Increasing information gain in animal research by improving statistical model accuracy
Waterkamp, C.; von Kortzfleisch, V. T.; Neu, C.; Kitanovski, S.; Richter, S. H.; Hoffmann, D.
Show abstract
Reduction of the numbers of laboratory animals is one of the three pillars of ethical animal research. Equivalently, information gain per animal should be maximized. A road towards this goal that is barely taken in current animal research is the more accurate statistical modeling of experiments. Here we show for a typical experiment ("open field test") with outcomes that are non-normally distributed count data, how this can be implemented and what information gain is achieved. We contrast the state of the art - the use of confidence intervals based on null-hypothesis significance testing (NHST) -, with a Bayesian approach with the same underlying normal model, and a Bayesian approach with a more accurate negative binomial model. We find that the more accurate model leads to a marked improvement of knowledge gained with the experiment, especially for small sample sizes. As experimental data that violate assumptions of simple, conventional models are frequent, our findings have wider implications.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The unbiased estimation of the fraction of variance explained by a model 95%
- Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data 94%
- A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort. 94%
Similar papers in this journal
- Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance 93%
- Estimating multiplicity of infection, allele frequencies, and prevalences accounting for incomplete data 93%
- Theoretical properties of nearest-neighbor distance distributions and novel metrics for high dimensional bioinformatics data 93%
Similar papers in this journal
- Information encoded in volumes and areas of dendritic spines is nearly maximal across mammalian brains 93%
- Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions 93%
- Mathematical modelling of SARS-CoV-2 variant outbreaks reveals their probability of extinction 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.