One Day Introdutory Training Workshop on Survival Analysis

Date:

08/06/2017

Organised by:

University of Edinburgh

Presenter:

Professor Gillian Raab

Level:

Intermediate (some prior knowledge)

Contact:

Angela Fallon,LSCS Administrator
+44(0)131 314 4599
angela.fallon@ed.ac.uk

Map:

View in Google Maps  (EH8 9XP)

Venue:

University of Edinburgh, Geography Building, 1 Drummond St, Edinburgh

Description:

This is a one-day workshop led by SLS Staff (Prof. Gillian Raab) on survival analysis for time to event data.  The course is suitable for those with experience of statistical analyses but new to this type of analysis. It would be of particular interest to those considering using the Scottish Longitudinal Study to analyse time to event data.

This workshop will introduce methods to display and model time to event data, including Kaplan-Meier plots and Cox proportional hazards regression. The survival analysis theory will be complimented with hands-on practical sessions using either SPSS or Stata (R if sufficient interest is indicated) on training datasets similar to SLS data. Presentations of real projects will demonstrate research potential.

The course is intended for postgraduate students, academics and health or social researchers interested in learning how to do survival analysis in a statistical package. The course assumes some skills in statistical analysis, in particular a good knowledge of multiple regression and logistic regression would be beneficial. Additionally, a familiarity with using either SPSS, Stata or R syntax/command files is essential.

Cost:

Standard Registration – £30; Fee Exemption – PhD students and Edinburgh University/ADRC staff/LONGPOP Early Stage Researchers

Website and registration:

Region:

Scotland

Keywords:

Data Collection, Quantitative Data Handling and Data Analysis, Mixed Methods Data Handling and Data Analysis, Longitudinal Studies

Related publications and presentations:

Data Collection
Quantitative Data Handling and Data Analysis
Mixed Methods Data Handling and Data Analysis

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