Selected Topics in Biostatistics

Subject description

Students choose one of the following three subjects:

Survival analysis

Basics:
- Censoring, survival curve, hazard function
- Regression models in survival analysis
- Counting processes
Specific methods and chapters
- Goodness of fit
- Explained variation
- Relative survival
- Linear model for censored data
- Pseudo-observations
- Competing risks and multistate models

2. Methods for analysing highdimensional data with applications in bioinformatics:

Basics:
- Statistical properties of highdimensional data
- Highdimensional data in biomedical research
- Methods for multiple testing and classification
Specific methods and chapters:
- Types of errors in multiple testing
- Adapted and non-adapted p-values and the control of type I error
- Multivariate permuation methods
- Multivariate classification functions
- Estimation of predictive accuracy

3. Design and analysis of experiments

Basics:
- Overview of the basic ideas (vsebinsko pomembni pojmi)
- Basics of experimental design: properties, usage, advantages and disadvantages
- More complex experimental designs: properties, usage, advantages and disadvantages
- Statistical analysis: parametric and nonparametric approaches
- Generalized linear models and their application in the analysis of experiments
Specific methods and chapters:
- Modelling: various approaches and their usage
- Response surfaces

The subject is taught in programs

Objectives and competences

The aim of the course is to give an overview of some topics and specific methods in biostatistics, with a focus on survival analysis, methods for analysing highdimensional data with applications in bioinformatics and methods for design and analysis of experiments in various fields of science.

Teaching and learning methods

Lectures.
Written projects of literature overviews that form the basis of the student’s doctoral work.
Consultations.

Expected study results

In-depth knowledge of the existing methods in the chosen field will form the basis of student’s doctoral work.

Basic sources and literature

Collett D (2003): Modelling Survival Sata in Medical Research. Chapman & Hall.
Kalbfleisch JD, Prentice RL (2002): The Statistical Analysis of Failure Time Data. New York:Wiley.
Andersen PK, Borgan O, Gill R, Keiding N (1993): Statistical Models Based on Counting Processes. New York: Springer.
Simon RM, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y (2004). Design and Analysis of DNA Microarray Investigations. Springer: New York, NY.
Dudoit S, van der Laan MJ (2008). Multiple Testing Procedures with Applications to Genomics. Springer Series in Statistics. Springer: New York, NY.
Bishop CM (2006). Pattern Recognition and Machine Learning. New York: Springer.
Box G, Hunter S, and Hunter WG (2005). Statistics for Experimenters: Design, Innovation, and Discovery, Wiley.
Mead R, Curnow R & Hasted A. (2002). Statistical Methods in Agriculture and Experimental Biology, Third Edition. Chapman & Hall/CRC Press.
Steel RGD., Torrie JH., Dickey D (1997). Principles and Procedures of Statistics. A Biometrical Approach. McGraw-Hill.
Quinn GP., Keough MJ (2002). Experimental design and data analysis for biologists. Cambridge University Press.
Kuehl RO (2000). Design of experiments: statistical principles of research design and analysis. Duxbury/Thomson Learning.