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Introduction/Objectives: The fecal immunochemical test (FIT) helps triage primary care patients at risk of colorectal cancer (CRC). Improving FIT returns has received recent attention, however uncertainty exists regarding the accurate completion of samples provided for laboratory analysis. This study aims to identify the rejection rate of returned FIT samples and determine rejection causes. Methods: FIT samples from symptomatic patients within South Yorkshire, Bassetlaw, and North Derbyshire are processed at a central laboratory. Tests requests are made from 225 GP practices, which serve an estimated 2 million population. This study describes a retrospective review of FIT samples received in the central laboratory between 01/09/19 and 31/12/22. Locally held data was interrogated in March 2023 to determine the number of FIT samples received and rejected during the study period. Documented reasons for rejection were explored to identify common themes. Results: Total FIT specimens received during the study period was 126 422. Of these, 5190 (4.1%) were rejected. Monthly rejection rates fell from 17.4% in September 2019 to 1.3% in December 2022 (P <.001). Sampling errors were the most frequent cause for FIT rejection (2151/5190), with other causes including: expired specimen; no sample collection date/ time, no request form, incomplete patient information and illegible handwriting. Conclusions: This is the first study exploring FIT rejection rates in symptomatic primary care patients, which shows improvements in rejection rates over time. Targeted interventions could improve rejection rates further, thereby reducing NHS resource use and costs and diagnostic delays. [ABSTRACT FROM AUTHOR]

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context. Author summary: The genome-wide association study (GWAS) is a method used to identify genetic variants which contribute to the risk of developing disease. These genetic variants are frequently shared between conditions, such that the study of the genetic basis of one disease can be informed by knowledge of another, similar disease. This approach can be productive where the disease in question is rare such that a GWAS has less power to associate variants with the disease, but there exist larger GWAS of similar diseases. Existing methods do not measure genetic similarity precisely when patients are few. Here we assess a previously published method of testing for genetic similarity between pairs of diseases using GWAS data, the 'GPS' test, against three other methods with the use of real and simulated data. We present a new computational procedure for carrying out the test and show that the GPS test is superior to its comparators in identifying genetic similarity when the sample size is small and when the genetic similarity signal is less strong. Use of the test will enable accurate detection of genetic similarity and the study of rarer conditions using data from better-characterised diseases. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to evaluate whether the acceptability of different types of business- and individual-level non-compliance has different impacts on the likelihood of participation in undeclared work. Design/methodology/approach: To evaluate this, data is reported on the EU27 and the UK from the special Eurobarometer survey no. 498, using a novel statistical methodology that deals with two potential sources of bias: sample selection error (avoidance to answer to the question about participation to undeclared work) and misclassification in the response variable (false statements about engagement in undeclared work). Findings: This reveals the association between tax morale and participation in undeclared work. It shows that citizens find far more unacceptable undeclared work conducted by firms than individuals, but both are significantly associated with participation in undeclared work although the greatest effect is clearly exerted by individual-level tax morale. Originality/value: This paper uses a methodology that accounts for the potential bias related to sample selection error and misclassification in the response variable of participation in undeclared work and sheds light on different components of tax morale. [ABSTRACT FROM AUTHOR]

The UK standard for estimating flood frequencies is outlined by the flood estimation handbook (FEH) and associated updates. Estimates inevitably come with uncertainty due to sampling error as well as model and measurement error. Using resampling approaches adapted to the FEH methods, this paper quantifies the sampling uncertainty for single site, pooled (ungauged), enhanced single site (gauged pooling) and across catchment types. This study builds upon previous progress regarding easily applicable quantifications of FEH-based uncertainty estimation. Where these previous studies have provided simple analytical expressions for quantifying uncertainty for single site and ungauged design flow estimates, this study provides an easy-to-use method for quantifying uncertainty for enhanced single site estimates. [ABSTRACT FROM AUTHOR]

Psychiatric conditions exhibit a higher-order transdiagnostic structure that helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structure of psychopathology may differ, raising questions about the validity and utility of these factors. To investigate the factor structure of psychopathology, we used structural equation modeling techniques to characterize patterns of (dis)similarity among ten psychiatric conditions at phenotypic and genetic levels of analysis using UK Biobank and public genomic data. We used a variety of multivariate statistical techniques to evaluate the validity and utility of domain-specific (e.g., internalizing) and general psychopathology factors, including genome-wide analyses of individual variants, genetic correlations, and polygenic indices. We found that the structure of thought disorder and internalizing psychopathology was largely consistent across genetic and phenotypic levels of analysis, whereas externalizing and compulsive disorders were differentially related to other conditions at phenotypic and genetic levels. Through a series of analyses, we rule out that these differences could be due to sampling error. Despite some structural differences across levels of analysis, the thought disorder, internalizing, externalizing, and compulsive problems factors improved statistical power: These factors exhibited relatively low rates of heterogeneity in genetic analyses, providing novel evidence for the validity and utility of domain-specific transdiagnostic factors in biological psychopathology. Finally, we report that the general psychopathology factor (i.e., a 'p' factor) had relatively low validity and utility in genetic analyses. While p factor models can be fit to the data, they offer few insights into biology due to heterogeneous genetic effects across mental conditions. Moreover, the p factor polygenic indices obfuscated domain-specific associations: For instance, the positive correlation between cortical mean thicknesses and the internalizing polygenic index and the negative correlation between cortical mean thicknesses and the thought disorder polygenic index was obscured when examining associations between cortical mean thicknesses and p factor polygenic index. These results suggest that environmental influences may cause phenotypic correlations to deviate from genetic ones and that domain-specific factors, specifically thought disorder and internalizing, are more informative than the p factors to study comorbidity across psychiatric conditions at the level of individual variants and polygenic indices. This study enhances our understanding of comorbidity and shared etiology in psychopathology, highlights the intricate interplay between genes and environment, and offers guidance for psychiatric research using polygenic indices. [ABSTRACT FROM AUTHOR]

In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely. [ABSTRACT FROM AUTHOR]

The North Star Ambulatory Assessment (NSAA), which consists of 17 functional items, is designed for the assessment of ambulatory patients with Duchenne muscular dystrophy (DMD) and regularly conducted per guidelines and recorded in medical records throughout the United Kingdom, Europe and elsewhere. At too many centers and practices, however, recording of standardized ambulatory assessments in DMD medical records is limited or inconsistent, which hinders retrospective learning from real-word data in this rare, progressive and life-limiting disease. In this study, we evaluated whether consistently recording a concise subset of ambulatory assessments, conducted as part of a complete assessment per care guidelines, could accurately quantify real-world disease progression for DMD research. Drawing on data from over 1,800 complete NSAA assessments among 320 patients in the North Star Clinical Network database, we applied machine learning (lasso) to identify item subsets that best predicted total score. Predictions were evaluated in a held-out sample with the standard error of measurement (SEM) of the NSAA total score (previously estimated at 2.7 units) serving as a benchmark for accuracy. A subset of six NSAA items (hop, jump, rise, climb, walk, stand on one leg) predicted total score in validation data with correlation 0.97 and prediction error 1.8 units, less than the SEM. Prediction errors remained consistently below the SEM when stratified by age groups and by NSAA total score. These six items accord with key markers of progression in recent DMD disease models. The full NSAA should be conducted by trained assessors, consistent with care guidelines, so that disease management is not divorced from measurement. When entry of all NSAA items into the medical record is not possible, consistently recording or prioritizing entry of the six items identified here would significantly broaden the foundation for real-world outcomes research in ambulatory patients with DMD. [ABSTRACT FROM AUTHOR]

A typical way to quantify aboveground carbon in forests is to measure tree diameters and use species-specific allometric equations to estimate biomass and carbon stocks. Using "citizen scientists" to collect data that are usually time-consuming and labor-intensive can play a valuable role in ecological research. However, data validation, such as establishing the sampling error in volunteer measurements, is a crucial, but little studied, part of utilizing citizen science data. The aims of this study were to (1) evaluate the quality of tree diameter and height measurements carried out by volunteers compared to expert scientists and (2) estimate how sensitive carbon stock estimates are to these measurement sampling errors. Using all diameter data measured with a diameter tape, the volunteer mean sampling error (difference between repeated measurements of the same stem) was 9.9 mm, and the expert sampling error was 1.8 mm. Excluding those sampling errors >1 cm, the mean sampling errors were 2.3 mm (volunteers) and 1.4 mm (experts) (this excluded 14% [volunteer] and 3% [expert] of the data). The sampling error in diameter measurements had a small effect on the biomass estimates of the plots: a volunteer (expert) diameter sampling error of 2.3 mm (1.4 mm) translated into 1.7% (0.9%) change in the biomass estimates calculated from species-specific allometric equations based upon diameter. Height sampling error had a dependent relationship with tree height. Including height measurements in biomass calculations compounded the sampling error markedly; the impact of volunteer sampling error on biomass estimates was ±15%, and the expert range was ±9%. Using dendrometer bands, used to measure growth rates, we calculated that the volunteer (vs. expert) sampling error was 0.6 mm (vs. 0.3 mm), which is equivalent to a difference in carbon storage of ±0.011 kg C/yr (vs. ±0.002 kg C/yr) per stem. Using a citizen science model for monitoring carbon stocks not only has benefits in educating and engaging the public in science, but as demonstrated here, can also provide accurate estimates of biomass or forest carbon stocks. [ABSTRACT FROM AUTHOR]