Selected Topics in Biostatistics

Subject description

Students choose one of the following three subjects:

  1. 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.


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