Optimal Number of Replicates to Ensure Reproducibility in Pre-Clinical Studies

Music City Center 

Reproducibility in pre-clinical research has gained attention, especially concerning the application of statistical methods. This awareness underscores the need for increased rigor and reproducibility, particularly in experimental replications. Factors such as the experimenter, animal batch, and environmental conditions can influence experimental outcomes, causing findings from a single experiment to be potentially non-reproducible under different conditions. Typically, two independent experiments are conducted with a possible third if initial results conflict. Despite frequent replication, a formal framework for assessing and optimizing replication numbers is lacking. This project aims to quantify reproducibility and establish necessary replication numbers. We simulated a 2x3 factorial design experiment (2 groups, 3 conditions) and replicated it 2 to 8 times. Data were analyzed using a linear mixed-effects model (LMEM), allowing group effects to vary across experiments. The LMEM quantified between-replication variation, serving as a measure of reproducibility. Our simulations showed that reproducibility reaches nearly 100% at 4 replications regardless of group effect size.

Quantify reproducibility in pre-clinical research

optimal number of replications

incorporate all replications in data analysis

covariance parameter estimates

Linear Mixed Effects Model

Random Effects 

Biometrics Section