R4 - Survival analysis & Biomarker data

Survival analysis and biomarker data / Statistics in medicine

This course will cover methods for survival analysis including visualisation techniques such as Kaplan-Meier plots, and regression models such as Cox proportional hazards regression. During the second day you will learn how to best analyse biomarker data, which has become a vital part of modern medicine.

Many studies are concerned with the time until an event happens: time until an individual contracts a disease, time until a patient diagnosed with a disease dies, and so on. On day one of this course we study methods for survival analysis used for analysing such data. Examples include visualisation techniques such as Kaplan-Meier plots, and regression models such as Cox proportional hazards regression – and newer regression models that often are better than the classical Cox model. In addition, we learn how to handle competing risks, recurrent events and time-varying variables in survival models.

In the second day of the course we learn how best to analyse biomarker data, which has become a vital part of modern medicine. Biomarker measurements rarely follow a normal distribution, and often have detection limits, meaning that some measurement will fall below the lowest levels of the biomarker that the laboratory analysis can detect. We can still make use of these nondetects if we use the right statistical methods. We study methods tailored to such data, including regression, visualisation, techniques for finding biomarkers related to diseases, and understanding correlations between biomarkers.