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

B Qiang1, A Abdalla2, S Morgan2

  • 1Department of Math and Stats, Southern Illinois University Edwardsville, USA.

Biostatistics and Biometrics Open Access Journal
|June 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical method to estimate chemical concentration changes and lag times, even when only surrogate measurements are available. The approach effectively recovers the signal function and lag-time in simulations and real-world analytical chemistry experiments.

Keywords:
Calibration studyChange-pointChemical concentrationGeneral linear modelTime-course experiment

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Area of Science:

  • Analytical Chemistry
  • Biostatistics
  • Pharmacokinetics

Background:

  • Determining the relationship between drug intervention and chemical concentration over time is crucial.
  • Direct measurement of chemical concentration is often not feasible, requiring the use of surrogate responses.
  • Surrogate responses can vary depending on the measurement method, such as different electrodes (sensors).

Purpose of the Study:

  • To develop a statistical procedure for estimating the functional relationship between time and chemical concentration.
  • To estimate the lag-time between drug administration and the change in chemical concentration.
  • To utilize calibration data to correct for variations in surrogate measurements from different sensors.

Main Methods:

  • A statistical procedure was developed to analyze time-course data and calibration data.
  • The method estimates the signal function representing the chemical concentration over time.
  • It also estimates the lag-time, which is the delay between intervention and effect.

Main Results:

  • Simulation studies demonstrated the procedure's ability to accurately recover the signal function and lag-time.
  • The proposed method was successfully applied to real data from an analytical chemistry experiment.
  • The statistical procedure effectively integrates information from multiple sensors with varied behaviors.

Conclusions:

  • A robust statistical method is proposed for estimating chemical concentration dynamics and intervention lag-times using surrogate measurements.
  • The procedure addresses challenges posed by indirect measurements and sensor variability.
  • This approach provides a valuable tool for analytical chemistry and pharmacokinetic studies.