A Bayesian approach to ordinal outcomes for neurosurgical clinical research.

Sally A. Wood, Melbourne Business School, University of Melbourne

Abstract

The objective of this study is to demonstrate a Bayesian approach for the statistical analysis of neurosurgical data where the investigators have used an ordinal scale for the outcome. A Bayesian approach that uses data augmentation and Gibbs sampling to perform ordinal probit regression is demonstrated in a neurosurgical context. The statistical approach is applied to a regression analysis to examine the relationship between female gender and the outcome of severe traumatic brain injury measured with the Glascow Outcome Scale. The approach is applied to a hierarchical meta-analysis to examine the relationship between age and the outcome from subarachnoid haemorrhage where the outcome is measured using ordinal scales. A model selection procedure is described for deriving the probability of statistical association between a regression covariates and the outcome variable. In the selection procedure the likelihood of the model is compared with alternative models that have random ordering of the covariate of interest. Results and Conclusions. The method had the flexibility and intuitive appeal of the Bayesian approach. The models provided a good fit to the data and useful predictive probabilities. The method has an underlying assumption that a normally distributed outcome variable has been categorized. The intuitive appeal of this assumption should make the method suitable to a wide range of neurosurgical studies including observational studies, controlled trials, and meta-analyses.