Confidence Intervals For Estimates Research Articles

Vestibular Function is Associated with Prefrontal and Sensorimotor Cortical Gray Matter Volumes in a Cross-Sectional Study of Healthy, Older Adults

Background The vestibular system is associated with alterations in the structure and function of the central nervous system. Yet, whether age-related vestibular loss is related to volume loss in the cerebral cortical areas that have been reported to receive vestibular input remains unknown. In this cross-sectional study of 117 healthy, older adults from the Baltimore Longitudinal Study of Aging, we examine the relationships between age-related vestibular functions and the gray matter volumes of the prefrontal cortex and its subregions and of the sensorimotor cortex—regions known to process vestibular information. Methods T1-weighted MRI scans were automatically segmented using MRICloud. Log-linear multiple regression was used to investigate the relationships between average regional volume and vestibular function, adjusting for age, sex, and intracranial volume. Permutation testing was used for hypothesis testing, and bootstrapping was used to estimate confidence intervals. Results We found that age-related changes in vestibular end-organ function are associated with differentially altered gray matter volumes in the prefrontal and sensorimotor cortices, with many findings persisting when considering left (or right) side only. Concomitant with age-related, global brain atrophy, lower canal and utricular function were associated with additional volume atrophy of the prefrontal cortex and middle frontal gyrus, respectively. Lower saccular and utricular function were associated with the preservation of the volumes of the sensorimotor cortex and the pole of the superior frontal gyrus, respectively, against age-related, global brain atrophy. Canal and utricular function were not associated with the volumes of the sensorimotor cortex, and saccular function was not associated with the relative volumes of the prefrontal cortex. Conclusion Together, these findings of relative volume preservation or additional atrophy suggest that vestibular function may play a role in the resilience to or magnification of global age effects on cerebral cortical structure.

Application of the Parametric Bootstrap Method for Confidence Interval Estimation and Statistical Analysis of PM2.5 in Bangkok

Research in epidemiology and health science indicates that exposure to particles with an aerodynamic diameter of less than 2.5 µm (PM2.5) causes harmful health consequences. Probability density functions (pdf) are utilized to analyze the distribution of pollutant data and study the occurrence of high-concentration occurrences. In this study, PM2.5 concentrations (in μg/m^3 ) were recorded daily from January 2011 to December 2022 at 12 air quality monitoring locations in Bangkok. The study utilized two-parameter distributions such as gamma, inverse Gaussian, lognormal, log-logistic, Weibull, and Pearson type V to identify the most suitable statistical distribution model for PM2.5 in Bangkok. The Anderson-Darling test result indicates that the inverse Gaussian and Pearson type V distributions are the most appropriate probability density functions for the daily average PM2.5 concentration at stations in Bangkok. The projected 98th percentile of daily PM2.5 levels at two locations is higher than the 24-hour threshold for daily PM2.5 concentrations in Thailand, posing significant health risks. Additionally, the two parametric bootstrap methods used to estimate confidence intervals for the median, namely percentile bootstrap and simple bootstrap, indicate that two stations have poor air quality for those with sensitive health conditions.

Development and initial testing of a brief, generic self-reported disability questionnaire: The Universal Disability Index.

Disability is an important multifaceted construct. A brief, generic self-reported disability questionnaire that promises a broader and more comparable measure of disability than disease-specific instruments does not currently exist. The aim of this study was to develop and evaluate such a questionnaire: the Universal Disability Index (UDI). An online survey was used to collect general population data. Data were randomly divided into training and validation subsets. The dimensionality and structure of eight UDI questionnaire items were evaluated using exploratory factor analysis (EFA, training subset) followed by confirmatory factor analysis (CFA, validation subset). To assess concurrent validity, the UDI summed score from the full dataset was compared to the Groningen Activity Restriction Scale (GARS) and the Graded Chronic Pain Scale (GCPS) disability scores. Internal consistency and discriminant validity were also assessed. Bootstrapping was used to evaluate model stability and generalisability. 403 participants enrolled; 364 completed at least one UDI item. Three single-factor versions of the UDI were assessed (8-item, 7-item, and 6-item). All versions performed well during EFA and CFA (182 cases assigned to each), but none met the RMSEA (Root Mean Square Error of Approximation) criterion (≤ 0.08). All versions of the UDI had high internal consistency (Cronbach's α > 0.90), were strongly correlated (Pearson's r > 0.7) with both GARS and GCPS disability scores, indicating concurrent validity, and could accurately discriminate between upper and lower quartiles of these comparators. Confidence intervals of estimates were narrow, suggesting model stability and generalisability. A brief, generic self-reported disability questionnaire was found to be valid and to possess good psychometric properties. The UDI has a single factor structure and either a 6-item, 7-item or 8-item version can be used to measure disability. For brevity and parsimony, the 6-item UDI is recommended, but further testing of all versions is warranted.

