Whole-cage randomization for animal studies with unequal cage or group sizes

Area of Science:

• Animal research methodology
• Biostatistics
• Laboratory animal science

Background:

• Individual animal randomization is standard practice to minimize bias in in vivo studies.
• Co-housing male mice for welfare necessitates whole-cage randomization, posing statistical challenges.
• Existing algorithms struggle with unequal group sizes or animal numbers per cage.

Purpose of the Study:

• To develop a novel algorithm for whole-cage randomization.
• To balance baseline variables across treatment groups in animal studies with whole-cage allocation.
• To address limitations of current methods for complex housing arrangements.

Main Methods:

• A new, fast, and reliable algorithm was designed for whole-cage randomization.
• The algorithm balances one or more baseline variables across treatment groups.
• The method was tested using a realistic example dataset.

Main Results:

• The proposed algorithm successfully performs whole-cage randomization.
• It effectively balances baseline variables even with unequal group sizes or animal numbers.
• Demonstrated applicability in a practical research scenario.

Conclusions:

• The novel algorithm provides an effective solution for whole-cage randomization in animal research.
• It enhances statistical rigor and ethical animal welfare in studies with specific housing requirements.
• This method addresses a critical gap in current biostatistical tools for in vivo studies.