The analysis of peak expiratory flow data using a three-level hierarchical model

Paul C Lambert, Paul R Burton, Keith R Abrams, Adrian M Brooke

Research output: Contribution to journalArticle (Academic Journal)

11 Citations (Scopus)

Abstract

Peak expiratory flow (PEF) is a measure commonly used in clinical practice and research for respiratory diseases such as asthma. In research, PEF is usually recorded in a diary for a 2-week period with two or more measurements per day. Interest may lie in whether certain groups of individuals tend to have higher or lower PEF. In addition the variability of PEF may be of interest as, for example, asthmatics tend to have more variable airways. In this paper we develop a three-level hierarchical model that can simultaneously model the mean level and variability of PEF. The variability is broken down into three components, between-subject variability, between-day within-subject variability, and within-day within-subject variability. The latter two components are of specific clinical interest. We fit both classical and Bayesian models. The Bayesian models have the advantage of taking the uncertainty in the variance component estimates into account when estimating the standard errors of the fixed effects. In addition, the Bayesian models provide an intuitive and simple way to investigate the within-subject variance components.

Original languageEnglish
Pages (from-to)3821-3839
Number of pages19
JournalStatistics in Medicine
Volume23
Issue number24
DOIs
Publication statusPublished - 30 Dec 2004

Bibliographical note

2004 John Wiley & Sons, Ltd.

Keywords

  • Asthma
  • Bayes Theorem
  • Child
  • Child, Preschool
  • Circadian Rhythm
  • Computer Simulation
  • Female
  • Humans
  • Male
  • Models, Statistical
  • Peak Expiratory Flow Rate

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