Percentile Interval Research Articles

Percentile Bootstrap Interval on Univariate Local Polynomial Regression Prediction

This study offers a new technique for constructing percentile bootstrap intervals to predict the regression of univariate local polynomials. Bootstrap regression uses resampling derived from paired and residual bootstrap methods. The main objective of this study is to perform a comparative analysis between the two resampling methods by considering the nominal coverage probability. Resampling uses a nonparametric bootstrap technique with the return method, where each sample point has an equal chance of being selected. The principle of nonparametric bootstrapping uses the original sample data as a source of diversity in contrast to parametric bootstrapping, where the variety comes from generating a particular distribution. The simulation results show that the paired and residual bootstrap interval coverage probabilities are close to nominal coverage. The results showed no significant difference between paired bootstrap interval and percentile residual. Increasing the bootstrap sample size sufficiently large gives the scatterplot smoothness of the confidence interval. Applying the smoothing parameter by choice gives a second-order polynomial regression with a smoother distribution than the first-order polynomial regression. The scatterplot shows that the second-degree polynomial regression can capture the data curvature feature compared to the first-degree polynomial. The bands made from second-degree polynomials give a narrower width than first-degree polynomials. In contrast, applying optimal smoothing parameters to the model provides different conclusions by using smoothing parameters based on choice. In addition to the differences based on the scatterplot, the bootstrap estimates of the coverage probability are also other. Selecting smoothing parameters based on a particular value provides probability coverage with the paired bootstrap method for the first-degree local polynomial regression is 0.93, while the second-degree local polynomial is 0.96. The probability of coverage based on the residual bootstrap method for the first-degree local polynomial regression is 0.95, while the second-degree local polynomial is 0.96. The probability coverage based on the optimal parameters of the paired bootstrap method for the first-degree local polynomial regression is 0.945, while the second-degree local polynomial is 0.93. The residual bootstrap method gives the first-degree local polynomial regression of 0.95, while the second-degree local polynomial is 0.93. In general, both bootstrap methods work well for estimating prediction confidence intervals.

Impact of increased antiretroviral therapy use during the treatment as prevention era in Australia.

We analysed the impact of increased antiretroviral therapy (ART) on HIV epidemiology and healthcare costs in Australia during the 'Treatment-as-prevention' and 'Undetectable equals Untransmissible (U=U)' eras. We conducted a retrospective modelling analysis between 2009 and 2019 to calculate the potential impact of early initiation of ART and treatment-as-prevention on HIV among gay and bisexual men (GBM). The model incorporates the change in the proportion diagnosed, treated, and virally suppressed, as well as the scale-up of oral HIV pre-exposure prophylaxis (PrEP) and the change in sexual behaviour during this period. We simulated a baseline and a no ART increase scenario and conducted a costing analysis from a national health provider perspective with cost estimates in 2019 AUD. Increasing ART use between 2009 and 2019 averted an additional 1624 [95% percentile interval (PI): 1220-2099] new HIV infections. Without the increase in ART, the number of GBM with HIV would have increased from 21 907 (95% PI: 20 753-23 019) to 23 219 (95% PI: 22 008-24 404) by 2019. HIV care and treatment costs for people with HIV increased by $296 (95% PI: $235-367) million AUD (assuming no change in annual healthcare costs). This was offset by a decrease in the lifetime HIV costs (with 3.5% discounting) for those newly infected of $458 (95% PI: $344-592) million AUD, giving a net cost saving of $162 (95%: $68-273) million AUD (and a benefits-to-cost ratio of 1.54). Increasing the proportion of Australian GBM on effective ART between 2009 and 2019 likely resulted in substantial reductions in new HIV infections and cost savings.

