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Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data
Clark R Andersen1,2, Jordan Wolf1,3, Kristofer Jennings2
1The Moody Project for Translational Traumatic Brain Injury Research, University of Texas Medical Branch, Galveston, Texas, USA.
Journal of Neurotrauma
|August 25, 2020
Summary
Accelerated failure time (AFT) models are superior for analyzing Morris water maze (MWM) data in traumatic brain injury (TBI) studies. This method accurately detects cognitive deficits, unlike logistic regression or ANOVA, by accounting for censored data.
Area of Science:
- Neuroscience
- Biostatistics
- Behavioral Science
Background:
- Traumatic brain injury (TBI) causes significant cognitive impairments.
- Accurate assessment of learning and memory deficits is crucial for TBI research.
- The Morris water maze (MWM) is a standard tool for evaluating cognitive function after TBI.
Purpose of the Study:
- To compare the efficacy of three statistical methods for analyzing MWM data.
- To identify the optimal analytical approach for detecting TBI-induced cognitive deficits in MWM studies.
Main Methods:
- Analysis of hidden platform spatial MWM data from three experiments.
- Comparison of logistic regression, ANOVA, and accelerated failure time (AFT) models.
- Evaluation of statistical significance and accounting for data censoring.
Main Results:
- Logistic regression showed no significant differences between sham and TBI groups.
- ANOVA identified differences but may be biased by ignoring censoring.
- AFT models revealed significant differences between sham and TBI groups, accounting for censoring.
Conclusions:
- Accelerated failure time (AFT) models are recommended for analyzing MWM latency to platform data.
- AFT models provide a more statistically robust method for TBI research.
- This finding enhances the reliability of cognitive deficit assessment in TBI studies.
