Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, USA.
Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
Transl Vis Sci Technol. 2024 Jan 2;13(1):26. doi: 10.1167/tvst.13.1.26.
Demonstrate that a novel Bayesian hierarchical spatial longitudinal (HSL) model improves estimation of local macular ganglion cell complex (GCC) rates of change compared to simple linear regression (SLR) and a conditional autoregressive (CAR) model.
We analyzed GCC thickness measurements within 49 macular superpixels in 111 eyes (111 patients) with four or more macular optical coherence tomography scans and two or more years of follow-up. We compared superpixel-patient-specific estimates and their posterior variances derived from the latest version of a recently developed Bayesian HSL model, CAR, and SLR. We performed a simulation study to compare the accuracy of intercept and slope estimates in individual superpixels.
HSL identified a significantly higher proportion of significant negative slopes in 13/49 superpixels and a significantly lower proportion of significant positive slopes in 21/49 superpixels than SLR. In the simulation study, the median (tenth, ninetieth percentile) ratio of mean squared error of SLR [CAR] over HSL for intercepts and slopes were 1.91 (1.23, 2.75) [1.51 (1.05, 2.20)] and 3.25 (1.40, 10.14) [2.36 (1.17, 5.56)], respectively.
A novel Bayesian HSL model improves estimation accuracy of patient-specific local GCC rates of change. The proposed model is more than twice as efficient as SLR for estimating superpixel-patient slopes and identifies a higher proportion of deteriorating superpixels than SLR while minimizing false-positive detection rates.
The proposed HSL model can be used to model macular structural measurements to detect individual glaucoma progression earlier and more efficiently in clinical and research settings.
展示一种新的贝叶斯分层空间纵向（HSL）模型与简单线性回归（SLR）和条件自回归（CAR）模型相比，如何提高局部黄斑神经节细胞复合体（GCC）变化率的估计准确性。
我们分析了 111 只眼（111 位患者）的 49 个黄斑超像素内的 GCC 厚度测量值，这些患者有 4 次以上的黄斑光学相干断层扫描和 2 年以上的随访。我们比较了最新版本的最近开发的贝叶斯 HSL 模型、CAR 和 SLR 从超像素-患者特异性估计及其后验方差。我们进行了一项模拟研究，以比较个体超像素中截距和斜率估计的准确性。
与 SLR 相比，HSL 在 13/49 个超像素中确定了更高比例的显著负斜率，在 21/49 个超像素中确定了更低比例的显著正斜率。在模拟研究中，截距和斜率的 SLR[CAR]相对于 HSL 的平均平方误差中位数（十分位数，九十位数）比分别为 1.91（1.23，2.75）[1.51（1.05，2.20）]和 3.25（1.40，10.14）[2.36（1.17，5.56）]。
一种新的贝叶斯 HSL 模型提高了患者特定局部 GCC 变化率的估计准确性。与 SLR 相比，该模型用于估计超像素-患者斜率的效率提高了两倍以上，同时将 SLR 识别为恶化超像素的比例提高了，而假阳性检测率却降低了。
翻译为简体中文。
解析：这个句子的结构比较复杂，需要进行断句和语序调整。首先，将“PURPOSE”和“METHODS”这两个词单独提出来，因为它们分别是段落的主题和方法，与中文的行文习惯不符。然后，将“Demonstrate that a novel Bayesian hierarchical spatial longitudinal (HSL) model improves estimation of local macular ganglion cell complex (GCC) rates of change compared to simple linear regression (SLR) and a conditional autoregressive (CAR) model”这部分内容翻译成“展示一种新的贝叶斯分层空间纵向（HSL）模型与简单线性回归（SLR）和条件自回归（CAR）模型相比，如何提高局部黄斑神经节细胞复合体（GCC）变化率的估计准确性”。最后，把“CONCLUSIONS”和“TRANSLATIONAL RELEVANCE”这两个段落的主题词也单独提出来，因为它们在中文中也不太常见。
