Modeling longitudinal biomarker data from multiple assays that have different known detection limits.

Assays to measure biomarkers are commonly subject to large amounts of measurement error and known detection limits. Studies with longitudinal biomarker measurements may use multiple assays in assessing outcome. I propose an approach for jointly modeling repeated measures of multiple assays when these assays are subject to measurement error and known lower detection limits. A commonly used approach is to perform an initial assay with a larger lower detection limit on all repeated samples, followed by only performing a second more expensive assay with a lower minimum level of detection when the initial assay value is below its lower limit of detection. I show how simply replacing the initial assay measurement with the second assay measurement may be a biased approach and investigate the performance of the proposed joint model in this situation. Additionally, I compare the performance of the joint model with an approach that only uses the initial assay measurements in analysis. Further, I consider alternative designs to only performing the second assay when the initial assay measurement is below its lower detection limit. Specifically, I show that one only needs to perform the second assay on a fraction of assays that are above the lower detection limit on the first assay to substantially increase the efficiency. Further, I show the efficiency advantages of performing the second assay at random without regard to the initial assay measurement over a design in which the second assay is only performed when the initial assay is below its lower limit of detection. The methodology is illustrated with a recent study examining the use of a vaccine in treating macaques with simian immunodeficiency virus.