Addictive behaviour of students: Early detection experience and socio-psychological characteristics

Introduction. Today, there is an increase in the risks of involving minors in various forms of deviant behaviour, among the varieties of which addictive behavioural models are particularly dangerous. In this regard, proactive preventive measures are becoming particularly relevant, among which one of the key areas is the timely identification of potential addicts, including among students of educational organisations.Aim. The aim of the study was to identify the actual socio-psychological characteristics of the personality of underage students who are prone to addictive behaviour.Methodology and research methods. The comparative-analytical method of working with scientific literature presented in eLIBRARY.RU, Scopus, Web of Science and Google Scholar databases was used as the main theoretical method. When organising the study, the authors relied on a number of methodological provisions: the structural method, when the research material is interpreted in accordance with the specific characteristics of the object of study; the method of comparisons, which consists in comparing individual results with each other. The main empirical method was the testing method. To obtain the results of the study, the authors employed a unified methodology of socio-psychological testing aimed at the identification of the risk of formation of addictive behaviour in minors. For mathematical and statistical data processing, the Statistics SPSS 19.0 package was applied: the Kolmogorov-Smirnov Dmax criterion (for boys and girls separately and jointly), the Mann-Whitney U test, regression analysis, analysis of averages, confidence and percentile intervals. Also, the factor analysis method was used – the principal component method, Varimax rotation. The stability of factors was calculated using the Lewandowski’s method. The study was conducted in different regions of Russia in 2019–2020. The sample consisted of 6,300 people.Results. Based on the analysis of scientific sources, the authors described the epidemiological picture of various forms of addictive behaviour of minors, the differences between male and female samples (young men demonstrate a greater desire for leadership, protest reactions, rivalry, and girls are more anxious, frustrated, focused on internal experiences), the interrelationships between the scales of the methodology and the poles of the selected factor structure from the point of view of asociality and prosociality of juvenile behaviour (desire for risk, social maladjustment, avoidance of conflict and others).Scientific novelty. The scientific novelty of the results is due to the receipt of relevant data on the specifics of the socio-psychological characteristics of modern adolescents and young men prone to addictive behaviour.Practical significance. The practical significance lies in the fact that the identified socio-psychological characteristics of the behaviour of a person prone to addictive behaviour can become the main planning work on the early detection of a tendency to addictive behaviour, organisation of preventive work with imperfect-year-olds, as well as for the organisation of psychological work with students at risk.

Statistical Inference in the Cumulative Exposure Lognormal Model with Hybrid Censoring

This research aims to analyze data coming from step stress life testing experiments where the stress level is incremented at a preset time to obtain failure data faster. To analyze step stress data, a model that extrapolates the information attained from the accelerated tests to normal conditions needs to be fit to the life test data. We used the Cumulative Exposure Model (CEM) to model simple step stress lognormal life test data where hybrid censoring is present and applied the maximum likelihood estimation method to find the point and interval estimates of the parameters. Bootstrap intervals (bootstrap-t intervals and percentile intervals) were also constructed. We then performed a simulation study to assess the proposed methods of estimation under different hybrid censoring schemes. The Bias and MSE of the maximum likelihood estimators (MLEs) along with the coverage probability and average lengths of the corresponding confidence intervals were investigated. Finally, an illustrative example has been used to demonstrate the application of the methods discussed in this paper.

Empowerment and job satisfaction in university teachers: A theory of power in educational organizations

AbstractWhile the importance of teacher job satisfaction is well documented, the processes and mechanisms by which educational institutions manage to influence the satisfactions of their members are not. Studies on the organizational elements associated with satisfaction have focused on isolated factors without the common umbrella of a consolidated theoretical model. Based on Kanter's theory of power in organizations, we aimed to analyze the mediator role of psychological empowerment between structural empowerment and job satisfaction in a sample of 267 university teachers in southern Spain. A cross‐sectional study was carried out, utilizing questionnaires for data collection and convenience sampling. Percentile confidence intervals, based on 5000 resamples, were calculated to test the mediation model. Results showed that a two‐way effect of structural empowerment on job satisfaction: a direct effect based on access to organizational resources of power and an indirect effect through the development of positive cognitions at work. Access to opportunities, resources, information, and support empower university teachers psychologically, fostering their intrinsic motivation and increasing their job satisfaction, key elements for teaching quality in higher education.

Predictive performance and clinical application of COV50, a urinary proteomic biomarker in early COVID-19 infection: a prospective multicentre cohort study

BackgroundThe SARS-CoV-2 pandemic is a worldwide challenge. The CRIT-CoV-U pilot study generated a urinary proteomic biomarker consisting of 50 peptides (COV50), which predicted death and disease progression from SARS-CoV-2. After the interim analysis presented for the German Government, here, we aimed to analyse the full dataset to consolidate the findings and propose potential clinical applications of this biomarker.MethodsCRIT-CoV-U was a prospective multicentre cohort study. In eight European countries (Austria, France, Germany, Greece, North Macedonia, Poland, Spain, and Sweden), 1012 adults with PCR-confirmed COVID-19 were followed up for death and progression along the 8-point WHO scale. Capillary electrophoresis coupled with mass spectrometry was used for urinary proteomic profiling. Statistical methods included logistic regression and receiver operating characteristic curve analysis with a comparison of the area under curve (AUC) between nested models. Hospitalisation costs were derived from the care facility corresponding with the Markov chain probability of reaching WHO scores ranging from 3 to 8 and flat-rate hospitalisation costs adjusted for the gross per capita domestic product of each country.FindingsFrom June 30 to Nov 19, 2020, 228 participants were recruited, and from April 30, 2020, to April 14, 2021, 784 participants were recruited, resulting in a total of 1012 participants. The entry WHO scores were 1–3 in 445 (44%) participants, 4–5 in 529 (52%) participants, and 6 in 38 (4%) participants; and of all participants, 119 died and 271 had disease progression. The odds ratio (OR) associated with COV50 in all 1012 participants for death was 2·44 (95% CI 2·05–2·92) unadjusted and 1·67 (1·34–2·07) when adjusted for sex, age, BMI, comorbidities, and baseline WHO score; and for disease progression, the OR was 1·79 (1·60–2·01) when unadjusted and 1·63 (1·41–1·91) when adjusted (p<0·0001 for all). The predictive accuracy of the optimised COV50 thresholds was 74·4% (71·6–77·1%) for mortality (threshold 0·47) and 67·4% (64·4–70·3%) for disease progression (threshold 0·04). When adjusted for covariables and the baseline WHO score, these thresholds improved AUCs from 0·835 to 0·853 (p=0·033) for death and from 0·697 to 0·730 (p=0·0008) for progression. Of 196 participants who received ambulatory care, 194 (99%) did not reach the 0·04 threshold. The cost reductions associated with 1 day less hospitalisation per 1000 participants were million Euro (M€) 0·887 (5–95% percentile interval 0·730–1·039) in participants at a low risk (COV50 <0·04) and M€2·098 (1·839-2·365) in participants at a high risk (COV50 ≥0·04).InterpretationThe urinary proteomic COV50 marker might be predictive of adverse COVID-19 outcomes. Even in people with mild-to-moderate PCR-confirmed infections (WHO scores 1–4), the 0·04 COV50 threshold justifies earlier drug treatment, thereby potentially reducing the number of days in hospital and associated costs.FundingGerman Federal Ministry of Health.

Establishment of reference intervals for serum amylase and lipase: a large sample study based on Chinese healthy children.

This study aimed to establish age- and sex-specific reference intervals for serum amylase and lipase in children by prospective investigation. A total of 6466 healthy Han children of 1 month ∼ <18 years were recruited in communities and schools in five administrative regions of Jilin Province, China. The serum amylase and lipase concentrations were measured on the VITROS 5600 integrated system. The reference intervals were defined by a nonparametric 95% percentile interval. Reference intervals of amylase were divided into four age-specific partitions. It increased rapidly between the ages of 1 month ∼ <1 year and 1 ∼ <5 years, slightly decreased between the ages of 5 ∼ <12 years, and increased slowly between 12 ∼ <18 years. Lipase concentrations showed sex differences after 1-year-old, with reference intervals in males divided into 2 divisions and 3 divisions in females. The lipase concentrations in both males and females showed an upward trend, and the lipase concentrations in females were higher than those in males. This study established reference intervals for amylase and lipase in healthy Chinese children and provided a more accurate explanation for the diagnosis and prognosis of clinical pediatric diseases.

Automatic jitter measurement in needle-detected motor unit potential trains

In an active motor unit (MU), the time intervals between the firings of its muscle fibers vary across successive MU activations. This variability is called jitter and is increased in pathological processes that affect the neuromuscular junctions or terminal axonal segments of MUs. Traditionally, jitter has been measured using single fiber electrodes (SFEs) and a difficult and subjective manual technique. SFEs are expensive and reused, implying a potential risk of patient infection; so, they are being gradually substituted by safer, disposable, concentric needle electrodes (CNEs). As CNEs are larger, voltage contributions from individual fibers of a MU are more difficult to detect, making jitter measurement more difficult. This paper presents an automatic method to estimate jitter from trains of motor unit potentials (MUPs), for both SFE and CNE records. For a MUP train, segments of MUPs generated by single muscle fibers (SF MUP segments) are found and jitter is measured between pairs of these segments. Segments whose estimated jitter values are not reliable, according to several SF MUP segment characteristics, are excluded. The method has been tested in several simulation studies that use mathematical models of muscle fiber potentials. The results are very satisfactory in terms of jitter estimation error (less than 10% in most of the cases studied) and mean number of valid jitter estimates obtained per simulated train (greater than 1.0 in many of the cases and less than 0.5 only in the most complicated). A preliminary study with real signals was also performed, using 19 MUP trains from 3 neuropathic patients. Jitter measurements obtained by the automatic method were compared with those extracted from a commercial system (Keypoint) and the edition and supervision of an expert electromyographer. From these measurements 63% were taken from equivalent interval pair sites within the time span of the MUP trains and, as such, were considered as compatible measurements. Differences in jitter of these compatible measurements were very low (mean value of 1.3 μs, mean of absolute differences of 2.97 μs, 25% and 75% percentile intervals of −0.85 and 3.82 μs, respectively). Although new tests with larger number of real recordings are still required, the method seems promising for clinical practice.

Bootstrap-based inferential improvements to the simplex nonlinear regression model.

In this paper we evaluate the performance of point and interval estimators based on the maximum likelihood(ML) method for the nonlinear simplex regression model. Inferences based on traditional maximum likelihood estimation have good asymptotic properties, but their performance in small samples may not be satisfactory. At out set we consider the maximum likelihood estimation for the parameters of the nonlinear simplex regression model, and so we introduced a bootstrap-based correction for such estimators of this model. We also develop the percentile and bootstrapt confidence intervals for those parameters as competitors to the traditional approximate confidence interval based on the asymptotic normality of the maximum likelihood estimators (MLEs). We then numerically evaluate the performance of these different methods for estimating the simplex regression model. The numerical evidence favors inference based on the bootstrap method, in special the bootstrapt interval, which was decisive in an application to real data.

Imaging Signs of May-Thurner Syndrome in Asymptomatic Patients: Computed Tomography Angiography Analysis of Kidney Donors

The current study aimed to evaluate the distance between the right common iliac artery (RCIA) and lumbar vertebra in asymptomatic patients in order to determine whether such distance was statistically correlated with the left common iliac vein (LCIV) diameter (LCIVD) and to investigate if both measures were related to demographic characteristics and anthropometric data, such as sex, age, height, and body mass index (BMI). In this descriptive and uncontrolled anatomic study, data from high-definition computed tomography (CT) angiography images of living kidney donors without a medical history of chronic venous insufficiency or past deep vein thrombosis (DVT) were analyzed. The RCIA crossed over the LCIV in 311 individuals, who were then included in this study. CT scans were reviewed to measure (1) the narrowest space between the RCIA and fifth lumbar vertebral body and (2) the LCIVD. Measures were subjected to normality tests and were divided according to the sex of the study population. Correlations of measures with age, BMI, and height were calculated. Of the 311 patients analyzed, 66.6% (n=207) were female. The mean lumbar vertebral body-iliac artery distance (LVBIAD) was 7.2mm, whereas the mean LCIVD was 8.5mm; both were higher in men (P<0.001). The statistical analysis of LVBIAD and LCIVD distributions revealed no normality pattern (P<0.05). The analysis of the correlation between them showed a weak statistically significant relationship with age. A linear regression model considering the normality percentile interval indicated a strong positive correlation between LVBIAD and LCIVD (R2=0.884). The LVBIAD was <5mm and <3mm in 25% and 5% of asymptomatic individuals, respectively. The LCIVD correlated with the space between the RCIA and lumbar vertebra. The distance between the RCIA and lumbar vertebra and the LCIVD were higher in male subjects and older patients, but did not correlate with BMI and height.

Comparing methods for assessing a difference in correlations with dependent groups, measurement error, nonnormality, and incomplete data.

I compared multiple methods to estimate and test a difference in correlations (ρdiff) between two variables that are repeatedly measured or originate from dyads. Fisher's z transformed correlations are often used for testing ρdiff. However, raw scores are typically used directly to compute correlations under this popular method, whose performance has not been evaluated with measurement error or nonnormality in data. Structural equation modeling (SEM) can handle measurement error via latent-variable modeling. To handle nonnormality for testing a difference in correlations in dependent groups, when measurement error was of no concern, I discussed SEM using maximum likelihood estimation (ML) with sandwich-type standard errors, ML with bootstrap percentile or bias-corrected confidence intervals, and Bayesian credible intervals. In addition, I also examined a mean- and variance-adjusted likelihood ratio test and the Bollen-Stine bootstrapping model fit test. When measurement error existed, ρdiff was tested under a latent variable model. Furthermore, under the same SEM frameworks with complete data, I also assessed ρdiff with ignorable missing data. A simulation study was conducted considering whether measurement error existed, whether data were normal, and whether data were complete. Empirical examples were used for illustration. Recommendations were then given based on the findings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bootstrap confidence intervals for 11 robust correlations in the presence of outliers and leverage observations

Researchers often examine whether two continuous variables (X and Y) are linearly related. Pearson’s correlation (r) is a widely-employed statistic for assessing bivariate linearity. However, the accuracy of r is known to decrease when data contain outliers and/or leverage observations, a circumstance common in behavioral and social sciences research. This study compares 11 robust correlations with r and evaluates the associated bootstrap confidence intervals [bootstrap standard interval (BSI), bootstrap percentile interval (BPI), and bootstrap bias-corrected-and-accelerated interval (BCaI)] across conditions with and without outliers and/or leverage observations. The simulation results showed that the median-absolute-deviation correlation (r-MAD), median-based correlation (r-MED), and trimmed correlation (r-TRIM) consistently outperformed the other estimates, including r, when data contain outliers and/or leverage observations. This study provides an easy-to-use R code for computing robust correlations and their associated confidence intervals, offers recommendations for their reporting, and discusses implications of the findings for future research.

ALTERNATIVES TO THE CLASSICAL FREQUENTIST CONFIDENCE INTERVAL FOR DESCRIBING ZERO-INFLATED LEAF DISEASE SEVERITY

This paper presents the bootstrap percentile interval and the Bayesian credible interval as alternatives to the classical frequentist confidence interval for analysis of zero-inflated data. The indicated methods were applied to soybean downy mildew severity data obtained by stratified sampling in two municipalities in the state of São Paulo: Estiva Gerbi and Piracicaba. The amplitudes of the frequentist and bootstrap percentile confidence intervals were similar. For the Bayesian approach, the credible intervals of the posterior predictive distribution were considered using the zero-inflated beta distribution as likelihood. The credible intervals showed a wider range and included values in the upper bounds of the intervals greater than those observed in the data. We conclude that Bayesian inference is more complex, but allows incorporation of prior information regarding regional and seasonal aspects, contributing to better disease management in the field. When this information is not known, nonparametric bootstrap resampling is a simple alternative to construct intervals for zero-inflated data without assuming the distribution function.

Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models

This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard errors.

Establishment of Reference Values for Pepsinogens in Healthy Subjects of the Gansu Province: a Cross-Sectional Study.

At present, the pepsinogen reference values applicable to subjects from Gansu province have not been established. Therefore, the current study aimed to establish reference values for PGI, PGII, and the PGI/ PGII ratio in Gansu Province, Northwest China. The present study was a cross-sectional study. Following screening in the physical examination center of Gansu Provincial Hospital, 2,130 healthy subjects were enrolled (age range 18 - 88 years; BMI range 15.35 - 38.89 kg/m2) from March 2018 to December 2020. Serum PGI and PG II concentration levels were detected by chemiluminescence. The reference values were defined according to age and gender by non-parametric 95th percentile intervals. The increase in age caused a gradual increase in the levels of PG I and PG II, while PG I/PG II ratio gradually decreased. The PG I, PG II and PG I/PG II ratio in males were significantly higher than those in females. The reference values for PG I, PG II and PG I/PG II ratio in males: < 40 years old were 22.79 - 119.79 ng/mL, 3.02 - 21.57 ng/mL, and 2.99 - 10.25, respectively; ≥ 40 years old were 17.58 - 125.12 ng/mL, 3.70 - 25.84 ng/mL, and 1.52 - 10.53, respectively. The reference values for PG I, PG II, and PG I/PG II ratio in females: < 40 years old were 22.57 - 103.90 ng/mL, 3.17 - 20.73 ng/mL, and 2.28 - 10.46, respectively; ≥ 40 years old were 14.24 - 117.81 ng/mL, 3.36 - 29.57 ng/mL, and 1.26 - 9.85, respectively. The present study determined the missing reference values of serum PGs for healthy subjects of different gender and ages in Gansu Province.

Establishment of SARS-CoV-2 Immunoglobulins (IgM, IgG) Reference Intervals for Elder Population in China based on 3,733 Samples.

Establishment of reference intervals (RIs) for different biomarkers is essential for clinical monitoring. The purpose of this study was to establish laboratory RIs of SARS-CoV-2 IgM and IgG for elder population. Performance verification was conducted with reference to the Clinical and Laboratory Standards Institute (CLSI) guidelines, including linearity, imprecision, and allowable dilution ratio. Based on CLSI C28-A3 document, a total of 3,734 serum samples were collected, and 3,733 serum samples were used for the establishment of RIs for SARS-CoV-2 IgM and IgG. The subjects were grouped by gender and age. The age groups were as follows: 60 - 69 years, 70 - 79 years, 80 - 89 years, and 90 - 101 years. The RI was defined by nonparametric 95th percentile intervals. Percentage deviation of all the seven dilutions were all less than 12.5% during linearity evaluation. The inter-assay and intra-assay imprecision were all less than 5%. There is no significant difference between different gender and age groups for IgM (p = 0.0818, p = 0.7094), and there is significant difference between different gender and age groups for IgG (p = 0.0011, p = 0.0013). Harris-Boyd's test did not indicate partitioning for IgM and IgG. Cutoff values of RI for SARS-CoV-2 IgM and IgG were defined as 0.1523 S/CO and 0.2663 S/CO, respectively. RIs of SRAR-CoV-2 IgM and IgG were established for elder population, which can play an important role in the prevention and control of the epidemic.

Aspirin use is associated with increased risk for incident heart failure: a patient-level pooled analysis.

AimsRecent trials evaluating the effect of aspirin in the primary prevention of cardiovascular disease showed little or no benefit. However, the role of aspirin on the risk of incident heart failure (HF) remains elusive. This study aimed to evaluate the role of aspirin use on HF incidence in primary and secondary prevention and whether aspirin use increases the risk of incident HF in patients at risk.Methods and resultsData from 30 827 patients at risk for HF enrolled in six observational studies were analysed [women 33.9%, mean age (±standard deviation) 66.8 ± 9.2 years]. Cardiovascular risk factors and aspirin use were recorded at baseline, and patients were followed up for the first incident of fatal or non‐fatal HF. The association of incident HF with aspirin use was assessed using multivariable‐adjusted proportional hazard regression, which accounted for study and cardiovascular risk factors. Over 5.3 years (median; 5th–95th percentile interval, 2.1–11.7 years), 1330 patients experienced HF. The fully adjusted hazard ratio (HR) associated with aspirin use was 1.26 [95% confidence interval (CI) 1.12–1.41; P ≤ 0.001]. Further, in a propensity‐score‐matched analysis, the HR was 1.26 (95% CI 1.10–1.44; P ≤ 0.001). In 22 690 patients (73.6%) without history of cardiovascular disease, the HR was 1.27 (95% CI 1.10–1.46; P = 0.001).ConclusionsIn patients, at risk, aspirin use was associated with incident HF, independent of other risk factors. In the absence of conclusive trial evidence, our observations suggest that aspirins should be prescribed with caution in patients at risk of HF or having HF.

