Stronger pre-clinical statistics by optimal animal matching

What is it about?

To improve the reproducibility of pre-clinical animal studies, we implemented a mathematical optimization framework that matches animals based on their baseline variables, and showed how the matching information improved not only the animal allocations to balanced intervention groups, but also enabled to boost power to detect true intervention effects with smaller number of animals. To promote its widespread use, we have made the matching tool freely available, both as an open-source and extendable R-package (https://cran.r-project.org/web/packages/hamlet/), and via a web-based user interface (http://rvivo.tcdm.fi/). We invite the preclinical research community to try out this new tool and to give us feedback and further suggestions.

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Why is it important?

Due to the ever-increasing costs of the current drug development process, there has been an increasing demand for improving the validity, reproducibility and translatability of the pre-clinical animal studies, in particular, their experimental design and statistical analysis (Nature 520, 271-2; 2015; doi: 10.1038/520271a). We believe that one important reason why researchers are currently not implementing the best practices is the lack of statistical software implementations that would be specifically tailored for the preclinical researchers for designing adequately-powered intervention studies.

Perspectives