  Reproducible translation of transcriptomics data has been hampered by the
ubiquitous presence of batch effects. Statistical methods for managing batch
effects were initially developed in the setting of sample group comparison and
later borrowed for other settings such as survival outcome prediction. The most
notable such method is ComBat, which adjusts for batches by including it as a
covariate alongside sample groups in a linear regression. In survival
prediction, however, ComBat is used without definable groups for survival
outcome and is done sequentially with survival regression for a potentially
confounded outcome. To address these issues, we propose a new method, called
BatMan ("BATch MitigAtion via stratificatioN"). It adjusts batches as strata in
survival regression and utilize variable selection methods such as LASSO to
handle high dimensionality. We assess the performance of BatMan in comparison
with ComBat, each used either alone or in conjunction with data normalization,
in a re-sampling-based simulation study under various levels of predictive
signal strength and patterns of batch-outcome association. Our simulations show
that (1) BatMan outperforms ComBat in nearly all scenarios when there are batch
effects in the data, and (2) their performance can be worsened by the addition
of data normalization. We further evaluate them using microRNA data for ovarian
cancer from the Cancer Genome Atlas, and find that BatMan outforms ComBat while
the addition of data normalization worsens the prediction. Our study thus shows
the advantage of BatMan and raises caution about the naive use of data
normalization in the context of developing survival prediction models. The
BatMan method and the simulation tool for performance assessment are
implemented in R and publicly available at
https://github.com/LXQin/PRECISION.survival.
