Biostatistics I - Introduction for epidemiologists
Doctoral course within the doctoral programme in Epidemiology
Course number: 1579
Credit points: 3
Course dates: Week 1: September 22, 23, 26, 27, 28, exam September 30.
Week 2: October 5, 6, 7, 10, 11, exam October 13
The course aims to introduce statistical concepts and methods with emphasis on applications in epidemiology and public health.
After successfully completing this course students should be able to:
- define the concept of probability, laws of probability, and make simple probability calculations. (S2)
- suggest a statistical distribution to describe a naturally occurring phenomenon and evaluate the appropriateness of the distribution given real data. (S3)
- present appropriate descriptive statistics for an epidemiological study. (S2)
- explain the difference between hypothesis testing and interval estimation and the relation between p-values and confidence intervals. (S3)
- suggest an appropriate statistical test for a comparison of two groups, perform the hypothesis test using standard statistical software, and interpret the results. (S3)
- estimate and interpret three alternative measures of association between binary exposures and binary outcomes and discuss the relative merits of each measure for a given research question. (S3)
- explain the concept of confounding in epidemiological studies and demonstrate how to control/adjust for confounding using stratified analysis. (S2)
- explain the basis of the linear regression model, fit a linear regression model using standard statistical software, assess the fit of the model, and interpret the results. (S2)
Learning outcomes are classified according to Bigg's structure of the observed learning outcome (SOLO) taxonomy: (S1) uni-structural, (S2) multi-structural, (S3) relational, and (S4) extended abstract.
Contents of the course
The course introduces classical statistical concepts and methods with emphasis on methods used in epidemiology and public health. Topics covered include: the importance of statistical thinking; types of data (nominal, binary, discrete and continuous variables); data summary measures; contingency tables; graphical representations; notions of probability; probability models (distributions); principles of statistical inference; parameter estimation (mean, proportion (prevalence), incidence and ratios); concepts of confidence intervals and hypothesis tests; and a general introduction to correlation and linear regression models.
Literature and other teaching material
Kirkwood BR. Essentials of Medical Statistics. 2nd ed. John Wiley & Sons; 2003.
Rabe-Hesketh S, Everitt BS. A Handbook of Statistical Analyses Using Stata. 4th ed. College Station: Stata Press; 2006.
Juul S. An Introduction to Stata for Health Researchers. College Station: Stata Press; 2006.
Dawson B, Trapp R. Basic & Clinical Biostatistics. 4th ed. McGraw-Hill Medical; 2004
Woodard M. Epidemiology: Study Design and Data Analysis. 2nd ed. Chapman & Hall;2004