with apologies for cross-posting
One Day Introductory Training Workshop on Survival Analysis
7th June 2018, 0915-1700, University of Edinburgh, Geography Building, Drummond St, EH8 9XP
This is a one-day workshop led by Longitudinal Studies Centre Scotland (LSCS) 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 (SLS) 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. Presentations of real projects will also be given to demonstrate research potential.
The course is intended for postgraduate students, academics and social or health 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.
Price: Standard Registration - £30; Fee Exemption - Q-Step or PhD students based at Edinburgh University/ADRC-S staff/LONGPOP ESR
Lunch provided for all registered participants.
Places limited to 20 - Early registration is recommended.
Registration and further information: https://sls.lscs.ac.uk/events
This course is being jointly run by the LSCS and the European H2020 project “Methodologies and Data mining techniques for the analysis of Big Data based on Longitudinal Population and Epidemiological Registers” (LONGPOP).
Marie Sklodowska-Curie Early-Stage Researcher Fellowship
Marie Curie Early Stage Researcher (Health Population & Demography)
Applications are invited for an Early Stage Researcher position funded by the Marie Sklodowska-Curie Innovative Training Network “LONGPOP (Methodologies and Data mining techniques for the analysis of Big Data based on Longitudinal Population and Epidemiological Registers)” within the Horizon 2020 Programme of the European Commission. LONGPOP is a consortium of universities, research institutions and companies located in Spain, Netherlands, Sweden, Italy, United Kingdom, Belgium and Switzerland, The successful applicant will join a network of 14 Early Stage Researchers who are already embedded in the consortium. This is a high-profile position that offers exceptional benefits ideally suited for top graduates.
This position is based in the Centre for Research on Environment Society & Health (CRESH) and Longitudinal Studies Centre Scotland (LSCS), School of GeoSciences at the University of Edinburgh. You will join a broad, dynamic research team with interests in population health, demography and human geography. You will be expected to work with other investigators of the network, both in Edinburgh and at the other LONGPOP network institutions.
The post is available as soon as possible and is fixed term until 31st January 2020.
Marie Sklodowska-Curie eligibility rules require Early-Stage Researchers to have no more than four years' postgraduate research experience and not yet have a PhD.
To satisfy Marie Sklodowska-Curie mobility criteria, you must not have resided or carried out your main activity (work, studies, etc.) in the UK for more than 12 months in the 3 years immediately prior to the start date (short stays such as holidays and/or compulsory national service are not taken into account).
The Marie Sklodowska-Curie programme places no restrictions on nationality: applicants can be of any nationality and currently resident in any country worldwide, provided they meet the eligibility requirements set out above.
Closing date: 22 May 2018
http://longpop-itn.eu/recruitment/ & https://www.vacancies.ed.ac.uk vacancy ref: 043527