<p>Estimating treatment effects in randomized controlled trials (RCTs) is often costly and time-consuming because large sample sizes are needed to ensure adequate statistical power. Covariate adjustment using statistical models, particularly analysis of covariance (ANCOVA), can improve efficiency and reduce required enrollment. We examined the use of super-covariates (SCs), defined as participant-specific predicted outcomes under control treatment derived from a prognostic model, as adjustment variables in ANCOVA. An important feature of this approach is an ensemble of multiple predictive learners (typically machine learning methods) trained on historical control data. Although SCs from well-trained prognostic models improve the efficiency of ANCOVA estimators, their performance remains insufficiently characterized in practice, especially when (i) baseline covariate distributions differ between the historical data and the new RCT and (ii) the new RCT has a relatively small sample size. Using statistical simulations, we evaluated ANCOVA estimators incorporating a simplified SC under these scenarios and provided practical guidance. SC-based adjustment improves efficiency when abundant historical data are available. To optimize prognostic modeling, investigators should leverage all historical data rather than limiting the training set to subsets matched to the new RCT population. For treatment effect estimation, an ANCOVA adjusting for both an SC and baseline covariates can be more efficient than adjusting for either alone. However, simulations reveal small-sample bias under extreme covariate–treatment interactions. Caution is advised against preemptive sample size reductions even under high correlations between the SC and historical control outcomes, as such correlations are not necessarily reproduced in a new RCT.</p>