Comparison of three statistical methods for analysis of fall predictors in people with dementia: Negative binomial regression (NBR), regression tree (RT), and partial least squares regression (PLSR)

Abstract

Searching for background factors associated with falls in people with dementia is difficult because the population is heterogeneous. The aim of this study was to compare the efficacies of three statistical methods for analysis of fall predictors in people with dementia. NBR, RT and PLSR analyses were compared. Data used for the comparison were from a prospective cohort study of 192 patients at a psychogeriatric ward, specializing in patients with cognitive impairment and related behavioral and psychological symptoms. Seventy-eight of these patients fell a total of 238 times. PLSR and RT analyses are directed at finding patterns among predictor variables related to outcome, whereas an NBR model is directed at finding predictor variables that, independent of other variables, are related to the outcome. The NBR analysis explained an additional 10–15% variation compared with the PLSR and RT analyses. The results of PLSR and RT show a similar plausible pattern of predictor variables. However, none of these techniques appears to be sufficient in itself. In order to gain patterns of explanatory variables, RT would be a good complement to NBR for analysis of fall predictors.