  Batch effects are pervasive in biomedical studies. One approach to address
the batch effects is repeatedly measuring a subset of samples in each batch.
These remeasured samples are used to estimate and correct the batch effects.
However, rigorous statistical methods for batch effect correction with
remeasured samples are severely under-developed. In this study, we developed a
framework for batch effect correction using remeasured samples in highly
confounded case-control studies. We provided theoretical analyses of the
proposed procedure, evaluated its power characteristics, and provided a power
calculation tool to aid in the study design. We found that the number of
samples that need to be remeasured depends strongly on the between-batch
correlation. When the correlation is high, remeasuring a small subset of
samples is possible to rescue most of the power.
