arXiv subjects

Tomomichi Suzuki

Publications and source records attributed to Tomomichi Suzuki.

4 recordsLinked to original sources

arXiv2026

Statistical evaluation of measurement precision in linear dose-response relationships via interlaboratory studies

This paper proposes a framework for evaluating the statistical precision of measurement methods from interlaboratory studies where the outcome is a dose-response relationship summarized by a regression line. For such measurement methods, where a linear mixed-effects model is applied that allows laboratories to differ in both baseline level and dose-response slope, we define precision evaluation metrics specified in ISO 5725, repeatability and between-laboratory variances. These are method-level precision metrics, and the latter are constructed as design-averaged dose-specific between-laboratory variances over the dose levels and the participating laboratories. For fully balanced designs with common dose levels and equal replication, we obtain an exact decomposition of the total sum of squares, closed-form analysis of variance (ANOVA) estimators of the precision variances, and three associated $F$-tests targeting (i) the overall dose-response trend, (ii) homogeneity of intercepts, and (iii) homogeneity of slopes across laboratories. This formulation enables precision to be quantified and estimated directly and supports an evaluation of whether between-laboratory discrepancies are caused primarily by baseline shifts or by differences in sensitivity, in contrast to fixed-effect comparisons that only detect the presence of differences. Furthermore, we analyze data obtained from an interlaboratory study on observations in bronchoalveolar lavage fluid from experiments involving the intratracheal administration of nanomaterials to rats, using the proposed method as a case study.

stat.AP↗

arXiv2026

Bootstrap-based estimation and inference for measurement precision under ISO 5725

The ISO 5725 series frames interlaboratory precision through repeatability, between-laboratory, and reproducibility variances, yet practical guidance on deploying bootstrap methods within this one-way random-effects setting remains limited. We study resampling strategies tailored to ISO 5725 data and extend a bias-correction idea to obtain simple adjusted point estimators and confidence intervals for the variance components. Using extensive simulations that mirror realistic study sizes and variance ratios, we evaluate accuracy, stability, and coverage, and we contrast the resampling-based procedures with ANOVA-based estimators and common approximate intervals. The results yield a clear division of labor: adjusted within-laboratory resampling provides accurate and stable point estimation in small-to-moderate designs, whereas a two-stage strategy-resampling laboratories and then resampling within each-paired with bias-corrected and accelerated intervals offers the most reliable (near-nominal or conservative) confidence intervals. Performance degrades under extreme designs, such as very small samples or dominant between-laboratory variation, clarifying when additional caution is warranted. A case study from an ISO 5725-4 dataset illustrates how the recommended procedures behave in practice and how they compare with ANOVA and approximate methods. We conclude with concrete guidance for implementing resampling-based precision analysis in interlaboratory studies: use adjusted within-laboratory resampling for point estimation, and adopt the two-stage strategy with bias-corrected and accelerated intervals for interval estimation.

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arXiv2024

A model and method for analyzing the precision of binary measurement methods based on beta-binomial distributions, and related statistical tests

This study developed a new statistical model and method for analyzing the precision of binary measurement methods from collaborative studies. The model is based on beta-binomial distributions. In other words, it assumes that the sensitivity of each laboratory obeys a beta distribution, and the binary measured values under a given sensitivity follow a binomial distribution. We propose the key precision measures of repeatability and reproducibility for the model, and provide their unbiased estimates. Further, through consideration of a number of statistical test methods for homogeneity of proportions, we propose appropriate methods for determining laboratory effects in the new model. Finally, we apply the results to real-world examples in the fields of food safety and chemical risk assessment and management.

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arXiv2022

A remark on statistics for detecting laboratory effects in ORDANOVA

The present study defines a new statistic for detecting laboratory effects in the analysis of ordinal variation (ORDANOVA). The ORDANOVA is an analysis method similar to one-way analysis of variance for analysing ordinal data obtained from interlaboratory comparison studies. In this paper, we present an approximate continuous distribution for the new statistic for the case of an arbitrary number of ordinal levels, and we demonstrate that $alpha$-percentiles of the distribution are suitable criteria for conducting statistical tests. In addition, a real example involving data from an interlaboratory comparison study is analysed using the proposed statistic.

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