Nonparametric Bootstrap Techniques Research Articles

An interactive 3D atlas of sentinel lymph nodes in breast cancer developed using SPECT/CT

BackgroundThe identification and assessment of sentinel lymph nodes (SLNs) in breast cancer is important for optimised patient management. The aim of this study was to develop an interactive 3D breast SLN atlas and to perform statistical analyses of lymphatic drainage patterns and tumour prevalence.MethodsA total of 861 early-stage breast cancer patients who underwent preoperative lymphoscintigraphy and SPECT/CT were included. Lymphatic drainage and tumour prevalence statistics were computed using Bayesian inference, non-parametric bootstrapping, and regression techniques. Image registration of SPECT/CT to a reference patient CT was carried out on 350 patients, and SLN positions transformed relative to the reference CT. The reference CT was segmented to visualise bones and muscles, and SLN distributions compared with the European Society for Therapeutic Radiology and Oncology (ESTRO) clinical target volumes (CTVs). The SLN atlas and statistical analyses were integrated into a graphical user interface (GUI).ResultsDirect lymphatic drainage to the axilla level I (anterior) node field was most common (77.2%), followed by the internal mammary node field (30.4%). Tumour prevalence was highest in the upper outer breast quadrant (22.9%) followed by the retroareolar region (12.8%). The 3D atlas had 765 SLNs from 335 patients, with 33.3–66.7% of axillary SLNs and 25.4% of internal mammary SLNs covered by ESTRO CTVs.ConclusionThe interactive 3D atlas effectively displays breast SLN distribution and statistics for a large patient cohort. The atlas is freely available to download and is a valuable educational resource that could be used in future to guide treatment.

Confidence sets for intraclass correlation coefficients in test–retest curve measurements

Evaluating test–retest reliability is crucial in biomechanical research, as it validates experimental results. While methods for reliability of scalar outcome variables are well-established, methods to assess reliability of continuous curve data (such as joint angle trajectories during gait) remain less explored. This study investigates methods for constructing confidence sets for curve-level intraclass correlation coefficients (ICC), which can be expressed as either an ICC curve or an integrated ICC. Currently, no standardised guidelines exist in biomechanics for reporting curve-level ICC uncertainty. Nonparametric bootstrapping techniques are proposed for both the ICC curve’s confidence bands and the integrated ICC’s confidence intervals, and these methods are validated through Monte Carlo simulations, covering various effect sizes and curve characteristics. Additionally, these methods are applied to assess the test–retest reliability of knee kinematics in three different planes during landing of one-leg hops, where less uncertainty is observed for the ICC curve and integrated ICC in the frontal plane compared to other planes. When the entire time domain is of primary empirical interest, we recommend using a rank-based bootstrap confidence band to express ICC uncertainty, as it yields increasingly precise and valid results as the number of individuals increases, with the coverage rate approaching the correct level of 95%. When a single summary metric is of primary interest, we recommend using the integrated ICC along with a typical bootstrap confidence interval based on the normal distribution, as the coverage rate remains adequately accurate and stable at around the correct level of 95% across varying number of individuals.

Efficient Bayesian inference for a defect rate based on completely censored data

This paper discusses the challenging issues that reliability practitioners face in conducting destructive tests that lead to completely censored lifetimes, especially in estimating the defect rate of products. Manufacturers need to measure the defect rate for quality control purposes, but obtaining enough defective devices for accurate estimation is not easy when the defect rate is relatively low. To address the issues, a Bayesian approach for estimating the defect rate is proposed in this paper. The proposed method is devised to make up for the heavy computational burdens of the Metropolis-Hastings algorithm. To quantify the uncertainty in the Bayesian estimation, a nonparametric bootstrap technique is employed to construct a credible interval for the defect rate. The performance of the proposed method is evaluated through a variety of Monte Carlo simulation studies. The efficiency of the proposed Bayesian estimation procedure is validated using a real-world dataset of return-springs in DC motor systems under an accelerated destructive degradation test.

Association between breakfast skipping and psychosomatic symptoms among Canadian adolescents.

