TY - JOUR
T1 - Statistical analysis of latency outcomes in behavioral experiments
AU - Jahn-Eimermacher, Antje
AU - Lasarzik, Irina
AU - Raber, Jacob
N1 - Funding Information:
The first author thanks the Department of Public Health and Preventive Medicine of the Oregon Health & Science University, USA, for hosting the author during her research fellowship. This work was supported by JA1821/2 DFG grant ( Deutsche Forschungsgemeinschaft ), IIRG-05-14021 ( Alzheimer’s Association ), NNJ05HE63G ( NASA ) grants, and MH77647 ( NIH ).
PY - 2011/8/1
Y1 - 2011/8/1
N2 - 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.
AB - 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.
KW - Barnes maze
KW - Latency
KW - Morris water maze
KW - Passive avoidance
KW - Statistical analysis
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U2 - 10.1016/j.bbr.2011.03.007
DO - 10.1016/j.bbr.2011.03.007
M3 - Article
C2 - 21397635
AN - SCOPUS:79954628683
SN - 0166-4328
VL - 221
SP - 271
EP - 275
JO - Behavioural Brain Research
JF - Behavioural Brain Research
IS - 1
ER -