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  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
  • patent
  • Administrative Staff
  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Clinical Research Nurse
  • Clinician Researcher
  • Department Manager
  • Dual-education Student
  • Full Professor
  • Honorary Professor
  • Lab assistant
  • Master Student
  • Non-permanent Researcher
  • Nursing Staff
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Prize
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
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© Research
Publication : Revue d'epidemiologie et de sante publique

[A methodological review of specific techniques of time series analysis in epidemiology and public health]

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Revue d'epidemiologie et de sante publique - 01 Jan 1995

Watier L

Link to Pubmed [PMID] – 7732202

Rev Epidemiol Sante Publique 1995;43(2):162-72

Time series analysis of health indicator can contribute to the improvement of knowledge in epidemiology and public health. It is necessary to use models and methods appropriate to the time domain. A class of models proposed by Box & Jenkins are presented together with the necessary tools for identifying such models. Two useful generalisations are also discussed. To study the link between two time series, regression models need to include autocorrelated error terms while to evaluate the effect of an intervention, the use of intervention models are appropriate. This paper presents basic concepts of such analyses in a simple way to encourage the use of those methods.