MNBS081c Biostatistics - practice

Faculty of Medicine
Spring 2018
Extent and Intensity
0/1/0. 1 credit(s). Type of Completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
MVDr. Halina Matějová (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Supplier department: Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Timetable
Tue 14:00–15:40 F01B1/709
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives (in Czech)
Předmět je koncipován jako úvodní a prakticky orientovaný kurz zaměřený na širokou oblast aplikace analýzy dat a informačních technologií v medicíně. Důraz je kladen na řízení a správu dat klinických studií a základní aspekty provozní informatiky zdravotnických zařízení. V oblasti analýzy dat student projde teoretickým výkladem základů jednorozměrných a vícerozměrných metod a seznámí se s problematikou optimalizace experimentálních plánů. Důraz je kladen i na praktickou stránku výuky a veškeré výpočetní techniky jsou procvičovány s pomocí běžně dostupných softwarových nástrojů (Statistica for Windows, SPSS). Studenti budou podrobně seznámeni se všemi aspekty správy a hodnocení dat klinických studií, především stanovení nutné velikosti vzorku, nastavení pravidel managementu dat, randomizace při náběru pacientů, průběžné a závěrečné statistické hodnocení. Aplikace informačních technologií se zaměřují na obecné principy přístupu uživatele k výpočetním zdrojům a konkretizují je vždy na různých implementacích počítačových sítí. Posluchač získá teoretické a praktické poznatky z oblasti tvorby a správy databází a naučí se prakticky využívat dnes běžně přístupné zdroje místních počítačových systémů, jejich sítí a jajeich připojení k Internetu. V průběhu kurzu budou posluchači rovněž zdokonaleni v užívání produktů MS Office.
Syllabus
  • BLOCK A. - Fundamentals of Statistics data analysis in clinical research and practice - an introduction to the basic principles of statistical analysis . Probabilistic presentation of results , principles of planning research , basic hypothesis testing . Types of data in clinical research and the possibilities of their representation . Specifics of clinical data and their implications for analysis. Description of data quantifying variability and parameter selection center layout. Distribution function. Understanding the concepts of calibration, the forecast model. Model distributions and their practical use. Estimates of confidence intervals , presentations variance estimates , arithmetic mean, geometric mean and median. Summary statistics of continuous and discrete data . Preparation of data for analysis. Graphical tools . Transforming data. Quality control data to find outliers , the use of computer technology. The theory of hypothesis testing . Univariate statistical methods in comparative tests , parametric and nonparametric methods . Continuous and discrete data. Basics of correlation and regression analysis : Principles correlation analysis. Regression analysis . Principles of multivariate analysis. Multivariate regression , logistic regression. Cluster analysis , factor analysis, discriminant analysis . Data mining. Statistical tests used in the evaluation of diagnostic tests : discriminant analysis , classification of subjects , ROC analysis, the sensitivity and specificity of the tests. Basics of survival analysis . Fundamentals of epidemiological data analysis and evaluation of population risks . Standardization of epidemiologic data, analysis of long-term trends predictive analysis. EPI INFO . BLOCK B - Data management in health care, information technology applications User's access to the computer , its profile , the local data. Operating System . The types of operating systems, protected and unprotected access . Network - transfer of information . Data transfer, remote login and work on a remote node , electronic mail, share peripherals . Joining computers. Low-speed peripherals (RS - 232C) . Network peripherals . Networking , Internet . Types of networks , the network of networks . The network type IP . Internet . History and principles of IP . Layers networks. Network services . File transfer, ftp. Sharing peripherals. Electronic mail, SMTP and POP3 , IMAP . Other network services , remote login , telnet, rlogin , talk , talk , write. Information services . WWW - URL , html , reader, writer . Network information systems , database processing . Authorization networks . Principles of formation of databases and data management with regard to data quality (QA / QC). Security and data backup , export, import , monitoring and data transfer . Options off- line and on- line communication. Data Capture - Data Manager role in clinical trials and practice , existing standards . Check the input data : logical links reentry . Privacy , legal aspects of medical informatics. BLOCK C. - planning , management and evaluation of clinical studies Basic terminology , ethical and legal aspects. Definition of basic terms : Clinical Drug Evaluation (KHL ) . Phase I- IV . Submitter . The investigator . Monitor. Statistics. Subjects. Contract research organization (CRO ) . Protocol . CRF.ICH GCP . Organization Studies : Communication with SIDC , documentation. Insurance KHL . Request for authorization / notification , amendments , annual report , early termination , final report . Ethical considerations: Informed consent / information for the patient. Declaration of Helsinki . Legal aspects: The main legislative sources in the Czech Republic and the EU harmonization. Analysis of the data in the KHL . Design KHL parallel arrangement . Cross -over a factorial design. Phase I- IV . Minimum Statistical Data Analysis : Data Types in the KHL . Presentation of data (descriptive statistics) . Hypothesis testing in the KHL . Optimization Factors affecting sample size sample size. The basic formula . Software tools. Applied Data Analysis in the KHL . Protocol . The course KHL - data management. summary report of the KHL Randomization and continuous monitoring of the planned experiment. The principle of randomization techniques , the principle of randomness. Complete randomization . Permuted block randomization . Stratification . The adaptive randomization techniques . Software to ensure randomization procedures , protocol functions , interim reports and summarize data base .
Teaching methods
drills
Assessment methods
credit
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2019, spring 2020, spring 2021, spring 2022, spring 2023, spring 2024, spring 2025.
  • Enrolment Statistics (Spring 2018, recent)
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