Mezinárodní společnost pro klinickou biostatistiku v České republice, zapsaný spolek (ISCB ČR)

Statut společnosti

  • Mezinárodní společnost pro klinickou biostatistiku v České republice, z.s. (ISCB ČR), je dobrovolné, nezávislé občanské sdružení pracovníků s právní podobou zapsaného spolku, který má povahu odborné společnosti
  • Posláním spolku je ve spolupráci s Mezinárodní společností pro klinickou biostatistiku (International Society for Clinical Biostatistics, ISCB) přispívat k rozvoji biostatistiky v České republice
  • Odborná činnost spolku se zaměřuje na aplikaci statistických metod v biomedicíně
  • Spolek je právnickou osobou

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Aktuální oznámení

  • Visualisation of spatially smoothed biological signal recorded on landmarks, curves or surfaces
    Stanislav Katina, Zdeňka Geršlová, Vojtěch Šindlář, Mojmír Vinkler
    Ústav matematiky a statistiky
    Přírodovědecká fakulta 
    Masarykova univerzita Brno
    Kotlářská 267/2
    611 37 Brno

    Spatial interpolation and smoothing is usually done for one surface. In our case, we have random samples of such surfaces from three different domains, i.e. electrical activity of human brain and heart, and human faces. First domain, electrical activity of the human brain, characterised by event-related potentials (ERPs) can be recorded by electroencephalography (EEG) using scalp electrodes mounted on an elastic cap where the 61 electrode sites formed an equidistant grid. For the purpose of visualisation, this number of points is quite low and therefore we use 100,000 points. Second domain, electrical activity of the human heart, characterised by body surface potential maps (BSPMs) can be recorded by electrocardiography (ECG) using electrodes mounted on the skin around the chest where the 80 electrode sites formed a regular grid of 5 rows and 16 columns. For the purpose of visualisation, this number of points is quite low and therefore we use 83,520 points (145 × 576 grid). Third domain, human faces, captured by stereo-photogrammetry are characterised by about 150,000 points. These points are triangulated by about 300,000 triangles. This number of points is extremely high for the purpose of statistical analyses, therefore the coordinates of landmarks and curves sufficiently characterising the shape have to be automatically identified and this simplified model comprising about 1000 points is then used in further statistical modelling.

    In this presentation we define the model for creating a grid of points in 3D for one subject that allows us to smooth the biological signal between the (semi)landmarks (coordinates of sensors, i.e. EEG/ECG electrodes, coordinates of biological (semi)landmarks) and then interpolate the signal using thin-plate splines. Then we visualise the brain activity on a sphere (simplification of the head) and consider the projection from sphere to the Montreal Neurological Institute (MNI) template of the brain, then the ECG signal on cylinder (simplification of the torso), and human face. We also visualise directional derivatives and the shape index, the measures of local surface topology. Finally, with the methods mentioned above, we create static images of the EEG/ECG signal and human faces in several different views, i.e. frontal, lateral, vertical, oblique or other, and animate this signal across chosen directions and views in the statistical software R. These images allow for an overview of a brain/heart activity or a shape of human face and ease the bio-medical interpretations. 

    Keywords: spline smoothing, interpolation, warping, shape index, visualisation, EEG, ECG, human face

    PřednášejícíDoc. PaedDr. RNDr. Stanislav Katina, Ph.D.Ústav matematiky a statistiky, Přírodovědecká fakulta Masarykovy univerzity v Brně, Kotlářská 267/2, 611 37 Brno

    Datum: Čtvrtek 8. listopadu 2018

    Čas: 13:00 hod
    Přidáno v 8. 11. 2018 3:53, autor: Zdeněk Valenta
  • A time-domain-constrained fuzzy clustering method in biomedical data analysis
    Aleksander Owczarek, Ph.D., DSc., Eng.
    Head of the Department of Statistics
    Department of Instrumental Analysis
    Faculty of Pharmacy and Laboratory Medicine in Sosnowiec
    Medical University of Silesia in Katowice

    AbstractThe lecture deals with a presentation of a fuzzy clustering method with time-domain-constraints used in biomedical data analysis. Proposed method makes it possible to include natural constraints for biomedical data (especially biomedical signals) analysis using fuzzy clustering, that is, the neighboring samples of data belong to the same cluster. The method introduces two approaches to include the above kind of constraints. The first approach leads to the time-domain-constrained fuzzy c-regression models method. The second approach leads to the epsilon-insensitive version of the above method, which results in additional robustness for outliers and non-Gaussian noise. Finally, simulations on synthetic as well as real-life data and signals would be used to evaluate the performance and to show usefulness of the time-domain-constrained fuzzy clustering methods..  

