Dynamic classification using credible intervals in longitudinal discriminant analysis

Hughes, David M ORCID: 0000-0002-1287-9994, Komarek, Arnost, Bonnett, Laura J ORCID: 0000-0002-6981-9212, Czanner, Gabriela and Garcia-Finana, Marta ORCID: 0000-0003-4939-0575 (2017) Dynamic classification using credible intervals in longitudinal discriminant analysis. STATISTICS IN MEDICINE, 36 (24). pp. 3858-3874.

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Abstract

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 uses Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalised linear mixed model of the biomarker's longitudinal evolution in each of the groups and 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. We also establish that by leaving some patients unclassified for a certain period, 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.

Item Type:	Article
Uncontrolled Keywords:	allocation scheme, credible intervals, longitudinal discriminant analysis
Depositing User:	Symplectic Admin
Date Deposited:	15 Jun 2017 10:11
Last Modified:	19 Jan 2023 07:03
DOI:	10.1002/sim.7397
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3007992