DS812: Introduction to Basal Biostatistical Terms and Regression

• Give the competence to identify basic experimental design and relevant statistical analysis methods. 
• Give skills to execute simple statistical analyses, fitting of models and model criticism.
• Give knowledge and understanding of risk-evaluation in relation to different exposures and covarying factors.

• Understand the measurement of effect and evaluation of risk due to differential exposure. 
• Understand and apply of uncertainty descriptions of estimated effects (e.g. confidence interval)
• Understand and critical appraise the statistical hypothesis test paradigm
• Describe and interpret essential characteristics of a diagnostic test
• Show insight into the necessity to adjust for possible influence factors additional to exposure in observational or experimental studies
• Understand the difference between explanatory and predictive modelling
• Interpret and differentiate between the terms interaction and confounding of factors
• Identify of a statistical analysis plan based on the substantial research question and the collected data
• Perform simple statistical analyses with help of linear and logistic regression covering effect estimation and description of the corresponding uncertainty, as well as hypothesis testing and model evaluation.
• Translate the statistical analysis results to the Public Health research domain.

• Aspects of simple clinical and observational experimental design
• Estimation to compare to intervention groups
• Evaluation of diagnostic tests (sensitivity and specificity)
• Estimation of linear and logistic regression models
• Non parametric tests
• Analysis of simple contingency tables
• Poweranalysis for the comparison of two interventiongroups wrt. to differences in means or proportions
• Short introduction to the statistical analysis program R

• Intro phase: 12 hours
• Skills training phase: 12 hours, hereof Tutorials: 12 hours

The introductory phase consists of 4 times 3 lectures where the central terms are introduced. In the training phase the problems are identified and solved with real data.
At the end of the course a, individual report is written where one analyses one or two small data-sets and answers up to 30 questions.