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Statistical modelling of time-to-event data using survival analysis: an introduction for animal beha

Oliver Hooker, November 22, 2018
 

Statistical modelling of time-to-event data using survival analysis: an introduction for animal behaviourists, ecologists and evolutionary biologists (TTED01)

https://www.psstatistics.com/course/statistical-modelling-of-time-to-event-data-using-survival-analysis-tted01/

This course will be delivered by Dr. Will Hoppitt for the 21st - 25th January 2019 in Glasgow City Centre

Course Overview: Survival analysis is a set of statistical methods initially designed to analyse data giving the times at which individuals die, and assess the effect that different predictor variables have on the rate of death and is therefore useful for studying population dynamics and patterns of seabirds. However, its applications are much broader than this: it can be used to analyse any time-to-event data. Ecologists and evolutionary biologists often encounter data of this kind. Often factors influencing survival itself will be of interest. But there are many other cases, e.g. what factors influence the time of first breeding? Or the time taken to reach maturity? Animal behaviourists too will encounter this type of data frequently, e.g. what factors influence the time it takes to learn a novel behaviour pattern? Or the time to respond to a stimulus? etc. And yet the techniques of survival analysis are not generally well known by researchers in these disciplines.

In this course, you will learn how to apply survival analysis models to quantify the effect that predictor variables (continuous or discrete) have on the rate at which events occur, and how to test hypotheses about these effects. We will focus on a flexible modelling technique called the Cox proportional hazards model, which makes minimal assumptions about the underlying probability distributions. You will learn how to fit and interpret these models, how to evaluate its assumptions, and how to extend it to model time dependent variables, random effects, multistate models and competing risks models.

Course Programme

Monday 21st – Classes from 09:30 to 17:30 Module 1: Statistical modelling of rates and times Module 2: Parametric survival models and the Cox model

Tuesday 22nd – Classes from 09:30 to 17:30 Module 3: Fitting Cox models Module 4: Interpreting Cox Models

Wednesday 23rd – Classes from 09:30 to 17:30 Module 5: Evaluating the proportional hazard assumption Module 6: Stratified Cox models

Thursday 24th – Classes from 09:30 to 17:30 Module 7: Time dependent variables Module 8: Frailty Models and Multistate models

Friday 25th – Classes from 09:30 to 17:30 Module 9: Competing risks models Module 10: Open session

Email oliverhooker@psstatistics.com

Check out our sister sites, www.PRstatistics.com (Ecology and Life Sciences) www.PRinformatics.com (Bioinformatics and data science) www.PSstatistics.com (Behaviour and cognition)

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