Dataset for: Dynamic classification using credible intervals in longitudinal discriminant analysis

Recently developed methods of Longitudinal Discriminant Analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers. The classification utilises Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalized linear mixed model of the biomarkers longitudinal evolution in each of the groups, which can be updated each time new data is available for a patient, providing a dynamic (over time) allocation scheme. However, the precision of the estimated group probabilities differs for each patient and also over time. This precision can be assessed by looking at credible intervals for the group membership probabilities. In this paper, we propose a new allocation rule that incorporates credible intervals for use in context of a dynamic longitudinal discriminant analysis, and show that this can decrease the number of false positives in a prognostic test, improving the Positive Predictive Value (PPV). We also establish that by leaving some patients unclassified for a certain period of time, the classification accuracy of those patients who are classified can be improved, giving increased confidence to clinicians in their decision making. Finally, we show that determining a stopping rule dynamically can be more accurate than specifying a set time point at which to decide on a patient's status. We illustrate our methodology using data from patients with epilepsy and show how patients who fail to achieve adequate seizure control are more accurately identified using credible intervals compared to existing methods.近年来发展的纵向判别分析（Longitudinal Discriminant Analysis）方法，可依托连续型与离散型生物标志物的纵向观测历史，将研究对象归类至预先设定的预后组别中。该分类方法依托针对每个预后组别的组归属概率的贝叶斯估计（Bayesian estimates）实现分类。此类估计值源于描述各分组内生物标志物纵向演化过程的多元广义线性混合模型，且每当患者获取新观测数据时即可完成更新，由此形成一种随时间动态调整的分类分配方案。不过，所估计的组归属概率的精度会因患者个体与时间维度存在差异，可通过观测组归属概率的可信区间（credible intervals）对该精度进行评估。本文提出一种将可信区间纳入动态纵向判别分析场景的新型分配规则，并证实该规则可降低预后检测中的假阳性数量，提升阳性预测值（Positive Predictive Value, PPV）。研究同时证实，若将部分患者在特定时段内暂不分类，可提升已分类患者的分类准确率，进而增强临床医生决策时的信心。此外，本文还表明，动态设定分类终止规则，相较于预先指定固定时间点以判定患者状态，能够获得更高的分类准确性。最后，我们利用癫痫患者的临床数据集对所提方法进行验证，并证实相较于现有方法，借助可信区间可更精准地识别出未能实现充分癫痫发作控制的患者。