An Algorithm for Randomizing Matched Sets Within and Between Batches

Music City Center 

Variation in laboratory assays can contribute to measurement error. Careful planning can minimize differential errors in effect measures. Randomization can help to ensure that sequencing of samples within and across batches is independent of sample characteristics. Batches may be comprised of multiple plates. We developed an algorithm to assign samples to batches that: 1) allows for variation in plate sizes within batches; 2) treats samples for matched study subjects such as cases and controls or exposed and unexposed individuals as a set; 3) randomizes sets to assigned batches; and, 4) orders samples randomly with sets. To evaluate variation within and between batches, quality control samples are: 1) assigned both within and to other batches; and, 2) quality control replicate samples in the same batch are required to be placed a certain distance apart. An option in the tool allows for minimal rearrangement of samples if a sequence of assays requiring different batch sizes are being conducted. A validation step verifies that algorithm arguments are being met. A R-Package with this tool is being developed, including a vignette and a test dataset.

randomization

case-control

laboratory assays

bias

Disclaimer:
The authors have no conflicts of interest to disclose. The views, information or content, and conclusions presented do not necessarily represent the

official endorsement be inferred on the part of, the Uniformed Services University, the Department of Defense, the U.S. Government or The Henry M. Jackson Foundation. 

ENAR