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Autoregressive Models for Longitudinal Data (120 Mean Monthly Population Records)

  • Ton J. Cleophas
  • Aeilko H. Zwinderman
Chapter
  • 78 Downloads

Abstract

Time series are encountered in every field of medicine. Traditional tests are unable to assess trends, seasonality, change points and the effects of multiple predictors, like treatment modalities, simultaneously. This chapter is to assess, whether autoregressive integrated moving average (ARIMA) methods are able to do all of that.

Supplementary material

333106_2_En_35_MOESM1_ESM.sav (4 kb)
arimafile (SAV 4 kb)
333106_2_En_35_MOESM2_ESM.sav (7 kb)
arimafile2 (SAV 6 kb)
333106_2_En_35_MOESM3_ESM.zip (2 kb)
exportarima (XML 4 kb)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ton J. Cleophas
    • 1
  • Aeilko H. Zwinderman
    • 2
  1. 1.Department Medicine Albert Schweitzer HospitalDordrechtThe Netherlands
  2. 2.Academic Medical CenterDepartment Biostatistics and EpidemiologyAmsterdamThe Netherlands

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