至少为-1 dB/y。最具体的是一种新的“三省略”方法，正在提出，以一种新颖的方式使用两个确认字段。目前使用确认字段来验证显著斜率的方法错误地标记了多达两倍的稳定眼睛，因为我们的新方法具有进展字段。结论：在任何应用中，当高度特异性是主要优先事项时，建议优先使用新提出的PLR方法。
PURPOSE To compare the specificity and sensitivity of several different methods for using pointwise linear regression (PLR) to detect progression (deterioration) in visual fields. METHODS First, theoretical results were derived to predict which of the considered PLR methods would be the most specific and hence the least sensitive. Then, a "Virtual Eye" simulation model was developed that simulates series of sensitivity readings for a point over time. The model adds normally distributed noise (estimated from published results) to the sensitivity at each point to produce a series of fields to be analyzed using each method. Stable and deteriorating eyes were simulated, with the latter defined to have a noise-free loss of 2 dB/y at a significant cluster of points over the series. RESULTS The most sensitive method tested was to flag a visual field as progressing if it had a point that exhibited a statistically significant slope (at the 1% level) of at least -1 dB/y in the sensitivity. The most specific was a new "Three-Omitting" method that is being proposed, using two confirmation fields in a novel way. Current methods of using confirmation fields to verify a significant slope incorrectly flagged up to twice as many stable eyes as having progressing fields as did our new method. CONCLUSIONS Using the new proposed PLR method is recommended in preference to current PLR methods in any applications when a high degree of specificity is the main priority.