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

Stat4Reg is a research laboratory at Umeå University, where scientists work at developing statistical 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:
  • New funding to Stat4Reg from the Swedish Research Council for 2017-2022 The Swedish Research Council has granted researchers at Stat4Reg with 12 million SEK for the project "Statistical methods to study causal effects with population based registers" (press release in Swedish ...
    Posted Mar 29, 2017, 6:48 AM by Xavier de Luna
  • STRATOS visiting Umeå Stat4Reg is hosting a meeting of the Causal Inference STRATOS group on January 19-21, 2017. The objective of STRATOS is to provide accessible and accurate guidance in the design ...
    Posted Dec 22, 2016, 7:36 AM by Xavier de Luna
Showing posts 1 - 2 of 12. 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 in observational studies: we develop models and methods to infer the effects of treatments, policies and health intervention based on non-experimental/observational large data sets.

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.


Comments