This cross-sectional study aimed to examine the mediating roles of gender and substance use in the influence of breakfast skipping on psychosomatic symptoms. The study used data among a nationally-representative sample of 2855 Canadian adolescents who participated in the 2018 Health Behavior in School-aged Children (HBSC) survey. Sequential logistic regressions were used to estimate the associations between breakfast skipping and psychosomatic symptoms. Path analysis using a non-parametric bootstrapping technique tested the hypothesized mediating roles. Results showed that adolescents who skipped breakfast were 2.55 times more likely to report higher psychosomatic symptoms compared to non-breakfast skippers (AOR = 2.55; 95% CI = 1.75-3.82). The bootstrapping mediation models showed that breakfast skipping indirectly influenced psychosomatic symptoms through substance use and gender, accounting for 18.47% ( = 0.0052, Boots 95% CI = 0.0025, 0.00730) and 10.70% ( = 0.0091, Boots 95% CI = 0.0052, 0.0125), respectively, of the total effect. Our findings have important implications for targeted public and mental health interventions to address both breakfast skipping and psychosomatic symptoms among adolescents. Conclusion: The study compellingly underscores the significance of incorporating gender-specific factors and substance use in understanding the correlationbetween breakfast skipping and psychosomatic symptoms. These insights hold importance for tailoring public health interventions to alleviate theprevalence of psychosomatic symptoms among adolescents by actively addressing breakfast skipping. What is Known: • Breakfast is considered the most important meal of the day due to its role in providing the brain with the energy necessary to enhance cognitive functions. • Adolescents commonly exhibit a prevalent lifestyle behaviour of skipping breakfast. What is New: • This study provides robust evidence supporting the association between breakfast skipping and elevated psychosomatic symptoms in adolescents. • Gender and substance use mediate this association, offering novel insights into the complex interplay that contributes to psychosomatic symptoms among this demographic group. • Longitudinal research is needed to unravel causal relationships and illuminate the underlying mechanisms of this intricate connection.

The Interplay Between Long Memory and Bootstrap Technique in Virtual and Real Currency Markets: The Evidence on the Pre/Post-COVID Era

This article explores the interplay between long memory and the parametric and nonparametric bootstrap techniques in virtual and real currency markets, specifically focusing on Bitcoin and the US dollar (USD) from 2019 to late 2022. The study compares the properties of the bootstrap tests, analyzes long memory effects, and evaluates the significance using P-value plots. Additionally, the research examines the corrected size-power curves to address size distortions. The findings confirm the presence of long memory in the examined currency markets and underscore the importance of accurate bootstrap utilization for robust analysis. The study provides valuable insights for investors and decision-makers in understanding the dynamics of the pre/post-COVID era.

Problematic social media use and overweight/obesity: explanatory pathway analysis of 124 667 in-school adolescents in 39 high-income countries.

The relationship between adolescent problematic social media use (PSMU) and overweight/obesity and the potential explanatory pathways for this association are unclear. This study (1) examined the relationship between PSMU and overweight/obesity and (2) evaluated potential explanatory pathways for this association. The data used in this study came from the 2018 Health Behaviour in School-aged Children (HBSC) survey. A sample of 124 667 in-school adolescents from 39 high-income countries was analysed. Sequential logistic regressions were used to estimate the association between PSMU and overweight/obesity. Pathway analysis using a non-parametric bootstrapping technique tested the hypothesized mediating roles. The multivariable logistic regressions indicated that higher problematic social media users were 1.11 times more likely to be overweight/obese compared with those who reported low PSMU (AOR = 1.11; 95% CI = 1.05-1.18). Our bootstrapping mediation analyses showed that PSMU indirectly influenced overweight/obesity through breakfast skipping, life satisfaction, family communication, self-rated health, and physical activity, accounting for 19.8% (β = 0.0068, Boots 95% CI = 0.0056-0.0074), 15.1% (β = 0.0050, Boots 95% CI = 0.0046-0.0056), 9.2% (β = 0.0031, Boots 95% CI = 0.0024-0.0038), 7.8% (β = 0.0024, Boots 95% CI = 0.001-0.004), and 5.2% (β = 0.0017, Boots 95% CI = 0.0014-0.0023), respectively, of the total effect. To the best of our knowledge, this is the first study that used nationally representative data from multiple countries in high-income countries to examine the association between PSMU and overweight/obesity and potential explanatory pathways among school-going adolescents. This finding has important implications for public health interventions to reduce overweight/obesity rates among young people.

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.

A new quadratic asymmetric error correction model: does size matter?

We introduce a new quadratic asymmetric error correction model that comprehensively accounts for both sign and size asymmetries. We also propose a test protocol that allows to rigorously identify different sources of long-run nonlinearity, namely quadratic nonlinearity, size asymmetry and sign asymmetry. We use a nonparametric residual recursive bootstrap technique to report p-values for the long-run tests. Simulation results confirm the consistency of our proposed estimator in finite samples and show that the bootstrapped tests have reasonably good size and power properties. Although our estimation of the Okun’s Law for the USA confirms previous findings on the direction of the sign asymmetry, its reveals that the magnitude of the impact of economic downturns on unemployment decreases faster than the impact of upturns. Forecasting results show that our new model performs better than NARDL.

