Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
Transl Vis Sci Technol. 2021 Apr 1;10(4):15. doi: 10.1167/tvst.10.4.15.
Develop a hierarchical longitudinal regression model for estimating local rates of change of macular ganglion cell complex (GCC) measurements with optical coherence tomography (OCT).
We enrolled 112 eyes with four or more macular OCT images and ≥2 years of follow-up. GCC thickness measurements within central 6 × 6 superpixels were extracted from macular volume scans. We fit data from each superpixel separately with several hierarchical Bayesian random-effects models. Models were compared with the Watanabe-Akaike information criterion. For our preferred model, we estimated population and individual slopes and intercepts (baseline thickness) and their correlation.
Mean (SD) follow-up time and median (interquartile range) baseline 24-2 visual field mean deviation were 3.6 (0.4) years and -6.8 (-12.2 to -4.3) dB, respectively. The random intercepts and slopes model with random residual variance was the preferred model. While more individual and population negative slopes were observed in the paracentral and papillomacular superpixels, superpixels in the superotemporal and inferior regions displayed the highest correlation between baseline thickness and rates of change (r = -0.43 to -0.50 for the top five correlations).
A Bayesian linear hierarchical model with random intercepts/slopes and random variances is an optimal initial model for estimating GCC slopes at population and individual levels. This novel model is an efficient method for estimating macular rates of change and probability of glaucoma progression locally.
The proposed Bayesian hierarchical model can be applied to various macular outcomes from different OCT devices and to superpixels of variable sizes to estimate local rates of change and progression probability.
利用光学相干断层扫描（OCT）开发一种分层纵向回归模型，以估计黄斑神经节细胞复合体（GCC）测量的局部变化率。
我们招募了 112 只眼，这些眼有 4 次或更多的黄斑 OCT 图像和≥2 年的随访。从黄斑容积扫描中提取中央 6×6 超像素内的 GCC 厚度测量值。我们分别使用几种分层贝叶斯随机效应模型对每个超像素的数据进行拟合。使用 Watanabe-Akaike 信息准则对模型进行比较。对于我们首选的模型，我们估计了群体和个体斜率和截距（基线厚度）及其相关性。
平均（SD）随访时间和中位数（四分位间距）基线 24-2 视野平均偏差分别为 3.6（0.4）年和-6.8（-12.2 至-4.3）dB。具有随机残差方差的随机截距和斜率模型是首选模型。虽然在旁中心和乳头黄斑超像素中观察到更多的个体和群体负斜率，但在超颞和下区域的超像素中，基线厚度和变化率之间的相关性最高（前五个相关性的 r 值为-0.43 至-0.50）。
具有随机截距/斜率和随机方差的贝叶斯线性分层模型是估计人群和个体水平 GCC 斜率的最佳初始模型。这种新模型是一种在局部估计黄斑变化率和青光眼进展概率的有效方法。
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