Quantification of Difference in Nonselectivity Between In Vitro Diagnostic Medical Devices

Abstract

Correct measurement results from in vitro diagnostic (IVD) medical devices (MD) are crucial for optimal patient care. The performance of IVD-MDs is often assessed through method comparison studies. Such studies can be compromised by the influence of various factors. The effect of these factors must be examined in every method comparison study, for example, nonselectivity differences between compared IVD-MDs are examined. Historically, selectivity or nonselectivity has been defined as a qualitative term. However, a quantification of nonselectivity differences between IVD-MDs is needed. This paper fills this need by introducing a novel measure for quantifying differences in nonselectivity (DINS) between a pair of IVD-MDs. Assuming one of the IVD-MDs involved in the comparison exhibits high selectivity for the analyte, it becomes feasible to quantify nonselectivity in the other IVD-MD by employing this DINS measure. Our approach leverages elements from univariate ordinary least squares regression and incorporates repeatability IVD-MD variances, resulting in a normalized measure. We also introduce a plug-in estimator for this measure, which is notably linked to the average relative increase in prediction interval widths attributable to DINS. This connection is exploited to establish a criterion for identifying excessive DINS utilizing a proof-of-hazard approach. Utilizing Monte Carlo simulations, we investigate how the estimator relates to population characteristics like DINS and heteroskedasticity. We find that DINS impacts the mean, variance, and 99th percentile of the estimator, while heteroskedasticity affects only the latter two, and to a considerably smaller extent compared to DINS. Importantly, the size of the study design modulates these effects. We also confirm, when using clinical data, that DINS between pairs of IVD-MDs influence the estimator correspondingly to those of simulated data. Thus, the proposed estimator serves as an effective metric for quantifying DINS between IVD-MDs and helping to determine the quality of a method comparison study.