Evaluating and ranking the mining damage prevention programs in South Korea: An application of the fuzzy set theory

As various types of mining damage have occurred in South Korea, the mining damage prevention project has been in place since 2007. The project includes seven programs: abandoned mining facilities demolition program, mine drainage treatment program, mine subsidence prevention program, mine tailings loss prevention program, noise, vibration, and dust damage prevention program, reforestation program, and soil remediation program. This research seeks to derive the priorities of the programs through a national survey of 1000 people. Respondents are asked to judge the importance of individual programs on a 5-point scale and then the confidence in the importance judgment on a 5-point scale. Applying the fuzzy set theory comprehensively reflecting each respondent's importance judgment and the confidence in the judgment, the weights of the seven program are computed. Moreover, the confidence interval for the weights are obtained using the nonparametric bootstrap technique. Several policy implications, including the need to match the ranking of programs derived from this study with the ranking of the budget spent on the seven programs in the past, from the results are presented.

Physical activity telephone coaching intervention for insufficiently physically active ambulatory hospital patients: Economic evaluation of the Healthy 4U-2 randomised controlled trial.

The Healthy 4U-2 randomised controlled trial demonstrated that a physical activity (PA) telephone coaching intervention was effective for improving objectively-measured PA and health-related outcomes. The current study reports on an economic evaluation performed alongside the trial to determine whether this effective intervention is also cost-effective from a healthcare funder perspective. Participants (N = 120) were insufficiently physically active adults recruited from an ambulatory care clinic in a public hospital in regional Australia. The primary outcome was change in moderate-to-vigorous physical activity (MVPA) measured using accelerometers. Changes in quality-adjusted life-years (QALYs) were derived from the 12-Item Short Form Health Survey Questionnaire (SF-12). Incremental cost-effectiveness ratios (ICERs) were calculated for each outcome. Uncertainty of cost-effectiveness results were estimated using non-parametric bootstrapping techniques and sensitivity analyses. The mean intervention cost was $132 per person. The control group incurred higher overall costs compared to intervention ($2,465 vs. $1,743, respectively). Relative to control, the intervention resulted in incremental improvements in MVPA and QALYs and was deemed cost-effective. Probabilistic sensitivity analysis indicated that compared to control, the intervention would be cost-effective for improving MVPA and QALYs at very low willingness to pay thresholds. Sensitivity analyses indicated that results were robust to varied assumptions. This study shows that PA telephone coaching was a low-cost strategy for increasing MVPA and QALYs in insufficiently active ambulatory hospital patients. Findings of health benefits and overall cost-savings are uncommon and PA telephone coaching offers a potentially cost-effective investment to produce important public health outcomes.

Development and internal validation of an equation using anthropometric measures to predict correct endotracheal tube insertion depth.

PurposeTo develop, internally validate, and assess the utility of implementing a regression model for determining endotracheal tube (ETT) insertion depth.Research methodsWe recorded height, weight, age, sex, ETT internal diameter (ID), lip marking, and tracheal position from the electronic record from a random subset of 2,000 intubated subjects obtained from 1 January 2009 to 5 May 2012. A multivariable linear regression model was constructed and validated by a nonparametric bootstrapping technique using unrestricted random sampling methods. A prospective pilot of subjects admitted to the pediatric intensive care unit requiring invasive mechanical ventilatory support was conducted from 7 January 2019 to 31 May 2019. Those with spinal and/or skeletal malformations, without a post-intubation chest-x-ray (CXR) order, or whose CXR quality impaired visualizing the carina and ETT tip, were excluded. The validated regression equation determined insertion depth. CXR following intubation determined ETT position. Demographic data were summarized. Two-tailed, one-sample binomial test of proportions assessed differences in the proportion of correct position by the equation.Main findingsFour hundred and seventy-seven subjects included in model construction yielded 10,000 independent samples for internal validation; 55% were female, and the mean age (SD) was 47 (63) months. Bias between bootstrap coefficients and refined model estimates were negligible (P < 0.01). Eleven subjects in the pilot were female (64%), mean age (SD) of 36.7 (38) months. Four protocol violations (36.4%) resulted in malposition. Subsequent repositioning per protocol resulted in 100% correct positioning (P = 0.01).ConclusionThe regression equation [0.8636 * (Ht. 0.6223)] facilitated correct ETT placement. A larger, diverse sample is required for external model validation.

A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies.

