BACKGROUND: Vaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities. METHODS: This work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome. RESULTS: Clinical trial simulations, in which the true relationship between CoP data and clinical outcome probability is a sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy. CONCLUSIONS: This study proposes and evaluates a novel approach for assessing baseline demographic covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions.

• Klíčová slova
• Baseline covariates, Correlate of protection, Logistic regression, Relative risk, Vaccine efficacy,
• MeSH
• demografie statistika a číselné údaje   MeSH
• klinické zkoušky, fáze III jako téma statistika a číselné údaje   metody   MeSH
• lidé MeSH
• logistické modely MeSH
• počítačová simulace MeSH
• randomizované kontrolované studie jako téma statistika a číselné údaje   metody   MeSH
• účinnost vakcíny * statistika a číselné údaje   MeSH
• vakcinace statistika a číselné údaje   metody   MeSH
• vakcíny terapeutické užití   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• vakcíny MeSH

The importance of environmental sustainability is becoming more and more obvious, so the rationale behind long-term usage of solely non-renewable energy sources appeared questionable. This study aims to identify, using Kaplan-Meier survival analysis and logistic regressions, the main determinants that affect the duration of Russian non-renewable energy exports to different regions of the world. Data were retrieved from the databanks of the World Development Indicators (WDI), World Integrated Trade Solution (WITS), and the French Centre for Prospective studies and International Information (CEPII). The obtained results point to the fact that approximately 52% of energy products survive beyond their first year of trading, nearly 38% survive beyond the second year, and almost 18% survive to the twelfth year. The survival of Russian non-renewable energy exports differs depending on the region, and the affecting factors are of different importance. The duration of Russian non-renewable energy exports is significantly linked to Russia's GDP, Total export, and Initial export values. A decline in Russia's GDP by 1% is associated with an increasing probability of a spell ending by 2.9% on average, in turn growing Total export and Initial export values positively linked with the duration of non-renewable energy exports from Russia. These findings may have practical relevance for strategic actions aimed at approaching both energy security and environmental sustainability.

• Klíčová slova
• Russia, discrete-time model, environmental sustainability, non-renewable energy exports, survival analysis,
• MeSH
• ekonomický rozvoj * MeSH
• logistické modely MeSH
• oxid uhličitý * MeSH
• prospektivní studie MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Rusko MeSH
• Názvy látek
• oxid uhličitý * 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

Classifying a measurable clinical outcome as a dichotomous variable often involves difficulty with borderline cases that could fairly be assigned either of the two binary class memberships. In such situations the indicated class membership is often highly subjective and subject to, for instance, a measurement error. In other situations the intermediate level of a three-level ordinal factor may sometimes be explicitly reserved for cases which could likely belong to either of the two binary classes. Such indefinite readings are often eliminated from the statistical analysis. In this article we review conceptual and methodological aspects of employing proportional odds logistic regression for a three level ordinal factor as a suitable alternative to ordinary logistic regression when dealing with limited uncertainty in classifying clinical outcome as a binary variable.

• MeSH
• ateroskleróza diagnostické zobrazování   MeSH
• cholesterol krev   MeSH
• interpretace statistických dat * MeSH
• kouření MeSH
• krevní glukóza metabolismus   MeSH
• lidé MeSH
• logistické modely * MeSH
• prediktivní hodnota testů MeSH
• statistické modely * MeSH
• ultrasonografie MeSH
• vápník krev   MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH
• Názvy látek
• cholesterol MeSH
• krevní glukóza MeSH
• vápník MeSH

At the beginning of 2020 there was a spinning point in the travel behavior of people around the world because of the pandemic and its consequences. This paper analyzes the specific behavior of travelers commuting to work or school during the COVID-19 pandemic based on a sample of 2000 respondents from two countries. We obtained data from an online survey, applying multinomial regression analysis. The results demonstrate the multinomial model with an accuracy of almost 70% that estimates the most used modes of transport (walking, public transport, car) based on independent variables. The respondents preferred the car as the most frequently used means of transport. However, commuters without car prefer public transport to walking. This prediction model could be a tool for planning and creating transport policy, especially in exceptional cases such as the limitation of public transport activities. Therefore, predicting travel behavior is essential for policymaking based on people's travel needs.

• Klíčová slova
• COVID-19, mobility, multinomial regression model, transport,
• MeSH
• COVID-19 * MeSH
• cyklistika MeSH
• doprava MeSH
• lidé MeSH
• logistické modely MeSH
• pandemie * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.

