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Change‐point models to estimate the limit of detection
Author(s) -
May Ryan C.,
Chu Haitao,
Ibrahim Joseph G.,
Hudgens Michael G.,
Lees Abigail C.,
Margolis David M.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5872
Subject(s) - limit (mathematics) , detection limit , change detection , blank , computer science , statistics , step detection , analyte , mathematics , data mining , artificial intelligence , chromatography , materials science , mathematical analysis , chemistry , filter (signal processing) , computer vision , composite material
In many biological and environmental studies, measured data is subject to a limit of detection. The limit of detection is generally defined as the lowest concentration of analyte that can be differentiated from a blank sample with some certainty. Data falling below the limit of detection is left censored, falling below a level that is easily quantified by a measuring device. A great deal of interest lies in estimating the limit of detection for a particular measurement device. In this paper, we propose a change‐point model to estimate the limit of detection by using data from an experiment with known analyte concentrations. Estimation of the limit of detection proceeds by a two‐stage maximum likelihood method. Extensions are considered that allow for censored measurements and data from multiple experiments. A simulation study is conducted demonstrating that in some settings the change‐point model provides less biased estimates of the limit of detection than conventional methods. The proposed method is then applied to data from an HIV pilot study. Copyright © 2013 John Wiley & Sons, Ltd.