Robust and Censored Modeling and Prediction of Progression in Glaucomatous Visual Fields
Robust and Censored Modeling and Prediction of Progression in Glaucomatous Visual Fields
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DOI：
10.1167/iovs.12-11185
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发表时间：
2013-10-01
影响因子：
4.4
通讯作者：
Lesaffre, Emmanuel M. E. H.
中科院分区：
文献类型：
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作者：
Bryan, Susan R.;Vermeer, Koenraad A.;Lesaffre, Emmanuel M. E. H.
目的。经典回归是基于与视野（VF）数据相冲突的某些假设。我们研究和评估了不同的回归模型及其假设，以确定青光眼的逐点VF进展，并更好地预测未来的视野损失，以便个性化的青光眼临床管理。纳入130例原发性青光眼患者的标准自动视野，随访时间至少为6年。每个VF位置的灵敏度估计随时间回归，使用经典的线性和指数回归模型，以及考虑审查和允许鲁棒拟合的这些模型的不同变体。比较了这些模型的最佳拟合和预测能力。使用不同数量的测量，提前6次测量（大约3年）对预测进行评估。对于拟合数据，经典的无删节线性回归模型具有最低的均方根误差和95%的绝对误差。当增加用于预测未来测量的测量数量时，所有模型的这些误差都减小了，无论包含多少测量，经典的无删节线性回归模型的这些误差值都是最低的。所有模型的表现都相似。尽管违反了它的假设，经典的无删节线性回归模型似乎为我们的数据提供了最好的拟合。此外，该模型在预测未来的VFs时表现最好。然而，需要更先进的回归模型来探索青光眼进展的时空关系，以将预测误差降低到临床有意义的水平。
PURPOSE. Classic regression is based on certain assumptions that conflict with visual field (VF) data. We investigate and evaluate different regression models and their assumptions in order to determine point-wise VF progression in glaucoma and to better predict future field loss for personalised clinical glaucoma management.METHODS. Standard automated visual fields of 130 patients with primary glaucoma with a minimum of 6 years of follow-up were included. Sensitivity estimates at each VF location were regressed on time with classical linear and exponential regression models, as well as different variants of these models that take into account censoring and allow for robust fits. These models were compared for the best fit and for their predictive ability. The prediction was evaluated at six measurements (approximately 3 years) ahead using varying numbers of measurements.RESULTS. For fitting the data, the classical uncensored linear regression model had the lowest root mean square error and 95th percentile of the absolute errors. These errors were reduced in all models when increasing the number of measurements used for the prediction of future measurements, with the classical uncensored linear regression model having the lowest values for these errors irrespective of how many measurements were included.CONCLUSIONS. All models performed similarly. Despite violation of its assumptions, the classical uncensored linear regression model appeared to provide the best fit for our data. In addition, this model appeared to perform the best when predicting future VFs. However, more advanced regression models exploring any temporal-spatial relationships of glaucomatous progression are needed to reduce prediction errors to clinically meaningful levels.