Statistical methods for analysing longitudinal data in delirium studies

Abstract

Delirium is an often acute and highly fluctuating syndrome that can be transient or in some cases associated with prolonged disturbances. The best way to capture its natural course is to conduct studies with longitudinal design, but data analysis in longitudinal studies is difficult, as often the measured variables of each subject are correlated over the course of time. As such, there has been limited application of such methods for analysing longitudinal data in the study of delirium. This overview considers simple traditional approaches along with more complex methods that involve modelling of data. The relative merits of survival analysis, structural equation modelling, and path analysis are reviewed. Furthermore, two flexible modelling techniques are considered; the mixed effects model and generalized estimating equations with emphasis on their use with binary outcomes, as often the outcome in delirium studies is delirium/no delirium. Their contrasting approach to parameter interpretation and methods for accounting for correlation and dealing with missing data are detailed. Information on available software is provided. Delirium research will be substantially enhanced by incorporating such methods.