Statistical analysis of latency outcomes in behavioral experiments

Abstract

In experimental designs of animal models, memory is often assessed by the time for a performance measure to occur (latency). Depending on the cognitive test, this may be the time it takes an animal to escape to a hidden platform (water maze), an escape tunnel (Barnes maze) or to enter a dark component (passive avoidance test). Latency outcomes are usually statistically analyzed using ANOVAs. Besides strong distributional assumptions, ANOVA cannot properly deal with animals not showing the performance measure within the trial time, potentially causing biased and misleading results. We propose an alternative approach for statistical analyses of latency outcomes. These analyses have less distributional assumptions and adequately handle results of trials in which the performance measure did not occur within the trial time. The proposed method is well known from survival analyses, provides comprehensible statistical results and allows the generation of meaningful graphs. Experiments of behavioral neuroscience and anesthesiology are used to illustrate this method.

Introduction

In analyzing cognitive tests, time for a performance measure to occur (latency) is often used. In some cases, such as the Barnes maze and water maze, this refers to the time it takes the animal to successfully find the escape tunnel or hidden platform, respectively. In other cognitive tests, such as the passive avoidance test, latency refers to the time the animal takes to enter or re-enter the dark component. As an aversive stimulus is received in the dark component during training, a lower latency during the test is associated with reduced cognitive performance or memory. In all these kind of tests, there is a possibility that the animal does not show this behavior within the trial time. Thus, an animal might not locate the escape tunnel in the Barnes maze, locate the escape platform in the water maze, or enter the dark component of the passive avoidance test, within the maximum trial time. Typically, the maximum trial time is in such cases used to calculate latency data. However, there is obviously a large difference between animals that do and those that do not show the pertinent performance measure within the trial time. In this study, we will refer to these events as failures. If failures are treated as though the animals reached the performance measure in the maximum trial time, results will potentially be biased and conclusions potentially be misleading.

Having maximum trial time data in a particular experiment also questions that the data are normally distributed with equal variances per groups. This in turn affects assumptions for standard ANOVA tests typically used to analyze these kind of latency data. It has been well established that violations of these assumptions can have a severe impact on both type I error and power [1]. Still ANOVA F-tests are broadly applied, in most situations without checking for any deviation of the distributional assumptions [2]. We will demonstrate, why extreme caution is warranted in underlying a normal distribution with equal variances in latency data. As nonparametric methods follow naturally, we will show, that these may be preferable compared to ANOVA, but still overcome only some of the challenges arising in analyzing latency data. We will propose an appropriate and easy to apply alternative statistical method to analyze and graphically present latency data, which will provide well comprehensible results. The method is typically applied in survival data analysis and thus standard statistical software packages can be employed in most situations. All methods are illustrated using two examples of behavioural neuroscience.

The paper is organized as follows: In Section 2 the current practice to analyze latency outcomes and its shortcomings is described and an alternative approach overcoming these shortcomings is proposed in Section 2.2. This approach is illustrated on data examples including sample size considerations in Section 3. The paper closes with a discussion in Section 4.