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

We have developed a method to longitudinally classify subjects into two or more prognostic groups using longitudinally observed values of markers related to the prognosis. We assume the availability of a training data set where the subjects' allocation into the prognostic group is known. The proposed method proceeds in two steps as described earlier in the literature. First, multivariate linear mixed models are fitted in each prognostic group from the training data set to model the dependence of markers on time and on possibly other covariates. Second, fitted mixed models are used to develop a discrimination rule for future subjects. Our method improves upon existing approaches by relaxing the normality assumption of random effects in the underlying mixed models. Namely, we assume a heteroscedastic multivariate normal mixture for random effects. Inference is performed in the Bayesian framework using the Markov chain Monte Carlo methodology. Software has been written for the proposed method and it is freely available. The methodology is applied to data from the Dutch Primary Biliary Cirrhosis Study.

• MeSH
• biliární cirhóza farmakoterapie   MeSH
• biologické markery analýza   MeSH
• cholagoga a choleretika terapeutické užití   MeSH
• diskriminační analýza * MeSH
• interpretace statistických dat * MeSH
• kyselina ursodeoxycholová terapeutické užití   MeSH
• lidé MeSH
• lineární modely * MeSH
• longitudinální studie MeSH
• počítačová simulace MeSH
• progrese nemoci MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• biologické markery MeSH
• cholagoga a choleretika MeSH
• kyselina ursodeoxycholová MeSH

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

Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-one-out accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly. MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Several methodological issues need to be addressed to increase the usefulness of this classification approach.

• MeSH
• diskriminační analýza MeSH
• dospělí MeSH
• lidé MeSH
• lineární modely * MeSH
• magnetická rezonanční tomografie metody   MeSH
• mapování mozku * MeSH
• mladý dospělý MeSH
• mozek patologie   MeSH
• počítačové zpracování obrazu metody   MeSH
• psychiatrické posuzovací škály MeSH
• schizofrenie diagnóza   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Accuracy of identification tools in forensic anthropology primarily rely upon the variations inherent in the data upon which they are built. Sex determination methods based on craniometrics are widely used and known to be specific to several factors (e.g. sample distribution, population, age, secular trends, measurement technique, etc.). The goal of this study is to discuss the potential variations linked to the statistical treatment of the data. Traditional craniometrics of four samples extracted from documented osteological collections (from Portugal, France, the U.S.A., and Thailand) were used to test three different classification methods: linear discriminant analysis (LDA), logistic regression (LR), and support vector machines (SVM). The Portuguese sample was set as a training model on which the other samples were applied in order to assess the validity and reliability of the different models. The tests were performed using different parameters: some included the selection of the best predictors; some included a strict decision threshold (sex assessed only if the related posterior probability was high, including the notion of indeterminate result); and some used an unbalanced sex-ratio. Results indicated that LR tends to perform slightly better than the other techniques and offers a better selection of predictors. Also, the use of a decision threshold (i.e. p>0.95) is essential to ensure an acceptable reliability of sex determination methods based on craniometrics. Although the Portuguese, French, and American samples share a similar sexual dimorphism, application of Western models on the Thai sample (that displayed a lower degree of dimorphism) was unsuccessful.

• Klíčová slova
• Accuracy, Forensic anthropology population data, Population, Reliability, Sex estimation, Statistics,
• MeSH
• diskriminační analýza MeSH
• kefalometrie * MeSH
• lidé MeSH
• logistické modely MeSH
• rasové skupiny MeSH
• reprodukovatelnost výsledků MeSH
• soudní antropologie MeSH
• support vector machine MeSH
• určení pohlaví podle kostry metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH

In the present paper the role of linear discriminant analysis is delimitated all along with a range of problems which could be solved using this method in appropriate conditions. The analysis is given of possibilities on how to involve the decrease in number of variables in a model, and how to predict probability of incorrect classification. As an example of application, the prediction of dog survival is calculated in radiobiological experiment on the basis of biochemical and hematological values which were obtained by the 2nd day after the irradiation as well as an error probability was determined.

• MeSH
• diskriminační analýza * MeSH
• Publikační typ
• anglický abstrakt MeSH
• časopisecké články MeSH

The 24-hours blood pressure monitoring (BPM) is a reliable technique largely used for hypertension diagnosis in clinical practice. Presence of the lack of a nocturnal decrease in blood pressure values is one differentiating criterion between essential and secondary hypertension. New useful method of mathematical and statistical processing of BPM data using the linear discriminant analysis (LDA) was described. This statistical analysis was applied for 123 BPM measurements from patients with essential hypertension without and with different kidney diseases including hemodialyzed patients. The control group was normotensive healthy volunteers. The LDA method successfully separate group of patients with essential hypertension from patients with secondary renal hypertension but the error of return ranking to these groups was 28.5%. The recalculation for groups-controls, hypertensive patients and hypertensive hemodialyzed patients leads to decreasing of the error of return ranking to 5.7%. This retrospective study of nonhomogeneous groups could not clearly differentiate groups of hypertensive patients. The new prospective study on accurately definite groups of patients can eliminate these problems.

