Statistical modelling for falls count data

Abstract

Falls and their injury outcomes have count distributions that are highly skewed toward the right with clumping at zero, posing analytical challenges. Different modelling approaches have been used in the published literature to describe falls count distributions, often without consideration of the underlying statistical and modelling assumptions. This paper compares the use of modified Poisson and negative binomial (NB) models as alternatives to Poisson (P) regression, for the analysis of fall outcome counts. Four different count-based regression models (P, NB, zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB)) were each individually fitted to four separate fall count datasets from Australia, New Zealand and United States. The finite mixtures of P and NB regression models were also compared to the standard NB model. Both analytical (F, Vuong and bootstrap tests) and graphical approaches were used to select and compare models. Simulation studies assessed the size and power of each model fit. This study confirms that falls count distributions are over-dispersed, but not dispersed due to excess zero counts or heterogeneous population. Accordingly, the P model generally provided the poorest fit to all datasets. The fit improved significantly with NB and both zero-inflated models. The fit was also improved with the NB model, compared to finite mixtures of both P and NB regression models. Although there was little difference in fit between NB and ZINB models, in the interests of parsimony it is recommended that future studies involving modelling of falls count data routinely use the NB models in preference to the P or ZINB or finite mixture distribution. The fact that these conclusions apply across four separate datasets from four different samples of older people participating in studies of different methodology, adds strength to this general guiding principle.

Statistical modelling for recurrent events: an application to sports injuries

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Discrete distributions when modeling the disability severity score of motor victims

“…As there may be important differences between injured victims who did not suffer any disability (a zero score) and those left with permanent disabilities, zero-altered distributions, such as the zero-inflated and the hurdle models, are also considered. These zero-altered distributions assume that disability severity data are generated by a dual-state process (Lord et al, 2005;Ullah et al, 2009). Regression models are compared from two perspectives, namely the interpretation of the underlying data generating process and the level of statistical fit.…”

Motorcyclists’ Mortality Pattern in Colombia from 2000 to 2013: A Longitudinal Study

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