The use of an autoregressive model for the analysis of longitudinal data in epidemiologic studies

Bernard Rosner, Alvaro Muñoz, Ira Tager, Frank Speizer, Scott Weiss

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


Korn and Whittemore1,2 have presented methods for analyzing longitudinal data where the number of observations per individual is large relative to the number of variables considered for each subject. However, this is often not the case in epidemiologic studies, since one usually collects data at relatively few time points, and the quantity of data collected for each individual at each time point is typically extensive. We present here an autoregressive model for analyzing longitudinal data of this type for the case of a continuous outcome variable. Some of the important features of this model are that one can (1) in the same analysis, consider both independent variables that are time‐dependent and those that are fixed over time, (2) partially use data for an individual where some examinations are missing, (3) assess relationships between changes in outcome and exposure over short periods of time, (4) use ordinary multiple regression methods. Anderson3 has considered this type of model, but, to our knowledge, the model has never been applied to biostatistical problems. We illustrate these methods with data from a longitudinal study that seeks to identify the role of personal cigarette smoking on changes in pulmonary function in children.

Original languageEnglish (US)
Pages (from-to)457-467
Number of pages11
JournalStatistics in Medicine
Issue number4
StatePublished - 1985
Externally publishedYes


  • Autoregressive time series
  • Longitudinal data
  • Pulmonary function data
  • Regression methods

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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