Cost-Efficient Designs Based on Linearly Associated Biomarkers

• Activity Number: 379
• Type: Topic Contributed
• Date/Time: Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Changxing Ma*+ and Albert Vexler and Lili Tian and Enrique Schisterman
• Companies: State University of New York at Buffalo and New York State University at Buffalo and State University of New York at Buffalo and National Institutes of Health
• Keywords: biological samples ; cost-efficient sampling ; D-optimality ; linear regression ; pooling design ; random sampling

Abstract

A major limiting factor in much of epidemiological research is the cost of measuring the biomarkers or analytes of interest. Often the number of specimens available for analysis is greater than the number of assays that was budgeted for. In this article, we examine two types of pooling in lieu of a random sample. The first is random pooling and the second we characterize as optimal pooling. The second, which we propose for regression analysis, is pooling based on specimens ranked on the less expensive biomarker. The more expensive assay is then performed on the pool of relatively similar measurements. The optimal nature of this technique is also exemplified via simulation and real biomarker data. By displaying the considerable robustness of our method via a Monte Carlo study, it is shown that proposed pooling design is a viable option whenever expensive assays are considered.