A combined efficient design for biomarker data subject to a limit of detection due to measuring instrument sensitivity
Enrique F. Schisterman, Albert Vexler, Aijun Ye, Neil J. Perkins

TL;DR
This paper evaluates hybrid pooling designs for biomarker measurement under detection limits, demonstrating that a simple one-pool design offers high efficiency and practical applicability in biomedical research.
Contribution
It introduces and assesses the efficiency of hybrid pooling designs, especially a simple one-pool approach, for estimating biomarker parameters with detection limits.
Findings
The simple one-pool design is highly efficient under realistic conditions.
Hybrid designs improve estimation accuracy in censored biomarker data.
Maximum likelihood estimation effectively estimates parameters in these designs.
Abstract
Pooling specimens, a well-accepted sampling strategy in biomedical research, can be applied to reduce the cost of studying biomarkers. Even if the cost of a single assay is not a major restriction in evaluating biomarkers, pooling can be a powerful design that increases the efficiency of estimation based on data that is censored due to an instrument's lower limit of detection (LLOD). However, there are situations when the pooling design strongly aggravates the detection limit problem. To combine the benefits of pooled assays and individual assays, hybrid designs that involve taking a sample of both pooled and individual specimens have been proposed. We examine the efficiency of these hybrid designs in estimating parameters of two systems subject to a LLOD: (1) normally distributed biomarker with normally distributed measurement error and pooling error; (2) Gamma distributed biomarker…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
