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Reproducible translation of transcriptomics data has been hampered by theubiquitous presence of batch effects. Statistical methods for managing batcheffects were initially developed in the setting of sample group comparison andlater borrowed for other settings such as survival outcome prediction. The mostnotable such method is ComBat, which adjusts for batches by including it as acovariate alongside sample groups in a linear regression. In survivalprediction, however, ComBat is used without definable groups for survivaloutcome and is done sequentially with survival regression for a potentiallyconfounded outcome. To address these issues, we propose a new method, calledBatMan ("BATch MitigAtion via stratificatioN"). It adjusts batches as strata insurvival regression and utilize variable selection methods such as LASSO tohandle high dimensionality. We assess the performance of BatMan in comparisonwith ComBat, each used either alone or in conjunction with data normalization,in a re-sampling-based simulation study under various levels of predictivesignal strength and patterns of batch-outcome association. Our simulations showthat (1) BatMan outperforms ComBat in nearly all scenarios when there are batcheffects in the data, and (2) their performance can be worsened by the additionof data normalization. We further evaluate them using microRNA data for ovariancancer from the Cancer Genome Atlas, and find that BatMan outforms ComBat whilethe addition of data normalization worsens the prediction. Our study thus showsthe advantage of BatMan and raises caution about the naive use of datanormalization in the context of developing survival prediction models. TheBatMan method and the simulation tool for performance assessment areimplemented in R and publicly available athttps://github.com/LXQin/PRECISION.survival.

BatMan: Mitigating Batch Effects via Stratification for Survival Outcome Prediction