In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation method. Also, nonparametric bootstrap techniques are used to approximate the confidence interval of the reliability parameter. A simulation is conducted to examine the mean square error, standard deviations, confidence intervals, and coverage probabilities of the reliability parameter. Finally, three real applications of the exponentiated sine Weibull model are provided. One of them considers stress-strength data.

Calibration of surgical tools using multilevel modeling with LINEX loss function: Theory and experiment.

Quantifying the tool–tissue interaction forces in surgery can be used in the training process of novice surgeons to help them better handle surgical tools and avoid exerting excessive forces. A significant challenge concerns the development of proper statistical learning techniques to model the relationship between the true force exerted on the tissue and several outputs read from sensors mounted on the surgical tools. We propose a nonparametric bootstrap technique and a Bayesian multilevel modeling methodology to estimate the true forces. We use the linear exponential loss function to asymmetrically penalize the over and underestimation of the applied forces to the tissue. We incorporate the direction of the force as a group factor in our analysis. A weighted approach is used to account for the nonhomogeneity of read voltages from the surgical tool. Our proposed Bayesian multilevel models provide estimates that are more accurate than those under the maximum likelihood and restricted maximum likelihood approaches. Moreover, confidence bounds are much narrower and the biases and root mean squared errors are significantly smaller in our multilevel models with the linear exponential loss function.

A New Quadratic Asymmetric Error Correction Model: Does Size Matter?

We introduce a new quadratic error correction model that comprehensively accounts for both sign and size asymmetries. We also propose a test protocol that allows to rigorously identify the different sources of long-run nonlinearity, namely, quadratic nonlinearity, size asymmetry and sign asymmetry. We use a non-parametric residual recursive bootstrap technique to report p-values for the long-run tests. Although, our estimation of the Okun’s Law for the U.S confirms previous findings on the direction of the sign asymmetry, its reveals that the magnitude of the impact of economic downturns on unemployment decreases faster than the impact of upturns. Forecasting results show that our new QAECM performs better than NARDL.

A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data

Segmented regression is a standard statistical procedure used to estimate the effect of a policy intervention on time series outcomes. This statistical method assumes the normality of the outcome variable, a large sample size, no autocorrelation in the observations, and a linear trend over time. Also, segmented regression is very sensitive to outliers. In a small sample study, if the outcome variable does not follow a Gaussian distribution, then using segmented regression to estimate the intervention effect leads to incorrect inferences. To address the small sample problem and non-normality in the outcome variable, including outliers, we describe and develop a robust statistical method to estimate the policy intervention effect in a series of longitudinal data. A simulation study is conducted to demonstrate the effect of outliers and non-normality in the outcomes by calculating the power of the test statistics with the segmented regression and the proposed robust statistical methods. Moreover, since finding the sampling distribution of the proposed robust statistic is analytically difficult, we use a nonparametric bootstrap technique to study the properties of the sampling distribution and make statistical inferences. Simulation studies show that the proposed method has more power than the standard t-test used in segmented regression analysis under the non-normality error distribution. Finally, we use the developed technique to estimate the intervention effect of the Istanbul Declaration on illegal organ activities. The robust method detected more significant effects compared to the standard method and provided shorter confidence intervals.

Show, Don't Tell: Education and Physical Experience Effects in Remanufactured Product Markets

Problem Definition: We study the effectiveness of two theoretically and practically relevant interventions designed to stimulate the appeal of and willingness to pay (wtp) for re-manufactured (refurbished) consumer products that are often found repulsive by consumers: 1) educating consumers about the re-manufacturing process, 2) providing physical experience with re-manufactured products. Academic/Practical Relevance: We contribute to the emerging literature on behavioral issues in a circular economy, which examines consumer attitudes toward sustainable product solutions and ways to improve them. We provide insights to practitioners regarding how the adoption rates and wtp for re-manufactured consumer products may (may not) be improved through certain managerial interventions. Methodology: We conduct an online survey and two randomized experiments in a controlled lab environment and a regression based mediation analysis with non-parametric bootstrapping technique to answer the research questions. Results: Education does not cause an increase in the appeal of and wtp for re-manufactured consumer products. Providing physical experience, relative to text and text plus picture modalities, significantly increases both the appeal of and wtp, as a result of increasing perceived quality and decreasing disgust. Managerial Implications: Sellers can benefit from marketing re-manufactured consumer products through physical channels (i.e., brick-and-mortar, outlet, showroom stores) as opposed to solely through online channels, which is the common wisdom.

