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Survival analysis is the study of time-to-event outcomes which is widely applied in health research.  Special methods are required to analyse such data due to issues of censoring and truncation. This one-day workshop focuses on two key issues that are often encountered in health and medical studies and which require extension of basic survival analysis methods: competing risks and time-dependent covariates. Competing risks are events that preclude observation of the event of interest (e.g. death from injury prevents observation of death from cancer recurrence). We will show how to analyse survival data with competing risks using the recommended multi-state model framework, discuss the pitfalls of some approaches, and introduce other types of useful multi-state models for medical research. Time-dependent covariates are variables that change over time (e.g. blood pressure, body mass index, drug intake) and their observation can potentially elucidate the complex time-dynamics of health processes. We will show how to appropriately incorporate such variables into time-to-event analyses. The lectures will focus on interpretation, with example studies used to illustrate the different concepts.

Two computer practicals will provide participants with the tools to implement the approaches. Syntax will be provided in both Stata and R. Basic knowledge of at least one of these software packages and familiarity with introductory level survival analysis are required.

Timetable