Stat4Reg Lab: STATistical research laboratory FOR the analysis of REGister data

Stat4Reg is a research laboratory at Umeå University, where scientists work at developing methods and software (freeware) for the analysis of very large micro-databases, including linkages of: health, socio-economics, demographic and other administrative registers (record linkage data), longitudinal studies (intervention programs, panel studies, ...), etc.

Latest news:
  • Conference on Machine Learning Stat4Reg is co-organising the 46th Winter Conference in Statistics with theme Machine Learning, Hemavan, March 10-15, 2019. Keynote lectures will be given by Corrina Cortes, Head of Google ...
    Posted Nov 28, 2018, 12:07 AM by Xavier de Luna
  • New PhD student positions Join our research group! We are now opening fully funded PhD positions. Apply before 28 December. For more details on the research projects and how to apply follow the link ...
    Posted Nov 23, 2018, 1:24 AM by Xavier de Luna
Showing posts 1 - 2 of 19. View more »

Partners - methods are developed in collaboration with empirical scientists and our main partners include:

Umeå SIMSAM Lab: A lab giving access to world-unique record linkage micro-data for the study of Children living conditions and life long health and welfare.

CEDAR: Center for Demographic and Ageing Research

Riksstroke: The national stroke register in Sweden

Swedish Childhood Diabetes Study Group

Umeå Center for Functional Brain Imaging

Register Center North (Registercentrum Norr)

MONICA: The Northern Sweden MONICA Study. Monitoring of trends and determinants in cardiovascular disease and diabetes. 

IFAU: The Institute for Evaluation of Labour Market and Education Policy

International collaborations

Center for Statistics, Ghent University

Research themes: we conduct research within the following themes (for more details see programs and projects):

Causal inference and machine learning: we develop models, methods and algorithms for causal discovery and inference based on very large databases.

Missing data and other selection mechanisms: combining registers, surveys and other longitudinal studies yields complex micro-data sets. Studies based on such data must carefully model sample selection and other missing data mechanisms. We develop such models and proposed methods to study deviations from the assumptions made (sensitivity analyses).

Health care performance: Open comparisons of hospitals and regions (benchmarking) provide information used in treatment priorities, patients’ choice of health care provider, and financial funding. The national quality registers allow for such comparisons and relevant statistical methods are developped to exploit this data and provide better support to quality improvement in health care.

Life course studies: Life course studies is an emerging field of study in the social and health sciences, where the purpose study individual life trajectories. Life trajectories with different social and health domains can nowadays been observed and studied thanks to the availability of rich and large source of micro-data.

Free software: user friendly R functions are developed, making accessible the methods developed to the wider scientific community.