Nonparametric Bootstrap Technique to Improve Positional Accuracy in Mobile Robots With Differential Drive Mechanism

Wheeled mobile robots (WMRs) inevitably experience positional inaccuracy, which is caused by a number of factors including design imperfections, problems with component fabrication, sensor errors, and electromechanical malfunction. We have previously proposed an odometry-based technique that reduces positional inaccuracy in WMRs driven with standard wheels. This technique combines high sampling rates and short-term accuracy, and calculates necessary lateral and longitudinal corrections by using linear regression to model the relationship between positional inaccuracy and angular velocities of the robot's wheels. This technique can do so without the precision of measurement required by other techniques. In this paper, we discuss how a nonparametric bootstrap approach can be used to find both interval and point estimates of the modified angular velocities required to alleviate the positional inaccuracy of the WMR. First, the robot travels along a path recommended by the odometry-based error reduction technique. Then, the positional and angular errors of the robot at the stop point are measured. Next, these measurements are used to estimate angular velocities, providing the necessary confidence intervals (prediction). Results from these calculations could be incorporated into the robot program to modify the movement along a given path (validation). To show viability, the bootstrap technique was applied to a prototype mobile robot while the robot was programmed to move along an unseen trajectory. Results indicate that, for this typical unseen path, the bootstrap technique is capable of improving real-time positional systematic and non-systematic inaccuracy with acceptable levels of precision compared to the linear regression technique under the normality assumption. The bootstrap technique exhibited better efficacy than the linear regression, therefore, it may be a useful tool to conduct real-time calibration of differential drive WMRs.

Cost-effectiveness of telephone coaching for physically inactive ambulatory care hospital patients: economic evaluation alongside the Healthy4U randomised controlled trial

ObjectiveTo assess whether telephone coaching is a cost-effective method for increasing physical activity and health-related quality of life for insufficiently active adults presenting to an ambulatory care clinic in a...

Outpatient initiation of the ketogenic diet in children with pharmacoresistant epilepsy: An effectiveness, safety and economic perspective.

Children with pharmacoresistant epilepsy usually receive ketogenic diet (KD) as an inpatient, which makes it an expensive treatment. To compare the effectiveness, safety, and costs of outpatient versus inpatient initiated KD. Retrospective observational non-inferiority study. Patients (1-18 years of age) who started KD either inpatient or outpatient. Effectiveness was defined as ≥50% seizure reduction. Safety was measured by the numbers of emergency visits and complications. Economic impact was analyzed by calculating total costs of treatment. Non-inferiority of outpatient initiation was tested using 95% confidence intervals of the differences in effectiveness and safety endpoints between groups with non-inferiority margins of 10%. Nonparametric bootstrap techniques were used to derive a 95% confidence interval for the mean difference in total costs between the groups. Hundred and five patients started KD in the period 2001 to 2017: 43 inpatient and 62 outpatient. At three months, the KD was effective in 61% of outpatients versus 63% of inpatients. The KD was considered safe in 36% of the outpatients, as compared to 29% in the inpatients. Outpatient initiation was shown to be non-inferior to inpatient initiation in terms of safety. Total health care costs of outpatient initiation were € 2901, as compared to € 8195 of inpatient initiation per patient (mean difference € 5294, 95% CI; -€ 7653 to -€ 2935). Our study suggests that outpatient KD initiation is no worse than inpatient initiation in terms of effectiveness and safety, while carrying lower health care costs.

Smart Operation of Electric Vehicles With Four-Quadrant Chargers Considering Uncertainties

Given the expected impact of electric vehicle (EV) charging on power grids, this paper presents a novel two-step approach for the smart operation of EVs with four-quadrant chargers in a primary distribution feeder, accounting for the uncertainties associated with EVs, and considering the perspectives of both the utility and the EV owners. In the first step of the proposed approach, the mean daily feeder peak demand and corresponding hourly feeder control schedules, such as taps and switched capacitor setpoints, considering the bidirectional active and reactive power transactions between EVs and the grid, are determined. A nonparametric bootstrap technique is used, in conjunction with a genetic algorithm-based optimization model, to account for EV uncertainties and discrete variables. In the second step, the maximum possible power that can be given to connected EVs at each node, while providing active and/or reactive power to maintain the peak demand value and corresponding feeder dispatch schedules defined in the first step, is computed every few minutes in a way which is fair to the EVs. The proposed approach is validated using the distribution feeder model of a real primary feeder in Ontario, Canada, considering significant EV penetration levels. The results show that the proposed approach could be implemented in practice to properly operate EVs, satisfying feeder, and peak demand constraints, which would be better than the business-as-usual practice or a popular heuristic method in terms of number of tap operations, system peak demand, and voltage regulation.