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Constructing Confidence Intervals for BIFSG Disparity Estimates

This paper describes a method to construct confidence intervals for estimates of disparities in outcomes between racial/ethnic groups when race and ethnicity are estimated using Bayesian Improved First Name and Surname Geocoding (BIFSG). Standard errors produced by regression packages understate the uncertainty in these estimates because they fail to incorporate noise arising from estimating race and ethnicity. This can produce false confidence when assessing the direction and magnitude of disparities. Conversely, the bootstrapping approach introduced in this paper incorporates uncertainty arising from the use of BIFSG. However, this procedure does not correct for any statistical bias associated with using BIFSG.

The relationship between patient-reported and objective measures of physical function among cancer survivors receiving rehabilitation care: A correlation analysis.

Physical function is associated with cancer-related quality of life and survival. The correlation of patient-reported and objective measures of physical function has not been quantified previously in the cancer rehabilitation medicine setting. To correlate patient-reported physical function with objective measures of physical function. The secondary aim is to correlate fatigue and social participation levels with objective measures of physical function. Retrospective. Tertiary university cancer center. A total of 226 cancer survivors referred to an outpatient cancer rehabilitation medicine clinic. Not applicable. Correlation of Patient-Reported Outcomes Measurement Information System (PROMIS) Cancer Function Brief 3D Profile domains with objective physical function measured by the 30-second sit-to-stand and hand grip strength. Pearson correlation analysis revealed a moderate correlation between patient-reported physical function and the 30-second sit-to-stand test (r = 0.57; p <.001), and a weak correlation of patient-reported physical function and handgrip strength (r = 0.27; p <.001). Multivariable regression modeling controlling for age, gender, and receipt of systemic therapy demonstrated a significant association between patient-reported physical function and the 30-second sit-to-stand test (estimated confidence interval 0.76 [0.60, 0.92], p < .01), in addition to patient-reported function and handgrip strength (estimated 0.22 [0.10, 0.34], p < .01). Multiple myeloma but no other cancer type was inversely associated with lower physical function. Patient-reported physical function correlated moderately with the 30-second sit-to-stand test, whereas hand grip strength demonstrated a weak correlation. The 30-second sit-to-stand test is a suitable substitute for patient-reported physical function. Further work evaluating how measures relate to each other across cancer populations is needed before recommending a standardized set of outcome measures.

Front-line and second-line treatment for mantle cell lymphoma in clinical practice: A multicenter retrospective analysis.

There are few reports of clinical practice treatment patterns and efficacy in mantle cell lymphoma (MCL). We retrospectively studied a large, multicenter, cohort of patients with MCL diagnosed between 2000 and 2020 in eight institutions. 536 patients were registered (73% male, median of 70 years). Front-line treatment was based on high-dose cytarabine, bendamustine, and anthracyclines in 42%, 12%, and 15%, respectively. The median PFS for all patients was 45 months; 68, 34, and 30 months for those who received high-dose cytarabine-based, bendamustine-based and anthracycline-based therapy. 204 patients received second-line. Bendamustine-based treatment was the most common second-line regimen (36% of patients). The median second-line PFS (sPFS) for the entire cohort was 14 months; 19, 24, and 31 for bendamustine-, platinum-, and high-dose cytarabine-based regimens, with broad confidence intervals for these latter estimates. Patients treated with cytarabine-based therapies in the front-line and those with front-line PFS longer than 24 months had a substantially superior sPFS. Front-line treatment in this cohort of MCL was as expected and with a median PFS of over 3.5 years. Second-line treatment strategies were heterogeneous and the median second-line PFS was little over 1 year.

Radiology Imaging Adds Time and Diagnostic Uncertainty when Point of Care Ultrasound Demonstrates Cholecystitis.

Point of care ultrasound (POCUS) is specific for acute cholecystitis (AC), but surgeons request radiology imaging (RI) prior to admitting patients with POCUS-diagnosed AC. We sought to determine the test characteristics of POCUS for AC when performed and billed by credentialed emergency physicians (EPs), the accuracy rate of RI when performed after POCUS, and the time added when RI is requested after POCUS demonstrates AC. We performed a dual-site retrospective cohort study of admitted adult ED patients who had received biliary POCUS from November 1, 2020 to April 30, 2022. Patients with previously diagnosed AC, liver failure, ascites, hepatobiliary cancer, or cholecystectomy were excluded. Descriptive statistics and 95% confidence intervals for point estimates were calculated. Medians were compared using a Wilcoxon signed-rank test. Test characteristics of POCUS for AC were calculated using inpatient intervention for AC as the reference standard. Of 473 screened patients, 143 were included for analysis: 80 (56%) had AC according to our reference standard. POCUS was positive for AC in 46 patients: 44 true positives and two false positives, yielding a positive likelihood ratio of 17.3 (95%CI 4.4-69.0) for AC. The accuracy rate of RI after positive POCUS for AC was 39.0%. Median time from ED arrival to POCUS and ED arrival to RI were 115 (IQR 64, 207) and 313.5 (IQR 224, 541) minutes, respectively; p < 0.01. RI after positive POCUS performed by credentialed EPs takes additional time and may increase diagnostic uncertainty.

The effectiveness of abstinence-based and harm reduction-based interventions in reducing problematic substance use in adults who are experiencing homelessness in high income countries: A systematic review and meta-analysis: A systematic review.

The effectiveness of abstinence-based and harm reduction-based interventions in reducing problematic substance use in adults who are experiencing homelessness in high income countries: A systematic review and meta-analysis: A systematic review.

‘Not finding causal effect’ is not ‘finding no causal effect’ of school closure on COVID-19

In a paper recently published in Nature Medicine, Fukumoto et al. tried to assess the government-led school closure policy during the early phase of the COVID-19 pandemic in Japan. They compared the reported incidence rates between municipalities that had and had not implemented school closure in selected periods from March–May 2020, where they matched for various potential confounders, and claimed that there was no causal effect on the incidence rates of COVID-19. However, the effective sample size (ESS) of their dataset had been substantially reduced in the process of matching due to imbalanced covariates between the treatment (i.e. with closure) and control (without closure) municipalities, which led to the wide uncertainty in the estimates. Despite the study title starting with “No causal effect of school closures”, their results are insufficient to exclude the possibility of a strong mitigating effect of school closure on incidence of COVID-19. In this replication/reanalysis study, we showed that the confidence intervals of the effect estimates from Fukumoto et al. included a 100% relative reduction in COVID-19 incidence. Simulations of a hypothetical 50% or 80% mitigating effect hardly yielded statistical significance with the same study design and sample size. We also showed that matching of variables that had large influence on propensity scores (e.g. prefecture dummy variables) may have been incomplete.

Quantifying uncertainty in carbon emission estimation: Metrics and methodologies

Carbon emissions are a significant driver of global climate change in today's world. A central concern in discussing carbon emissions is the level of uncertainty associated with them. This study aims to assess the feasibility of using the Mean Squared Error (MSE) as a point estimation measure and confidence interval (CI) as an interval estimation measure to quantify the uncertainty surrounding carbon emissions. To achieve this goal, the bootstrap and Markov Chain Monte Carlo (MCMC) sampling methods were used, utilizing both classical and Bayesian inference methods to uncover the true parameter value.In the context of Bayesian inference, a 2-chain MCMC method proved to be the optimal choice for generating posterior distributions and accurately estimating the true parameter of the distribution θ. The analysis also shows that, while a CI is valuable as an evaluative measure, it does not inherently provide a quantified form of uncertainty and should not be used for quantitative uncertainty assessment. Instead, the Relative Standard Error (RSE) emerges as a promising quantitative measure for capturing the uncertainty of θ, while the percentage uncertainty offers a qualitative perspective. These combined measures provide a comprehensive toolkit for computing and communicating the uncertainty in carbon emission and emission factor values. This reinforces a move towards incorporating transparent uncertainty metrics, ensuring that stakeholders have access to reliable and accurate carbon emission values.

