Big Data Analytics and Machine Learning II – Biostatistics and Epidemiology in Translational Medicine

On completion of the course, the participants should be able to:

Knowledge

• List and distinguish common biases and pitfalls in the analysis of observational studies.
• Identify and discuss the potential problems with drawing causal conclusions from observational studies, and describe possible approaches to circumvent these problems.
• Explain the different properties including their advantages and limitations of a number of statistical and machine learning methods for causal inference.

Skills

• Employ tools from causal inference to clearly translate a scientific question to a well-defined causal and statistical parameter.
• Utilize causal diagrams to identify and vizualize confounding and selection biases.
• Exercise critical thinking generally in scientific collaboration.
• Apply inverse probability weighting, standardization and targeted learning in simple statistical analyses to draw causal inference from observational studies.

Competences

• Discuss the general problem of confounding and its critical impact on inference.
• Discuss and address the effect of time-dependent variables and confounders, and their influence on analysis and inference.
• Understand the central aspects of observational data analysis, and be able to discuss and communicate these.