• MeSH
• ambulantní monitorování krevního tlaku * MeSH
• diskriminační analýza MeSH
• hypertenze diagnóza   etiologie   MeSH
• lidé MeSH
• měření krevního tlaku metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• anglický abstrakt MeSH
• časopisecké články MeSH

Sex diagnosis of skeletal remains represents one of the main problems in the forensic osteological practice. The purpose of the paper was to apply discriminant analysis as the method of its solution--in the set of measurements carried out on the hyoid bone. Classical procedure by R. A. Fisher yielded the set of linear discriminant functions applicable also in cases of injured bones, which is the relatively frequent situation. Information efficiency of the method proved to be quite satisfactory--in case of complete set of all six measurement level of misclassification should not reach over four per cent of the items to diagnose.

• MeSH
• analýza určování pohlaví * MeSH
• lidé MeSH
• os hyoideum anatomie a histologie   MeSH
• soudní lékařství metody   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

A novel approach for the authentication of olive oil samples representing different quality grades has been developed. A new type of ion source, direct analysis in real time (DART), coupled to a high-resolution time-of-flight mass spectrometer (TOFMS) was employed for the comprehensive profiling of triacylglycerols (TAGs) and/or polar compounds extracted with a methanol-water mixture. The main parameters influencing the ionization efficiency of TAGs were the type of sample solvent, degree of sample dilution, ion beam temperature, and presence of a dopant (ammonia vapors). The ionization yield of polar compounds depended mainly on a content of water in the extract and ion beam temperature. Using DART-TOFMS, not only differentiation among extra virgin olive oil (EVOO), olive pomace oil (OPO) and olive oil (OO) could be easily achieved, but also EVOO adulteration with commonly used adulterant, hazelnut oil (HO), was feasible. Based on the linear discriminant analysis (LDA), the introduced method allowed detection of HO addition of 6 and 15% (v/v) when assessing DART-TOFMS mass profiles of polar compounds and TAGs, respectively.

• MeSH
• časové faktory MeSH
• diskriminační analýza MeSH
• hmotnostní spektrometrie metody   MeSH
• lineární modely MeSH
• methanol chemie   MeSH
• oleje rostlin analýza   MeSH
• olivový olej MeSH
• senzitivita a specificita MeSH
• voda chemie   MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH
• Názvy látek
• methanol MeSH
• oleje rostlin MeSH
• olivový olej MeSH
• voda MeSH

The hyoid bone is characterized by sexually dimorphic features, enabling it to occasionally be used in the sex determination aspect of establishing the biological profile in skeletal remains. Based on a sample of 298 fused and non-fused hyoid bones, the present paper compares several methodological approaches to sexing human hyoid bones in order to test the legitimacy of osteometrics-based linear discriminant equations and to explore the potentials of symbolic regression and methods of geometric morphometrics. In addition, two sets of published predictive models, one of which originated in an indigenous population, were validated on the studied sample. The results showed that the hyoid shape itself is a moderate sex predictor and a combination of linear measurements is a better representation of sex-related differences. The symbolic regression was shown to exceed the predictive powers of linear discriminant function analysis when two models based on a logistic and step regression reached 96% of correctly classified cases. There was a positive correlation between discriminant scores and an individual's age as the sex assessment was highly skewed in favour of males. This suggests that the human hyoid undergoes age-related modifications which facilitates determination of male bones and complicates determination of females in older individuals. The validation of discriminant equations by Komenda and Černý (1990) and Kindschud et al. (2010) revealed that there are marked inter-population and inter-sample differences which lessened the power to correctly determine female hyoid bones.

• Klíčová slova
• Geometric morphometrics, Hyoid bone, Linear discriminant function analysis, Sex determination, Symbolic regression,
• MeSH
• analýza rozptylu MeSH
• diskriminační analýza MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• lineární modely MeSH
• mladý dospělý MeSH
• multivariační analýza MeSH
• odchylka pozorovatele MeSH
• os hyoideum anatomie a histologie   MeSH
• reprodukovatelnost výsledků MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• software MeSH
• soudní antropologie MeSH
• určení pohlaví podle kostry metody   MeSH
• zobrazování trojrozměrné MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH