Multistate Models: Life Course Analysis from Event Histories and Panel Data
Start: 22 May 2017
End: 26 May 2017
Location: Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
Jutta Gampe, MPIDR
Hein Putter, Leiden University Medical Center
Ardo van den Hout, University College London
The life course of individuals can be conceived as a sequence of transitions between different states, for example
from being healthy to being ill, possibly recovering, and finally to death or
from living in parental home to living alone, cohabiting with a partner, with or without children, to perhaps living in an institution until death.
The aim of life course analysis is to understand the timing and sequence of transitions as well as the risk factors that accelerate or slow down transitions. Multistate models are the statistical framework to analyze life course patterns and to study and predict resulting population dynamics.
In this five-day course the participants will be introduced to the concepts of multistate models and will learn how to estimate the essential quantities in the two most frequently encountered data situations: Event-histories, for which the exact times of transitions are known, and panel data, where observations are only made in (more or less) regular intervals, leading to interval-censored data.
The course will start with a brief recap of standard survival analysis on which many of the concepts in multistate modeling are based. Moving beyond two-state models the core concepts will be introduced. Besides the estimation of the key parameters, the transition intensities, derived quantities, such as expected lengths of stay in particular states, will be discussed. Selecting and validating well-fitting models, assessing uncertainty of estimates and illustrative presentation of results will also be covered.
The course will be a mix of lectures and computer practicals using the statistical software R.
Target audience and prerequisites
The course addresses demographers and researchers from related disciplines such as other social sciences or epidemiology. Participants should be enrolled in a PhD program or have received their PhD.
Participants should have a good working knowledge of standard survival analysis and be familiar with the software R. Students are expected to bring their own laptops with the most recent version of R and an appropriate editor (e.g. Rstudio) installed.
Students’ performance will be evaluated on the basis of an assignment which will be handed out towards the end of the course.
A reading list will be distributed to the participants. Slides and R-code used in the lectures will be made accessible, too.
There is no tuition fee for this course. Students are expected to pay their own transportation and living costs. However, a limited number of scholarships are available on a competitive basis for outstanding candidates and for those applicants who might otherwise not be able to come.
Recruitment of students
A maximum of 15 students will be admitted.
The selection will be made by the MPIDR based on the applicants’ scientific qualifications.
How to apply
Applications should be sent by email to the MPIDR (address below). Please begin your email message with a statement saying that you apply for course IDEM 104 – Multistate Models. You also need to attach the following items integrated in *a single pdf file*: (1) A two-page curriculum vitae, including a list of your scholarly publications. (2) A one-page letter from your supervisor at your home institution supporting your application. (3) A two-page statement of your research and how it relates to the course. Please include a short description of your knowledge of survival analysis and your fluency in R. At the very end of your research statement, in a separate paragraph, please indicate (a) whether you would like to be considered for financial support and (b) if you would be able to come without financial aid from our side.
Send your email to Heiner Maier (firstname.lastname@example.org).
Application deadline is 31 March 2017.
Applicants will be informed of their acceptance by 20 April 2017.
Applications submitted after the deadline will be considered only if space is available.