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On model selection for standard curve in assay development

S K Tse1, S C Chow

  • 1Department of Applied Statistics and Operational Research, City University of Hong Kong, Hong Kong.

Journal of Biopharmaceutical Statistics
|November 1, 1995
PubMed
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Selecting the right statistical model for standard curves is crucial for assay validation. This study proposes a method using R2 and mean squared error to identify the best model for accurate assay results.

Area of Science:

  • Biostatistics
  • Assay Development
  • Analytical Chemistry

Background:

  • Accurate standard curves are essential for reliable assay validation.
  • The choice of statistical model significantly impacts assay performance.
  • Several statistical models are commonly used for standard curve fitting.

Purpose of the Study:

  • To propose a systematic procedure for selecting the optimal statistical model for standard curves in assay development.
  • To provide guidance on choosing the best model for assay validation.
  • To compare the performance of different statistical models in a real-world assay scenario.

Main Methods:

  • A selection procedure based on R-squared (R2) and mean squared error (MSE) was developed.
  • The procedure evaluates and compares five commonly used statistical models.

Related Experiment Videos

  • An assay validation study was used as a case example.

    • Main Results:

    • The proposed procedure effectively discriminates among different statistical models.
    • The R2 and MSE metrics provide a quantitative basis for model selection.
    • The example demonstrates the practical application of the procedure in assay validation.

    Conclusions:

    • The proposed R2 and MSE-based selection procedure enhances the reliability of standard curve modeling.
    • This method aids in selecting the most appropriate statistical model for assay development and validation.
    • Implementing this procedure improves the accuracy and trustworthiness of assay results.