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.