One-Sided and Two-Sided Tolerance Intervals in General Mixed and Random Effects Models Using Small-Sample Asymptotics

The calculation of historical control limits in toxicology: Do's, don'ts and open issues from a statistical perspective

For reporting toxicology studies, the presentation of historical control data and the validation of the concurrent control group with respect to historical control limits have become requirements. However, many regulatory guidelines fail to define how such limits should be calculated and what kind of target value(s) they should cover. Hence, this manuscript is aimed to give a brief review on the methods for the calculation of historical control limits that are in use as well as on their theoretical background. Furthermore, this manuscript is aimed to identify open issues for the use of historical control limits that need to be discussed by the community. It seems that, even after 40 years of discussion, more issues remain open than solved, both, with regard to the available methodology as well as its implementation in user-friendly software. Since several of these topics equally apply to several research fields, this manuscript is addressed to all relevant stakeholders who deal with historical control data obtained from toxicological studies, regardless of their background or field of research.

Nonconcomitant host-to-host transmission of multipartite virus genome segments may lead to complete genome reconstitution

Because multipartite viruses package their genome segments in different viral particles, they face a potentially huge cost if the entire genomic information, i.e., all genome segments, needs to be present concomitantly for the infection to function. Previous work with the octapartite faba bean necrotic stunt virus (FBNSV; family Nanoviridae, genus Nanovirus) showed that this issue can be resolved at the within-host level through a supracellular functioning; all viral segments do not need to be present within the same host cell but may complement each other through intercellular trafficking of their products (protein or messenger RNA [mRNA]). Here, we report on whether FBNSV can as well decrease the genomic integrity cost during between-host transmission. Using viable infections lacking nonessential virus segments, we show that full-genome infections can be reconstituted and function through separate acquisition and/or inoculation of complementary sets of genome segments in recipient hosts. This separate acquisition/inoculation can occur either through the transmission of different segment sets by different individual aphid vectors or by the sequential acquisition by the same aphid of complementary sets of segments from different hosts. The possibility of a separate between-host transmission of different genome segments thus offers a way to at least partially resolve the genomic maintenance problem faced by multipartite viruses.

Application of intraoral scanner to identify monozygotic twins

BACKGROUND

DNA base identification is a proper and high specificity method. However, identification could be challenged in a situation where there is no database or the DNA sequence is almost identical, as in the case of monozygotic (MZ) twins. The aim of this study was to introduce a novel forensic method for distinguishing between almost identical MZ twins by means of an intraoral scanner using the 3D digital pattern of the human palate.

METHODS

The palatal area of 64 MZ twins and 33 same-sex dizygotic (DZ) twins (DZSS) and seven opposite-sex dizygotic twins (DZOS) were scanned three times with an intraoral scanner. From the scanned data, an STL file was created and exported into the GOM Inspect® inspection software. All scans within a twin pair were superimposed on each other. The average deviation between scans of the same subject (intra-subject deviation, ISD) and between scans of the two siblings within a twin pair (intra-twin deviation, ITD) was measured. One-sided tolerance interval covering 99% of the population with 99% confidence was calculated for the ISD (upper limit) and the ITD (lower limit).

RESULTS

The mean ISD of the palatal scan was 35.3 μm ± 0.78 μm. The calculated upper tolerance limit was 95 μm. The mean ITD of MZ twins (406 μm ± 15 μm) was significantly (p < 0.001) higher than the ISD, and it was significantly lower than the ITD of DZSS twins (594 μm ± 53 μm, p < 0.01) and the ITD of DZOS twins (853 μm ± 202 μm, p < 0.05).

CONCLUSION

The reproducibility of palatal intraoral scans proved to be excellent. The morphology of the palate shows differences between members of MZ twins despite their almost identical DNA, indicating that this method could be useful in forensic odontology.

Confidence, prediction, and tolerance in linear mixed models

The literature about Prediction Interval (PI) and Tolerance Interval (TI) in linear mixed models is usually developed for specific designs, which is a main limitation to their use. This paper proposes to reformulate the two‐sided PI to be generalizable under a wide variety of designs (one random factor, nested and crossed designs for multiple random factors, and balanced or unbalanced designs). This new methodology is based on the Hessian matrix, namely, the inverse of (observed) Fisher Information matrix, and is built with a cell mean model. The degrees of freedom for the total variance are calculated with the generalized Satterthwaite method and compared to the Kenward‐Roger's degrees of freedom for fixed effects. Construction of two‐sided TIs are also detailed with one random factor, and two nested and two crossed random variables. An extensive simulation study is carried out to compare the widths and coverage probabilities of Confidence Intervals (CI), PIs, and TIs to their nominal levels. It shows excellent coverage whatever the design and the sample size are. Finally, these CIs, PIs, and TIs are applied to two real data sets: one from orthopedic surgery study (intralesional resection risk) and the other from assay validation study during vaccine development.

Testing against "normal" with environmental data

Normal ranges are some fraction of a reference distribution deemed to represent an expected condition, typically 95%. They are frequently used as the basis for generic criteria for monitoring programs designed to test whether a sample is outside of "normal," as in reference-condition approach studies. Normal ranges are also the basis for criteria for more classic environmental effects monitoring programs designed to detect differences in mean responses between reference and exposure areas. Limits on normal ranges are estimated with error that varies depending largely on sample size. Direct comparison of a sample or a mean to estimated limits of a normal range will, with some frequency, lead to incorrect conclusions about whether a sample or a mean is inside or outside the normal range when the sample or the mean is near the limit. Those errors can have significant costs and risk implications. This article describes tests based on noncentral distributions that are appropriate for quantifying the likelihood that samples or means are outside a normal range. These noncentral tests reverse the burden of evidence (assuming that the sample or mean is at or outside normal), and thereby encourage proponents to collect more robust sample sizes that will demonstrate that the sample or mean is not at the limits or beyond the normal range. These noncentral equivalence and interval tests can be applied to uni- and multivariate responses, and to simple (e.g., upstream vs downstream) or more complex (e.g., before vs after, or upstream vs downstream) study designs. Statistical procedures for the various tests are illustrated with benthic invertebrate community data collected as part of the Regional Aquatics Monitoring Program (RAMP) in the vicinity of oil sands operations in northern Alberta, Canada. An Excel workbook with functions and calculations to carry out the various tests is provided in the online Supplemental Data. Integr Environ Assess Manag 2017;13:188-197. © 2016 SETAC.

Tolerance bands for functional data

Often the object of inference in biomedical applications is a range that brackets a given fraction of individual observations in a population. A classical estimate of this range for univariate measurements is a "tolerance interval." This article develops its natural extension for functional measurements, a "tolerance band," and proposes a methodology for constructing its pointwise and simultaneous versions that incorporates both sparse and dense functional data. Assuming that the measurements are observed with noise, the methodology uses functional principal component analysis in a mixed model framework to represent the measurements and employs bootstrapping to approximate the tolerance factors needed for the bands. The proposed bands also account for uncertainty in the principal components decomposition. Simulations show that the methodology has, generally, acceptable performance unless the data are quite sparse and unbalanced, in which case the bands may be somewhat liberal. The methodology is illustrated using two real datasets, a sparse dataset involving CD4 cell counts and a dense dataset involving core body temperatures.

Application of tolerance limits to the characterization of image registration performance

Deformable image registration is used increasingly in image-guided interventions and other applications. However, validation and characterization of registration performance remain areas that require further study. We propose an analysis methodology for deriving tolerance limits on the initial conditions for deformable registration that reliably lead to a successful registration. This approach results in a concise summary of the probability of registration failure, while accounting for the variability in the test data. The (β, γ) tolerance limit can be interpreted as a value of the input parameter that leads to successful registration outcome in at least 100β% of cases with the 100γ% confidence. The utility of the methodology is illustrated by summarizing the performance of a deformable registration algorithm evaluated in three different experimental setups of increasing complexity. Our examples are based on clinical data collected during MRI-guided prostate biopsy registered using publicly available deformable registration tool. The results indicate that the proposed methodology can be used to generate concise graphical summaries of the experiments, as well as a probabilistic estimate of the registration outcome for a future sample. Its use may facilitate improved objective assessment, comparison and retrospective stress-testing of deformable.

L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals

The L-statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L-statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article has three main goals: (a) Show that the L-statistics are asymptotically normal for repeated measurements data. (b) Present three statistical applications of this result, namely, location estimation using trimmed means, quantile estimation and construction of tolerance intervals. (c) Obtain a Bahadur representation for sample quantiles. These results are generalizations of similar results for independently and identically distributed data. The practical usefulness of these results is illustrated by analyzing a real data set involving measurement of systolic blood pressure. The properties of the proposed point and interval estimators are examined via simulation.