Load:

1. komponenta
Lecture type  Total 
Lectures 
30 
Exercises 
15 
* Load is given in academic hour (1 academic hour = 45 minutes)

Description:

COURSE AIMS AND OBJECTIVES: The aim of the course is to introduce students with statistical methods frequently used in biomedical science. The objective of the course is to teach students to independently compile, statistically analyze, present and analyze compiled data using SAS. To enable students to discuss and make conclusions based on already analyzed data. To introduce students to the possibility of various interpretations of the same problem analyzed in different ways.
COURSE DESCRIPTION AND SYLLABUS:
ANOVA. One, Two and Three factors models. Modeling Interactions. Modeling Trend. Comparing using SAS.
Repeated Measure ANOVA. Experimental Design Basics. Between and Within subjects effects. Fixed and Random Effects.
Contingency Table Analysis. Sensitivity and Specificity of Diagnostic Tests. Positive and Negative Predictive Value. Likelihood ratio.
ROC (Receiver Operating Characteristic) Curves.
Survival analysis: The Survival Function and the Hazard Function for Continuous Random Variable. Parametric Methods.
The Survival Function and the Hazard Function for Discrete Random Variable. RightCensored, Leftcensored and Interval Censored Dana. Nonparametric Estimation. Residuals. LogRank and Wilcox on tests.
Cox Regression Model: The Proportional Hazards Model. Maximum Likelihood Estimation. Test of Adequacy of the Model. Proportion of Explained Variation (marginal, partial). Modeling Interactions. Difference Between Statistical and Biological Interaction. Choosing strategies.
Generalized Linear Models.
Logistic Regression.

Literature:

 R. R. Sokal, F. J. Rohlf: Biometry
 J. K. Lindsey: Applying Generalized Linear Models
 C. S. Davis: Statistical Methods for the Analysis of Repeated Measurements
 J. P. Klein, M. L. Moeschberger: Survival Analysis, 2nd edition
 P. McCullagh, J. A. Nelder: Generalized Linear Models
 D. W. Hosmer, S. Lemeshow: Applied Logistic Regression, 2nd edition
 C. E. McCulloch, S. R. Searle: Generalized, Linear and Mixed Models

Prerequisit for:

Enrollment
:
Passed
:
Mathematical statistics
Passed
:
Statistics lab 1
