BMAK051 Clinical data analysis

Faculty of Medicine
autumn 2022
Extent and Intensity
0/1.3/0. 2 credit(s) (plus 1 credit for an exam). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Prerequisites
None - basic course.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Intensive course for PhD students, doctors or specialists of other specializations. The course is aimed on basic principles of analysis of data, especially clinical data; it should provide information on graphical presentation of data, hypothesis testing together with the basics of multivariate analysis, survival analysis and predictive modelling of clinical data. The students will be able to understand principles of statistical tests, multivariate analysis and predictive modelling and will be provided by set of information sources of data analysis (books, journals, www pages). Examples provided in SW STATISTICA for Windows are the integral part of the course.
Learning outcomes
At the end of the course the students are able to:
Define structure of dataset for statistical analysis;
Visualize the data and interpret data visualisation;
Identify correct methods of descriptive statistics;
Formulate hypothesis for statistical testing;
Select the correct statistical tests for hypotheses confirmation/refusal;
Interpret results of statistical evaluation, both analysis of own data and statistics in scientific literature;
Assess the applicability of statistical methods on various types of data.
Syllabus
  • Statistics in medical research basic principles * Basic principles of statistical analysis. Probability in presentation of analysis results. Basics of experimental design and hypothesis testing. * Nominal, ordinal and continuous data in clinical research and their visualization. Special characteristics of clinical data and their subsequences for analysis. Description of data, descriptive statistic, distribution. Calibration, prognosis, models. * Statistical distributions and their usage as model distribution (normal, log-normal, binomial, Poisson, Student, F, Chi square) * Confidence intervals, estimation of statistical parameters and their presentation. Estimation of arithmetic mean, geometric mean, median and variability. Statistical summary of discrete and continuous data.
  • Pre-processing of data before analysis * Tools for visualization of data in their exploratory analysis /PP plots, QQ plots, normal probability plots, box-and-whisker plots, scatter plots, stem and leaf display, histograms, 3D histograms, matrix plots* face plots, contour plots, surface plots/. * Transformation of data, outliers and their importance. Advantages and problems of usage of computers in analysis of clinical data. Parametric and nonparametric techniques.
  • Univariate analysis of data * Univariate analysis of continuous data. One-sample and two-sample test. T-test for dependent and independent data. Basics of analysis of variance one way and multi-way ANOVA, post-hoc tests. Non parametric tests (Mann-Whitney test, Wald-Worowitz test, Kolmogorov-Smirnov two-sample test, Kruskal-Wallis test). Visualization and presentation of results of statistical tests. * Univariate analysis of discrete data. One-sample and two-sample test. Presentation and estimation of percentages data. Binomial test, Fisher exact test, goodness of fit test, analysis of frequency tables.
  • Correlation and regression * Basics of correlation and regression analysis. Parametric and non-parametric correlation. Linear regression. Application and visualization of correlation and regression. Basic principles of polynomial and non-linear regression. * Basic principles of multivariate and logistic regression. Multivariate and logistic regression as predictive tools for clinical data. Quality of models and their problems. Multivariate regression in prediction of clinically important parameters example. Logistic regression individualized prediction of patients. Presentation of predictive models.
  • Advanced techniques * Survival analysis. Probability of survival. Kaplan-Meier survival analysis and parameters estimates /median survival times.../. Range of approaches for comparison of two or more survival curves /Log-rank test, hazard ratio, log rank for trends, confidence intervals for survival probability/. "Cohort life tables" and their analysis of survival. Modeling of survival, Cox regression. Examples and application. Design of studies focused on survival analysis quantitative aspects of experimental design, samples size estimation. Survival analysis for stratified clinical trials. EORTC standards for experimental design of survival analysis. Internet and survival analysis: consultation on trials aimed on survival analysis, software for survival analysis. Nomograms for design of survival analysis trials. * Multivariate analysis of clinical data; introduction into modern method for analysis of huge amounts of data. Principles of multivariate methods and their application for clinical data analysis. Multivariate and univariate data analysis mutual collaboration or discrepancy? Multivariate data exploration, available tests for multivariate distribution. Multivariate similarity/distance of objects or variables review of important metrics. Dynamic regression models. Neural networks as a possible modeling technique. Data mining and automated analysis of data. Experiments optimizing; application of multivariate methods in sampling design.
Literature
  • Altman D. G. (1991) Practical statistics for medical research. Chapman and Hall. London.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • Flury B. and Riedwyl H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Snedecor G.W. and Cochran W.G. (1971). Statistical methods. Iowa State University Press.
  • Zar J.H. (1984). Biostatistical analysis. Perntice Hall. New Jersey.
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
Course is finished by written exam aimed on principles, prerequisties and correct selection of methods for solution of practical examples.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is taught: in blocks.
Note related to how often the course is taught: Výuka analýzy klinických dat proběhne v učebně B11/306 v termínu 9-13. 1. 2023 vždy v čase 15-19 hod.
Information on the extent and intensity of the course: 20.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2000, Autumn 2000, Spring 2001, Autumn 2001, Spring 2002, Autumn 2002, Spring 2003, Autumn 2003, Spring 2004, Autumn 2004, Spring 2005, Autumn 2005, Spring 2006, Autumn 2006, Spring 2007, Autumn 2007, Spring 2008, Autumn 2008, Spring 2009, Autumn 2009, Spring 2010, Autumn 2010, Spring 2011, Autumn 2011, Spring 2012, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, Autumn 2017, Spring 2018, autumn 2018, spring 2019, autumn 2019, spring 2020, autumn 2020, spring 2021, autumn 2021, spring 2022, spring 2023, autumn 2023, spring 2024, autumn 2024, spring 2025.
  • Enrolment Statistics (autumn 2022, recent)
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