Dynamic classification using credible intervals in longitudinal discriminant analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10365530" target="_blank" >RIV/00216208:11320/17:10365530 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1002/sim.7397" target="_blank" >http://dx.doi.org/10.1002/sim.7397</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/sim.7397" target="_blank" >10.1002/sim.7397</a>
Alternative languages
Result language
angličtina
Original language name
Dynamic classification using credible intervals in longitudinal discriminant analysis
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Statistics in Medicine
ISSN
0277-6715
e-ISSN
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Volume of the periodical
36
Issue of the periodical within the volume
24
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
3858-3874
UT code for WoS article
000412107100008
EID of the result in the Scopus database
2-s2.0-85026508117