IRINI: random group allocation of multiple prognostic factors

Abstract

Statistically sound experimental design in pharmacology studies ensures that the known prognostic factors, if any, are equally represented across investigational groups to avoid bias and imbalance which could render the experiment invalid or lead to false conclusions. Complete randomization can be effective to reduce bias in the created groups especially in large sample size situations. However, in small studies which involve only few treatment subjects, as in preclinical trials, there is a high chance of imbalance. The effects of this imbalance may be removed through covariate analysis or prevented with stratified randomization, however small studies limit the number of covariates to be analyzed this way. The problem is accentuated when there are multiple baseline covariates with varying scales and magnitudes to be considered in the randomization, and creating a balanced solution becomes a combinatorial challenge. Our method, IRINI, uses an optimization technique to achieve treatment to subject group allocation across multiple prognostic factors concurrently. It ensures that the created groups are equal in size and statistically comparable in terms of mean and variance. This method is a novel application of genetic algorithms to solve the allocation problem and simultaneously ensure quality, speed of the results and randomness of the process. Results from preclinical trials demonstrate the effectiveness of the method.