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 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.

• Klíčová slova
• Discriminant analysis, mixture distributions, multivariate generalized linear mixed model, multivariate longitudinal data, random effects,
• MeSH
• algoritmy MeSH
• biologické markery * MeSH
• diskriminační analýza * MeSH
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• mladiství MeSH
• mladý dospělý MeSH
• předškolní dítě MeSH
• progrese nemoci * MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• biologické markery * MeSH

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.

• Klíčová slova
• allocation scheme, credible intervals, longitudinal discriminant analysis,
• MeSH
• Bayesova věta * MeSH
• diskriminační analýza MeSH
• epilepsie diagnóza   terapie   MeSH
• indukce remise MeSH
• klasifikace metody   MeSH
• lidé MeSH
• lineární modely MeSH
• longitudinální studie MeSH
• multivariační analýza MeSH
• počítačová simulace MeSH
• pravděpodobnost * MeSH
• prognóza MeSH
• rozhodování MeSH
• senzitivita a specificita MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH