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Estimating Concentration Response Function and Change-Point using Time-Course and Calibration Data

Author

Listed:
  • Qiang B

    (Department of Math and Stats, Southern Illinois University Edwardsville, USA)

  • Abdalla A
  • Morgan S
  • Hashemi P

    (Department of Chemistry and Biochemistry, USC, Columbia, SC 29208)

  • Peña E

    (Department of Statistics, USC, USA)

Abstract

In this paper the problem of determining the functional relationship between time and the concentration of a chemical substance is studied. An intervention drug is administered on the experimental unit from which the chemical substance (specimen) is measured. This drug is hypothesized to cause a change in the concentration level of the chemical substance a certain lag-time after the intervention. However, the concentration value could not be directly measured, but rather a surrogate response can be measured. In the time-course study, this surrogate response is measured using different electrodes which possess varied behaviors. To utilize these surrogate measurements arising from the different electrodes (sensors), a calibration study is undertaken which measures the surrogate response for the different electrodes at known concentration levels. Based on the time-course and calibration data sets, a statistical procedure to estimate the signal function and the lag-time is proposed. Simulation studies indicate that the proposed procedure is able to reasonably recover the signal function and the lag-time. The procedure is then applied to the real data sets obtained during an analytical chemistry experiment.

Suggested Citation

  • Qiang B & Abdalla A & Morgan S & Hashemi P & Peña E, 2019. "Estimating Concentration Response Function and Change-Point using Time-Course and Calibration Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(3), pages 57-68, March.
    • Handle: RePEc:adp:jbboaj:v:9:y:2019:i:3:p:57-68
    DOI: 10.19080/BBOAJ.2019.09.555762
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    References listed on IDEAS

    as
    1. Pena, Edsel A. & Slate, Elizabeth H., 2006. "Global Validation of Linear Model Assumptions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 341-354, March.
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