This work was published before the author joined Aga Khan University.

Tayyeb Tahir, Aga Khan University
Jonathan Bisson, University Hospital of Wales, Cardiff, UK

This work was published before the author joined Aga Khan University.


Objective: The analysis of clinical trials in delirium is typically complicated by treatment dropouts and by the fact that even untreated individuals may have a good prognosis. These features of delirium trials warrant special statistical attention; implications for each stage of a trial planning process are described.

Methods: Choice of outcome, patient sample, and data collection in delirium trials are discussed. Descriptions are given, together with examples, of time-to-event, imputation-based, linear and nonlinear models for the analysis of randomised controlled trials for delirium.

Results: Imputation allows evaluation of the plausibility of individual recovery trajectories, but some simple imputations are found to be unsuitable for delirium research. Time-to-event and nonlinear models encourage a global perspective on analysis, which is often preferable to cross-sectional end-of-trial assessments. It is suggested that nonlinear random effects models for longitudinal trajectories are particularly suitable in a delirium context.

Conclusion: It is hoped that the methods described, and nonlinear models in particular, will play a part in convincing analyses of future delirium research.