Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

Hughes, DM ORCID: 0000-0002-1287-9994, Komarek, A, Czanner, Gabriela and Garcia-Finana, Marta ORCID: 0000-0003-4939-0575 (2018) Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types. Statistical Methods in Medical Research, 27 (7). pp. 2060-2080.

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Abstract

There is an emerging need in clinical research to accurately predict patients disease status and disease progression by optimally integrating multivariate clinical information. Clinical data is often collected over time for multiple biomarkers of different types (e.g. continuous, binary, counts). In this paper, we present a flexible and dynamic (time-dependent) discriminant analysis approach in which multiple biomarkers of various types are jointly modelled for classification purposes by the multivariate generalized linear mixed model. We propose a mixture of normal distributions for the random effects to allow additional flexibility when modelling the complex correlation between longitudinal biomarkers and to robustify the model and the classification procedure against misspecification of the random effects distribution. These longitudinal models are subsequently used in a multivariate time-dependent discriminant scheme to predict, at any time point, the probability of belonging to a particular risk group. The methodology is illustrated using clinical data from patients with epilepsy, where the aim is to identify patients who will not achieve remission of seizures within a 5-year follow up period.

Item Type:	Article
Uncontrolled Keywords:	Discriminant analysis, multivariate generalized linear mixed mode, multivariate longitudinal data, random effects, mixture distributions
Depositing User:	Symplectic Admin
Date Deposited:	28 Sep 2016 09:58
Last Modified:	19 Jan 2023 07:29
DOI:	10.1177/0962280216674496
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3003487