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

Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland 20892, USA. albertp@mail.nih.gov

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.

PMID: 17764483 [PubMed - indexed for MEDLINE]