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The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response...
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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Dose response signal detection under model uncertainty.

Holger Dette1, Stefanie Titoff2, Stanislav Volgushev1

  • 1Ruhr-Universität Bochum, Fakultät für Mathematik, 44780, Bochum, Germany.

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Summary
This summary is machine-generated.

This study introduces a new method for detecting dose response signals using likelihood ratio tests, even with uncertain or unknown regression models. The approach handles complex nonlinear models, improving statistical power in biological and pharmaceutical research.

Keywords:
Contrast testsDose response studiesLikelihood ratio testModel identificationNonlinear regression

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Area of Science:

  • Statistics
  • Biostatistics
  • Pharmacometrics

Background:

  • Model uncertainty is common in dose-response analysis.
  • Standard likelihood ratio tests face challenges with nonlinear models and non-identifiable parameters under the null hypothesis.
  • Accurate dose-response signal detection is crucial for drug development and biological research.

Purpose of the Study:

  • To develop and validate a robust likelihood ratio contrast test for dose-response signal detection under model uncertainty.
  • To address the challenges of parameter non-identifiability in nonlinear regression models.
  • To provide a statistically sound method for identifying dose-response relationships even when the underlying model is not fully known.

Main Methods:

  • Investigated likelihood ratio contrast tests for dose-response signal detection.
  • Derived asymptotic distributions for tests in regression models with non-identifiable parameters.
  • Simulated critical values using Gaussian processes.
  • Illustrated the method with a real-world data example.

Main Results:

  • The proposed method effectively handles model uncertainty in dose-response relationships.
  • Successfully derived and applied asymptotic distributions for non-identifiable parameter cases.
  • Demonstrated improved performance compared to existing procedures through simulations and theoretical analysis.

Conclusions:

  • The new likelihood ratio contrast test offers a powerful and flexible approach for dose-response signal detection.
  • This method enhances statistical rigor in situations with competing or uncertain regression models.
  • The approach is applicable to both linear and nonlinear regression models, particularly in pharmaceutical and toxicological studies.