Bayesian dynamic modelling to assess differential treatment effects on panic attack frequencies

Tanja Krone*, C. J. Albers, M. E. Timmerman

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
306 Downloads (Pure)

Abstract

To represent the complex structure of intensive longitudinal data of multiple individuals, we propose a hierarchical Bayesian Dynamic Model (BDM). This BDM is a generalized linear hierarchical model where the individual parameters do not necessarily follow a normal distribution. The model parameters can be estimated on the basis of relatively small sample sizes and in the presence of missing time points. We present the BDM and discuss the model identification, convergence and selection. The use of the BDM is illustrated using data from a randomized clinical trial to study the differential effects of three treatments for panic disorder. The data involves the number of panic attacks experienced weekly (73 individuals, 10–52 time points) during treatment. Presuming that the counts are Poisson distributed, the BDM considered involves a linear trend model with an exponential link function. The final model included a moving average parameter and an external variable (duration of symptoms pre-treatment). Our results show that cognitive behavioural therapy is less effective in reducing panic attacks than serotonin selective re-uptake inhibitors or a combination of both. Post hoc analyses revealed that males show a slightly higher number of panic attacks at the onset of treatment than females.
Original languageEnglish
Pages (from-to)343-359
Number of pages17
JournalStatistical Modelling
Volume16
Issue number5
Early online date13-Jul-2016
DOIs
Publication statusPublished - Oct-2016

Keywords

  • time series analysis
  • longitudinal data
  • count data
  • multilevel models
  • missing data
  • AR(1) MODEL
  • DISORDER
  • THERAPY

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