Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10377872" target="_blank" >RIV/00216208:11320/18:10377872 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1177/0962280216674496" target="_blank" >https://doi.org/10.1177/0962280216674496</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/0962280216674496" target="_blank" >10.1177/0962280216674496</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

Popis výsledku v původním jazyce

There is an emerging need in clinical research to accurately predict patients&apos; disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and 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 five-year follow-up period.

Název v anglickém jazyce

Dynamic longitudinal discriminant analysis using multiple longitudinal markers of different types

Popis výsledku anglicky

There is an emerging need in clinical research to accurately predict patients&apos; disease status and disease progression by optimally integrating multivariate clinical information. Clinical data are often collected over time for multiple biomarkers of different types (e.g. continuous, binary and 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 five-year follow-up period.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Statistical Methods in Medical Research

ISSN

0962-2802

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

2060-2080

Kód UT WoS článku

000433616600011

EID výsledku v databázi Scopus

2-s2.0-85030146736