Establishing reference intervals for islet autoantibodies in Han Chinese type 1 diabetes

Currently, islet autoantibodies (IAbs) constitute the most reliable marker for detecting the autoimmune process of type 1 diabetes (T1D). However, there are no appropriate reference intervals (RIs) to interpret the results of IAbs in China. In this study, we aimed to establish the RIs of four common IAbs based on the Han Chinese population and evaluate their clinical diagnostic values in patients with T1D. We collected 177 blood samples from healthy volunteers to detect the levels of IAbs directed against insulin (IAA), glutamic acid decarboxylase-65 (GADA), insulinoma antigen 2 (IA-2A), and zinc transporter-8 (ZnT8A) using a chemiluminescence immunoassay. RIs were calculated using nonparametric 95th percentile intervals in accordance with the Clinical and Laboratory Standards Institute guidelines, and their clinical diagnostic values were evaluated by detecting the levels of IAbs of 140 blood samples from patients with T1D in a clinical setting. We defined 138 individuals as the apparently healthy population from the 177 healthy volunteers based on the exclusion criteria. No association between the levels of the four IAbs and gender (p > .05) and age (p > .05) were found in the apparently healthy population. The combined RIs for GADA, IA-2A, ZnT8A, and IAA were 0–1.78 IU/mL, 0–3.91 IU/mL, 0–2.36 AU/mL, and 0–0.58 COI, respectively. Overall, the diagnostic efficiency for the four IAbs, especially for GADA and IAA, were improved by using the RIs established in this study. The RIs for IAbs established in this study will be a valuable tool for disease diagnosis and the therapeutic management of T1D in a clinical setting.

Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number

AbstractHydrologic models are often employed in flood risk studies to simulate possible hydrologic responses. They are, however, linked with uncertainty that is commonly represented with uncertainty intervals constructed based on a simulation ensemble. This work adapts an alternative clustering‐based approach to first, learn about hydrological responses in the frequency space, and second, select an optimal number of clusters and corresponding representative parameters sets for a hydrologic model. Each cluster is described with three parameter sets, which enable percentile and prediction intervals to be constructed. Based on a small Swiss catchment with 10,000 years of daily pseudo‐discharge simulations, it was found that clustering the ensemble of 1000 members into 5–7 groups is optimal to derive reliable flood prediction intervals in the frequency space. This lowers the computational costs of using a hydrological model by 98%. The developed approach is suitable for probabilistic flood risk analysis with current or future climate conditions to assess hydrologic changes.

A note on point estimation and interval estimation of the relative treatment effect under a simple crossover design.

To increase power or reduce the number of patients needed for a parallel groups design, the crossover design has been often used to study treatments for noncurable chronic diseases. However, in the presence of carry-over effect caused by treatments, the commonly-used estimator which ignores the carry-over effect leads to a biased estimator for estimating the treatment effect difference. A two-stage test approach aimed to address carry-over effect proposed was found to be potentially misleading. In this paper, we propose a weighted average of the commonly-used estimator and an unbiased estimator that uses only the first period of the data. We derive an optimal weight that minimizes the mean squared error (MSE) and its modified estimator. We apply Monte Carlo simulation to evaluate the performance of the proposed estimators in a variety of situations. In the simulations, we examine the estimated MSE (EMSE), percentile interval length, and coverage probability calculated from the percentile intervals among considered estimators. Simulation results show that our proposed weighted average estimator and its modified estimator lead to smaller EMSEs on average comparing to the two commonly used estimators. The coverage probabilities using our proposed estimators are reasonably close to the nominal confidence level and the interval lengths are shorter comparing to the use of the unbiased estimator that uses only the first period of the data. We apply an example that was to evaluate the efficacy of two type of bronchodilators for asthma treatment to demonstrate the use of the proposed estimators.