There is a continuous growth in data collected in clinical trials. Many of those patient’s characteristics
are potential confounding factors. Ideally, these factors should be accounted for in the randomization process to balance study arms and reduce the variability of the estimated treatment effect. However, the efficiency of the randomization decreases very fast with the number of factors, in particular
Our solution comes from the observation that
covariates individually. The problem is to balance patients while considering all covariate effects together. However, those multivariate interactions are difficult to model/estimate with
Then, even in small studies, a covariate adaptive randomization could be applied on this single
composite covariate to account for them all.
The composite covariate approach was first presented at the 2018 PSI conference in the context of adjusted analyses. Extending our results to covariate-adaptive randomization, we showed its particular interest with complex data (high-dimensional, non-linear, etc). Indeed, limiting the number of covariates to one has a direct positive impact on the efficiency of the randomization. We also put this
efficiency gain into perspective with the quality of the learning process.