This course is designed as an applied introduction to survival analysis, the study of the length of time until an event of interest occurs. Stata will be used for the hands-on computer exercises throughout this course.
Dr Mark Griffin is the Director of Insight Social Research & Statistics (https://www.insightrsa.com/industry-social-research). Insight focuses on research methodologies (including survey design and statistics) for public health, monitoring and evaluation for government and non-government organizations, and academic research. Insight has a secondary interest in providing IT services (as a Microsoft Business Partner).
Insight is based at the Gold Coast Health and Knowledge Precinct. The Precinct contains Griffith University Gold Coast, the Gold Coast University Hospital, the Gold Coast Private Hospital, and the Cohort and Lumina tech parks. Insight provides research, consulting, training, and IT support services for clients across the Precinct and for the broader international community.
To date he has presented over 100 two-day and 40 five-day workshops in statistics around Australia
Survival analysis is used to study the length of time until an event of interest occurs. Examples of such events include the time when a patient dies (or indeed is classified as disease-free), when a criminal re-offends, and when a customer makes their next purchase. As we study the time until such events researchers may be interested to know whether different interventions (such as medication or marketing) will influence the time until the event. This course is designed to introduce participants to the field of survival analysis where only a basic prior knowledge of statistics will be assumed at the start of the course.
Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.
The target audience for this course is researchers working with large, complex datasets that are using the techniques of survival analysis for the first time.
Day 1 – The foundation of survival analysis
On this first day we shall provide a foundation to the topic of survival analysis. We shall describe:
- common applications where survival analysis might be an appropriate technique, and the characteristics of datasets where survival analysis may be employed
- the similarity and differences between survival analysis and longitudinal data analysis
- simple descriptive and graphical techniques for describing survival data
- we shall also describing the mechanism of censoring (where not all participants may be followed until the time at which they have their event, eg. A patient who lives for many years after the first symptoms of a disease)
Day 2 – Regression models for modelling survival data
On this second day we will describe a major approach for assessing whether a set of covariates are predictors for whether study participants will experience a shorter or longer duration until the time of their event (eg. Patients who receive a treatment versus a placebo). During this time we shall discuss:
- the Proportional Cox Hazards Model
- how to interpret the results of a Proportional Cox Hazards Model
- measures of model fit
Day 3 - Stratified Proportional Cox Hazards Models
On this third day we will discuss the Proportional Cox Hazards Assumption, how to assess whether this assumption has been met, and what to do if this assumption is not valid. In essence the Proportional Cox Hazards Assumption assumes that the survival function for different groups of participants will have the same shape over time apart from a multiplicative constant dependent on predictors such as whether the participant has received the intervention. The Stratified Proportional Cox Hazards Model is utilized when this Assumption is not valid.
Day 4 – Time-Varying Covariates
In the simplest Cox Hazards model it is assumed that the predictive covariates are held constant over time (covariates such as whether the participant received the intervention or not). On this day we shall describe how to include covariates that vary over time into the Cox Hazards model.
Day 5 – Other Topics and Own data
On this last day we shall provide a brief introduction to more advanced topics within survival analysis, and will have time for participants to apply the methods of this course to their own datasets. More advanced topics that we shall briefly discuss include:
- joint longitudinal-survival models (where we predict a longitudinal outcome and a time-to-event variable in the same model)
- recurrent event models (where participants can experience more than event, such as a criminal that commits more than one crime)
This course will take place in a computer lab. All equipment will be supplied. You are encouraged to bring a data set and/or research problem with you.
Participants must have completed an introductory course in statistics or have equivalent experience. While this workshop will be taught using Stata it is not essential that participants have had prior exposure to Stata.
Other references include:
- Applied Survival Analysis: Regression Modeling of Time to Event Data 2nd Edition (2008). David W. Hosmer Jr., Stanley Lemeshow, Susanne May
Q: Do I have to have any prerequisites to do this course?
A: Yes, see recommended background section for details.
The instructor's bound, book length course notes will serve as the course texts.
Stata is distributed in Australia and New Zealand by Survey Design and Analysis Services.