Recent development of the quick contrast sensitivity function (qCSF) method has made it possible to obtain accurate, precise, and efficient contrast sensitivity function (CSF) assessment. To improve statistical inference on CSF changes in a within-subject design, we developed a hierarchical Bayesian model (HBM) to compute the joint distribution of CSF parameters and hyperparameters at test, subject, and population levels, utilizing information within- and between-subjects and experimental conditions. We evaluated the performance of the HBM relative to a non-hierarchical Bayesian inference procedure (BIP) on an existing CSF dataset of 112 subjects obtained with the qCSF method in three luminance conditions (Hou, Lesmes, Kim, Gu, Pitt, Myung, & Lu, 2016). We found that the average d's of the area under log CSF (AULCSF) and CSF parameters between pairs of luminance conditions at the test-level from the HBM were 33.5% and 103.3% greater than those from the BIP analysis of AULCSF. The increased d' resulted in greater statistical differences between experimental conditions across subjects. In addition, simulations showed that the HBM generated accurate and precise CSF parameter estimates. These results have strong implications for the application of HBM in clinical trials and patient care.

中文翻译：

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受试者内设计中对比敏感度函数的分层贝叶斯建模。


最近发展的快速对比敏感度函数（qCSF）方法使得获得准确、精确、高效的对比敏感度函数（CSF）评估成为可能。为了改进受试者内设计中 CSF 变化的统计推断，我们开发了分层贝叶斯模型 (HBM)，利用受试者内和受试者间的信息来计算测试、受试者和群体水平上 CSF 参数和超参数的联合分布和实验条件。我们在现有的 112 名受试者的 CSF 数据集上，在三种亮度条件下（Hou、Lesmes、Kim、Gu、Pitt、Myung 和卢，2016）。我们发现 HBM 测试级别的亮度条件对之间 log​​ CSF (AULCSF) 下面积和 CSF 参数的平均 d 比 AULCSF BIP 分析的平均 d 大 33.5% 和 103.3%。 d' 的增加导致受试者实验条件之间的统计差异更大。此外，模拟表明 HBM 生成准确且精确的 CSF 参数估计。这些结果对于 HBM 在临床试验和患者护理中的应用具有重要意义。