Mixed effects modeling of Morris water maze data revisited: Bayesian censored regression

Abstract

Young, Clark, Goffus, and Hoane (Learning and Motivation, 40(2), 160–177, 2009) documented significant advantages of linear and nonlinear mixed-effects modeling in the analysis of Morris water maze data. However, they also noted a caution regarding the impact of the common practice of ending a trial when the rat had not reached the platform by a preestablished deadline. The present study revisits their conclusions by considering a new approach that involves multilevel (i.e., mixed effects) censored generalized linear regression using Bayesian analysis. A censored regression explicitly models the censoring created by prematurely ending a trial, and the use of generalized linear regression incorporates the skewed distribution of latency data as well as the nonlinear relationships this can produce. This approach is contrasted with a standard multilevel linear and nonlinear regression using two case studies. The censored generalized linear regression better models the observed relationships, but the linear regression created mixed results and clearly resulted in model misspecification.