    Keywords: fuzzy clustering, switching regression models, biomedical data analysis

    PřednášejícíAleksander Owczarek, Ph.D., DSc., Eng.
    Místo: Ústav informatiky AV ČR, v.v.i., Pod Vodárenskou věží 2, 182 07 Praha 8, místnost č. 222
    Datum: Čtvrtek 1. listopadu 2018
    Čas: 14:00 hod

    Semináři bude předcházet Valná hromada členů ISCB ČR, která proběhne od 13:30 hod na témže místě. Na programu bude volba předsedy, místopředsedy, hospodáře a revizora společnosti.
    Přidáno v 25. 9. 2018 9:29, autor: Zdeněk Valenta
  • Identifying influential observations in joint Bayesian models
    Mgr. Šárka Rusá, Dept. of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Sokolovská 83, Praha 8-Libeň, Czech Republic

    AbstractAlthough increasingly complex Bayesian models are being employed in practice, most of existing literature has not dealt with model diagnostics. We propose a Bayesian approach to the detection of influential observations or sets of observations in joint models. 
        Moreover, the usage of our method is advantageous if the investigation of case-deletion is restricted to a subset of parameters. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilised in order to estimate the case influence on the parameters. 
        The method is illustrated on a three-level dataset from the field of nursing research which was previously used to fit a mediation model of patient satisfaction with care. We focused on case-deletion on both the second and the third level of the data.  

    Keywords: Bayesian joint models; Influential observations, Importance sampling.

    Přednášející: Mgr. Šárka Rusá, KPMS, MFF UK Praha, Sokolovská 83
    Místo: Ústav informatiky AV ČR, v.v.i., Pod Vodárenskou věží 2, 182 07 Praha 8, místnost č. 222
    Datum: Čtvrtek 5. dubna 2018
    Čas: 13:30 hod

    Semináři bude předcházet Valná hromada členů ISCB ČR, která proběhne od 13:00 hod na témže místě. Na programu bude zpráva o činnosti a hospodaření společnosti za rok 2017. 
    Přidáno v 5. 4. 2018 2:02, autor: Zdeněk Valenta
  • Measurement theory and statistical models
    Zhiliang Ying, Department of Statistics, Columbia University, 1255 Amsterdam Avenue, Room 1033 , School of Social Work Building, New York, NY 10027, U.S.A.

    AbstractMeasurement theory has played a foundational role in educational, psychological and psychiatric assessment.This talk will introduce various statistical models that have been serving as key tools in measurement theory. It will provide detailed discussions on existing and new statistical models and statistical inferences thereof, with special focus on certain latent class and latent factor models as well as their extensions for categorical and counting process data. The new developments will be applied to examples in educational assessment and psychological evaluation.  

    Keywords: Measurement theory; latent class model; categorical data; counting processes; psychometrics.

    PřednášejícíProf. Zhiliang Ying, Ph.D., Professor of Statistics, Director of Graduate Studies, Department of Statistics, Chief Co-Editor of Statistica Sinica
    Místo: Praktikum KPMS, MFF UK Praha, Sokolovská 83, 1. patro
    Datum: Čtvrtek 5. října 2017
    Čas: 15:40 hod
    Přidáno v 28. 8. 2017 13:22, autor: Zdeněk Valenta
  • Teaching psychometrics and analyzing composite measurements with R and Shiny
    Patrícia Martinková [1], Adéla Drabinová [1,2], Jakub Houdek [3], Lubomír Štěpánek [3,4]

    [1] Oddělení medicínské informatiky a biostatistiky, Ústav informatiky AV ČR v.v.i., Pod Vodárenskou věží 2, 182 07 Praha 8
    [2] Katedra pravděpodobnosti a matematické statistiky, Matematicko-fyzikální fakulta, Univerzita Karlova, Sokolovská 83, 186 75 Praha 8
    [3] Fakulta informatiky a statistiky, Vysoká škola ekonomická v Praze, Nám W. Churchilla 4, 130 67 Praha 3
    [4] 1. lékařská fakulta, Univerzita Karlova, Kateřinská 32, 121 08 Praha 2

    AbstractThis work introduces ShinyItemAnalysis (Martinková et al. 2017) R package and an online shiny application for psychometric analysis of educational tests and their items, and difNLR (Drabinová et al., 2017) R package for detection of differential item functioning (DIF).

    ShinyItemAnalysis covers broad range of methods and offers data examples, model equations, parameter estimates, interpretation of results, together with selected R code, and is thus suitable for teaching psychometric concepts with R. Besides, the application aspires to be a simple tool for analysis of educational tests and other composite measurements by allowing the users to upload and analyze their own data and to automatically generate analysis report in PDF or HTML.

    The R package difNLR has been developed for detection of potentially unfair items in educational and psychological testing, analysis of so called differential item functioning, based on extensions of logistic regression model. For dichotomous data, six models have been implemented to offer wide range of proxies to Item Response Theory models. Parameters are obtained using non-linear least square estimation and DIF detection procedure is performed by either F or likelihood ratio test of submodel. For unscored data, analysis of differential distractor functioning (DDF) based on multinomial regression model is offered to provide closer look at individual item options (distractors).

    We argue that psychometric analysis should be a routine part of test development in order to gather proofs of reliability and validity of the measurement. With example of admission test to medical school we demonstrate how presented R packages and Shiny application may provide simple and free tools to routinely analyze tests and to explain advanced psychometric models to students and to test developers. Attention will also be paid to technical details of automatically generated reports. 

    Keywords: psychometrics, item response theory, detection of item bias, differential item functioning, shiny, R

    PřednášejícíPatrícia Martinková, Adéla Drabinová, Jakub Houdek
    Místo: Učebna č. 222, Ústav informatiky AV ČR, v.v.i., Pod Vodárenskou věží 2, 182 07 Praha 8
    Datum: Čtvrtek 29. června 2017
    Čas: 14:00 hod
    Přidáno v 19. 6. 2017 4:35, autor: Zdeněk Valenta
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