Statistical Approaches to The Univariate Prognostic Analysis of The IMPACT Database on Traumatic Brain Injury

Abstract

The univariate prognostic analysis of the IMPACT database on traumatic brain injury (TBI) poses the formidable challenge of how best to summarize a highly complex set of data in a way which is accessible without being overly simplistic. In this paper, we describe and illustrate the battery of statistical methods that have been used. Boxplots, histograms, tabulations, and splines were used for initial data checking and in identifying appropriate transformations for more formal statistical modeling. Imputation techniques were used to minimize the problems associated with the analysis of incomplete data due to missing values. The associations between covariates and outcome (Glasgow Outcome Scale [GOS] assessed at 6 months) were expressed as odds ratios with supporting confidence intervals when the GOS was collapsed to a dichotomous scale. This was extended to use common odds ratios from proportional odds models to express associations over the full range of the GOS. Forest plots were used to illustrate the consistency of results from study to study within the IMPACT database. The overall prognostic strength of the prognostic factors was expressed as the proportion of variance explained (Nagelkerke's R(2) statistic). Many of our approaches are based on simple graphical displays of the data, but, where appropriate, we have also used methods that although established in the statistical literature are relatively novel in their application to TBI.