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J Mol Med (Berl). 2019 Apr 13. doi: 10.1007/s00109-019-01774-0. [Epub ahead of print]

Benefits of a factorial design focusing on inclusion of female and male animals in one experiment.

Author information

1
Institute of Laboratory Animal Science, University of Zurich, Wagistrasse 12, 8952 Schlieren, Zurich, Switzerland. thorsten.buch@uzh.ch.
2
Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Bachemer Str. 86, 50931, Cologne, Germany.
3
Center for Data and Simulation Science (CDS), University of Cologne, Cologne, Germany.
4
Institute of Laboratory Animal Science, University of Zurich, Wagistrasse 12, 8952 Schlieren, Zurich, Switzerland.
5
Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany.
6
Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland.
7
Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Bachemer Str. 86, 50931, Cologne, Germany. achim.tresch@uni-koeln.de.
8
Center for Data and Simulation Science (CDS), University of Cologne, Cologne, Germany. achim.tresch@uni-koeln.de.

Abstract

Disease occurrence, clinical manifestations, and outcomes differ between men and women. Yet, women and men are most of the time treated similarly, which is often based on experimental data over-representing one sex. Accounting for persisting sex bias in biomedical research is the misconception that the analysis of sex-specific effects would double sample size and costs. We designed an analysis to test the potential benefits of a factorial study design in the context of a study including male and female animals. We chose a 2 × 2 factorial design approach to study the effect of treatment, sex, and an interaction term of treatment and sex in a hypothetical situation. We calculated the sample sizes required to detect an effect of a given magnitude with sufficient power and under different experimental setups. We demonstrated that the inclusion of both sexes in experimental setups, without testing for sex effects, requires no or few additional animals in our scenarios. These experimental designs still allow for the exploration of sex effects at low cost. In a confirmatory instead of an exploratory design, we observed an increase in total sample sizes by 33%, at most. Since the complexities associated with this mathematical model require statistical expertise, we generated and provide a sample size calculator for planning factorial design experiments. For the inclusion of sex, a factorial design is advisable, and a sex-specific analysis can be performed without excessive additional effort. Our easy-to-use calculation tool provides help in designing studies with both sexes and addresses the current sex bias in preclinical studies. KEY MESSAGES: • Both sexes should be included into animal studies. • Exploratory study of sex effects can be conducted with no or small increase in animal number. • Confirmatory analysis of sex effects requires maximum 33% more animals per study. • Our calculation tool supports the design of studies with both sexes.

KEYWORDS:

Animal experimentation; Factorial design; Power; Sex

PMID:
30980104
DOI:
10.1007/s00109-019-01774-0

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