BPAM0411p Analysis and Data Management for Healthcare Specialisation - lecture

Faculty of Medicine
Spring 2009
Extent and Intensity
1.5/0/0. 1 credit(s). Type of Completion: zk (examination).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Timetable
Mon 23. 3. to Fri 29. 5. Thu 14:00–16:30 F01B1/709
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course is oriented on practical basics of data analysis and information technologies application in medicine. Highlighted topics are related to management of data of clinical trials and data storage in hospitals. The data analysis presented during the lectures goes from the descriptive statistics through principles of statistical testing, selected statistical tests for continuous and categorical data to basics of regression modeling and power analysis. All methods are presented using practical examples and common software (Statistica for Windows, SPSS). The subject provides basic knowledge a skills about computer's network. Main objectives can be summarized as follows: to understand the network terminology; to connect personal computer to Internet; to use network services; to reduce risk of lost of data or secret information.
Syllabus
  • A. Data analysis
  • 1. 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.;
  • 2. 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.
  • 3. 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.
  • 4. 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.
  • 5. 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.
  • 6. 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.
  • B. Information technologies
  • 7. Network - data transfer, hardware, software; Network - Internet. Types of nets, IP - network. Internet; How to connect to the Internet
  • 8. Client x server architecture, Clients, Servers, Services; Network services. FTP - file transfer, Sharing disks and printers, E-mail, services SMTP, POP3, IMAP. Other services, Remote desktop, telnet, talk, Skype;
  • 9. Authorization, Authentication, Login, Password, Cryprography; Security, reducing risk of networks transfer and communication
  • C. Management of clinical trial
  • 10. Terminology, legal topics
  • 11. Data analysis in clinical trial, design of experiment, power analysis
  • 12. Randomisation and monitoring of clinical trials
Literature
  • ZAR, Jerrold H. Biostatistical analysis. 4th ed. Upper Saddle River, N.J.: Prentice Hall, 1999, [941] s. ISBN 013081542X. info
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • ALTMAN, Douglas G. Practical statistics for medical research. 1st ed. Boca Raton: Chapmann & Hall/CRC, 1991, xii, 611. ISBN 0412276305. info
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
The course is also listed under the following terms Spring 2010, Spring 2011.
  • Enrolment Statistics (Spring 2009, recent)
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