• Klíčová slova
• image colour properties, image processing, image texture properties, machine vision system, supervised classification,
• MeSH
• dieta MeSH
• logistické modely MeSH
• Oncorhynchus mykiss MeSH
• support vector machine * MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH

In the present paper the prediction method using the logistic regression is explained, and the range of problems for its use is deliminated. The example is presented of mentioned method's application on how to predict the dog survival in a radiolobiological experiment. The obtained results are compared with the prediction of outcome using the linear discriminant function. Both models are identic in a proportion of erroneously classified subjects. This method may be diagnostically supportive in ranging individuals to one of two groups delimitated previously.

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

Non-invasive breath analysis has been used to search for volatile biomarkers of lungs and airways infection by Pseudomonas aeruginosa, PA, in cystic fibrosis patients. The exhaled breath of 20 PA-infected patients and 38 PA-negative patients was analysed using selected ion flow tube mass spectrometry, SIFT-MS. Special attention was given to the positive identification and accurate quantification of 16 volatile compounds (VOCs) as assured by the detailed consideration of their analytical ion chemistry occurring in the SIFT-MS reactor. However, the diagnostic sensitivity and specificity of the concentrations of any of the 16 compounds taken individually were found to be low. But when a linear combination of the concentrations of all 16 VOCs was used to construct an optimised receiver operating characteristics (ROC) curve using a linear logistic model, the diagnostic separation of PA-infected patients relative to the PA-negative patients was apparently good in terms of the derived sensitivity (89%), specificity (86%), and the area under the ROC curve is 0.91. Four compounds were revealed by the linear logistic model as significant, viz. malondialdehyde, isoprene, phenol and acetoin. The implications of these results to PA detection in the airways are assessed. Whilst such a metabolomics approach to optimise the ROC curve is widely used in breath analysis, it can lead to misleading indications. Therefore, we conclude that the results of the linear logistic model analyses are of limited immediate clinical value. The identified compounds should rather be considered as a stimulus for further independent studies involving larger patient cohorts.

• MeSH
• biologické markery analýza   MeSH
• cystická fibróza mikrobiologie   MeSH
• dechové testy metody   MeSH
• dítě MeSH
• dospělí MeSH
• hmotnostní spektrometrie MeSH
• lidé MeSH
• logistické modely MeSH
• metabolom MeSH
• metabolomika MeSH
• mladiství MeSH
• mladý dospělý MeSH
• předškolní dítě MeSH
• pseudomonádové infekce diagnóza   MeSH
• Pseudomonas aeruginosa fyziologie   MeSH
• ROC křivka MeSH
• senzitivita a specificita MeSH
• těkavé organické sloučeniny analýza   MeSH
• vydechnutí * MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• biologické markery MeSH
• těkavé organické sloučeniny MeSH

BACKGROUND: Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. OBJECTIVE: We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules. STUDY DESIGN: This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves. RESULTS: Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%. CONCLUSION: Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally.

• Klíčová slova
• International Ovarian Tumor Analysis, Simple Rules, adnexa, color Doppler, diagnosis, diagnostic algorithm, logistic regression analysis, ovarian cancer, ovarian neoplasms, preoperative evaluation, risk assessment, ultrasonography,
• MeSH
• hodnocení rizik MeSH
• kohortové studie MeSH
• lidé MeSH
• logistické modely MeSH
• nemoci děložních adnex diagnostické zobrazování   MeSH
• nemocnice MeSH
• onkologická péče - zařízení MeSH
• prediktivní hodnota testů MeSH
• průřezové studie MeSH
• ROC křivka MeSH
• senzitivita a specificita MeSH
• ultrasonografie dopplerovská barevná MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• validační studie MeSH

The paper presents a brief overview of statistical methods used in clinical and experimental medicine, ranging from basic indicators and parameters of descriptive statistics and hypotheses testing (parametric as well as non-parametric methods) to a description of the most frequently used multivariate methods in medical scientific publications, to logistic regression. The paper also describes Principle Component Analysis (PCA), which is one of the methods used to decrease a data dimensionality. The proper use of statistical methods is demonstrated on specific clinical cases.

• MeSH
• analýza hlavních komponent MeSH
• analýza rozptylu MeSH
• interpretace statistických dat MeSH
• logistické modely MeSH
• multivariační analýza MeSH
• neparametrická statistika MeSH
• regresní analýza MeSH
• statistika jako téma * MeSH
• Publikační typ
• anglický abstrakt MeSH
• časopisecké články MeSH
• přehledy MeSH