Statistical methods in randomised controlled trials for delirium.

Area of Science:

• Gerontology
• Clinical Statistics
• Neuroscience

Background:

• Delirium clinical trials face challenges with patient dropouts and variable prognoses.
• Standard statistical approaches may not adequately address these complexities in delirium research.

Purpose of the Study:

• To describe statistical considerations for planning and analyzing clinical trials in delirium.
• To recommend appropriate statistical models for handling common issues in delirium research.

Main Methods:

• Discusses choices for outcomes, patient samples, and data collection in delirium trials.
• Explains time-to-event, imputation-based, linear, and nonlinear models for randomized controlled trials.
• Evaluates the suitability of various imputation methods for delirium data.

Main Results:

• Simple imputation methods may be inadequate for delirium research.
• Time-to-event and nonlinear models offer a more comprehensive analytical perspective than end-of-trial assessments.
• Nonlinear random effects models for longitudinal trajectories are particularly well-suited for delirium studies.

Conclusions:

• The described statistical methods, especially nonlinear models, can enhance the rigor of future delirium research analyses.
• Adoption of these advanced statistical techniques is encouraged for more convincing results in delirium clinical trials.