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Statistical modelling of falls count data with excess zeros
  1. Asaduzzaman Khan1,
  2. Shahid Ullah2,
  3. Jenny Nitz3
  1. 1School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Australia
  2. 2School of Human Movement and Sport Sciences, University of Ballarat, Ballarat, Australia
  3. 3Division of Physiotherapy, School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Australia
  1. Correspondence to Dr Asad Khan, School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Qld 4072, Australia; a.khan2{at}uq.edu.au

Abstract

Objective To examine the appropriateness of different statistical models in analysing falls count data.

Methods Six count models (Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), zero-inflated NB (ZINB), hurdle Poisson (HP) and hurdle NB (HNB)) were used to analyse falls count data. Empirical evaluation of the competing models was performed using model selection criteria and goodness-of-fit through simulation. Data used were from a prospective cohort study of women aged 40–80 years.

Results Of the 465 women analysed, 330 (71%) did not fall at all. The analyses identified strong evidence of overdispersion in the falls data. The NB-based regression models (HNB, ZINB, NB) were better performed than the Poisson-based regression models (Poisson, ZIP, HP). Vuong tests favoured the HNB model over the NB and ZINB models and the NB model over the ZINB model. Model accuracy measures and Monte Carlo simulation of goodness-of-fit confirmed the lack of fit of the Poisson-based regression models and demonstrated the best fit for the HNB model with comparable good fit for the ZINB and NB models.

Conclusions Falls count data consisting of a considerable number of zeros can be appropriately modelled by the NB-based regression models, with the HNB model offering the best fit. The evaluation procedure presented in this paper provides a defensible guideline to appropriately model falls or similar count data with excess zeros.

  • Falls count data
  • poisson
  • negative binomial
  • zero-inflated models
  • hurdle models
  • methods

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Footnotes

  • Funding This work was supported in part by the University Queensland Start-up Grant (grant number 2007001006).

  • Competing interests None

  • Provenance and peer review Not commissioned; externally peer reviewed.

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