PrettYharmonize¶

Cross-site class imbalance¶

A common challenge in multi-site datasets occurs when class distributions differ across sites. For example:

• Site A contains mostly control subjects
• Site B contains mostly patients

In such cases, biological signal and site effects become confounded.

If harmonization removes site effects without accounting for the biological variable, the biological signal may also be partially removed.

To address this, ComBat allows users to include biological variables as covariates to preserve during harmonization.

However, this introduces an issue in machine-learning pipelines.

If the covariate being preserved is also the target variable of the ML model, then the target must be known during harmonization. In real-world prediction scenarios this information is unavailable, creating a new form of data leakage \({^6}\).

Even if harmonization parameters are estimated using only the training set, the transformation of test data still requires the target variable.

A suitable alternative in these scenarios is PrettYharmonize, which allows ComBat-based harmonization to be integrated into ML pipelines without requiring the target variable during inference.