Statistical analysis of latency outcomes in behavioral experiments

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.