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Using Smoothing Splines to Quantify Differences in Nonselectivity between In Vitro Diagnostic Medical Devices in
Pernille Kjeilen Fauskanger1,2, Sverre Sandberg1,3,4, Jesper V Johansen5
1Norwegian Organization for Quality Improvement of Laboratory Examinations (Noklus), Haraldsplass Deaconess Hospital, Bergen, Norway.
Clinical Chemistry
|June 2, 2026
Summary
A new smoothing spline model accurately estimates differences in nonselectivity (DINS) for in vitro diagnostic medical device (IVD-MD) comparison studies, outperforming traditional methods under nonlinear relationships. This enhances the reliability of IVD-MD assessments.
Area of Science:
- Clinical Chemistry and Diagnostics
- Biostatistics and Bioinformatics
- Medical Device Evaluation
Background:
- In vitro diagnostic medical device (IVD-MD) comparison studies are crucial for ensuring accuracy and reliability.
- Existing evaluation methods often assume linear relationships between IVD-MDs and may inaccurately estimate differences in nonselectivity (DINS).
- The ordinary least-squares (OLS) method for DINS estimation can be distorted by nonlinear relationships, leading to overestimation.
Purpose of the Study:
- To develop and evaluate a novel smoothing spline-based model for estimating DINS in IVD-MD comparison studies.
- To compare the performance of the smoothing spline DINS estimator against the traditional OLS approach.
- To assess the utility of the smoothing spline framework for external quality assessment material (EQAM) commutability assessment, particularly under nonlinear conditions.
Main Methods:
- Development of a DINS estimation model utilizing smoothing splines.
- Evaluation of both OLS and smoothing spline DINS models using clinical sample data and Monte Carlo simulations.
- Comparison of smoothing spline prediction intervals (PI) against Deming regression PIs for EQAM commutability assessment using simulations modeling linear and nonlinear IVD-MD relationships.
Main Results:
- The OLS model overestimated DINS under nonlinear relationships between IVD-MDs, whereas the smoothing spline estimator did not.
- The smoothing spline PI demonstrated empirical coverage probability close to the nominal 95% confidence level under both linear and nonlinear relationships.
- Deming regression PIs performed poorly under nonlinear relationships, while the smoothing spline PI maintained accuracy.
Conclusions:
- The smoothing spline framework offers an accurate method for quantifying DINS and assessing EQAM commutability, especially when nonlinear relationships are present.
- The smoothing spline DINS estimator is valuable for method comparison studies and can extend commutability assessment to IVD-MD pairs previously unsuitable for linear models.
- This approach improves the reliability and applicability of IVD-MD comparison studies in clinical diagnostics.
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