Abstract

Reducing the number of animal subjects used in biomedical experiments is desirable for both ethical and practical reasons. Previous suggestions for reducing sample sizes in these experiments have focused on improving experimental designs and methods of statistical analysis; reducing the number of controls (thus, the number of overall animals used) is rarely mentioned. We discuss how the number of current control animals can be reduced, without loss of statistical power, by incorporating information from historical controls, i.e. animals used as controls in similar previous experiments. Using example data from the literature, we describe how to incorporate information from historical controls under a range of assumptions, implemented either as familiar t-tests, contrasts, or in a mixed models context. Assuming more similarities between historical and current controls yields higher savings and allows the use of smaller current control groups. We conducted simulations, based on typical designs and sample sizes, to quantify how different assumptions about historical controls affect the power of statistical tests. Under our simulation conditions, the number of current control subjects can be reduced by more than half by including historical controls in the analyses. Paying attention to both the function and to the statistical requirements of control groups would result in reducing the total number of animals used in experiments, saving time, effort and money, and bringing research using animals within ethically acceptable bounds.

Keywords

animal testing, animal welfare, borrowing information, control group, reduction, sample size, statistical power, three Rs

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

 
May 1st, 9:00 AM

STRATEGIES FOR REDUCING CONTROL GROUP SIZE IN EXPERIMENTS USING LIVE ANIMALS

Reducing the number of animal subjects used in biomedical experiments is desirable for both ethical and practical reasons. Previous suggestions for reducing sample sizes in these experiments have focused on improving experimental designs and methods of statistical analysis; reducing the number of controls (thus, the number of overall animals used) is rarely mentioned. We discuss how the number of current control animals can be reduced, without loss of statistical power, by incorporating information from historical controls, i.e. animals used as controls in similar previous experiments. Using example data from the literature, we describe how to incorporate information from historical controls under a range of assumptions, implemented either as familiar t-tests, contrasts, or in a mixed models context. Assuming more similarities between historical and current controls yields higher savings and allows the use of smaller current control groups. We conducted simulations, based on typical designs and sample sizes, to quantify how different assumptions about historical controls affect the power of statistical tests. Under our simulation conditions, the number of current control subjects can be reduced by more than half by including historical controls in the analyses. Paying attention to both the function and to the statistical requirements of control groups would result in reducing the total number of animals used in experiments, saving time, effort and money, and bringing research using animals within ethically acceptable bounds.