The derivative-based approach to nonlinear mediation models: insights and applications

AbstractTraditional mediation analysis has been developed in the context of linear models, enabling the estimation of indirect effects through the product of regression coefficients. However, in the presence of nonlinearities, defining and estimating indirect effects becomes more challenging. While nonlinear mediation models are relatively easy to address in the counterfactual-based framework, very few generalizations to nonlinear associational settings have been proposed. One of the most intuitive is the derivative-based approach that, however, seems not to be widely spread among scholars. In this paper, we deepen such an approach to nonlinear mediation models, clarifying and proposing solutions to some issues which have not been addressed by the previous literature. Specifically, we discussed discrete exposures, binary mediators and extensions of this approach to more complex settings like the multilevel one. We also propose to estimate confidence intervals for the indirect effect within a Bayesian framework and compare its performance to that of other approaches in the literature through a simulation study. Finally, a real data application is presented.

Analysis of generalized type-II progressively hybrid Lindley-exponential data and its modeling in physics, engineering, and management

The life test is guaranteed to end at a predetermined time using a novel type-II generalized progressively hybrid censoring method that is investigated when the test participants’ lifetime model has a two-parameter Lindley-exponential (LndE) distribution. The LndE characteristics are estimated using Bayes and maximum likelihood inference techniques when the suggested censored data are present. By employing the normal approximation of each unknown quantity, the estimated confidence intervals are also constructed. Additionally, independent gamma density priors are used to generate the Bayesian estimators under symmetrical (squared error) loss. Since the likelihood function is formulated in a difficult manner, the Bayes estimators and their corresponding greatest posterior density intervals cannot be computed theoretically, but they may be evaluated using Markov-chain Monte Carlo algorithms. The most progressive design is then determined by applying four optimality criteria. Using Monte Carlo comparisons, the efficiency of the proposed estimating processes is evaluated, and some suggestions are made. In the end, the usefulness of the suggested methods that may be applied in practical situations is demonstrated by analyzing three distinct applications: physics, engineering, and management. When the experimenter’s primary concern is the test’s duration, the numerical findings revealed that the sampling strategy is adaptable and incredibly successful in finishing the experiment in a variety of realistic scenarios.

A systematic review and meta-analysis of the association between air pollutants and the incidence of tuberculosis

ObjectiveTo investigate the association between air pollutants and the incidence of tuberculosis (TB) through a systematic review and meta-analysis, and to provide directions for future research and prevention of TB. MethodsA search was conducted for all literature related to the incidence of TB and air pollution in the database. We screened the retrieved articles and proceeded statistical analyses using random effects models to investigate the relationships between five air pollutants (PM2.5, PM10, SO2, NO2 and O3) and the incidence of TB. ResultsThe initial search identified 100 pieces of literature and 9 studies met the screening criteria after the screening. The single-day lagged risk ratio (RR) and 95% Confidence Intervals (CIs) for the combined effects estimates are as follows: PM2.5: 1.059 (0.966, 1.160); PM10: 1.000 (0.996, 1.004); SO2: 0.980 (0.954, 1.007); NO2: 1.011 (0.994, 1.027); O3: 0.994 (0.980,1.008). The cumulative lagged results for these five pollutants are listed like this: PM2.5: 1.095 (0.983, 1.219); PM10: 1.035 (1.006, 1.066); SO2: 0.964 (0.830, 1.121); NO2: 1.037 (1.010, 1.065); O3: 0.982 (0.954, 1.010). ConclusionThe single-day lag effects of PM2.5, PM10, SO2, NO2, and O3 are not statistically significantly relevant for the occurrence of TB. However, the cumulative lag results show that both PM10 and NO2 contribute to the prevalence of TB, while the statistical relationship between the cumulative lag effects of PM2.5, SO2, and O3 and the onset of TB remains unknown.

Non-inferiority trials

Superiority trials are designed to test the hypothesis that a given diagnostic or therapeutic strategy is better than (i.e. "superior to") placebo or an active control. Conversely, non-inferiority trials test the hypothesis that a newer (i.e. alternative) strategy is not "unacceptably worse" than a control (i.e. "traditional", or "older") strategy. Non-inferiority trials are increasingly conducted in clinical medicine more often when a "newer" strategy is supposed to offer a relevant advantage in terms other than clinical efficacy (i.e. better tolerability, less cost, simpler regimen, etc.) versus a "gold standard" traditional strategy. The principle underlying non-inferiority trials is that the above advantage justifies the preferential use of the newer strategy in the clinical practice even if the clinical efficacy of the "new" appears to be a bit worse than that of the "old", albeit not unacceptably worse (i.e. not beyond a pre-specified value). The demonstration of non-inferiority requires that the confidence interval of the point estimate (e.g. the hazard ratio) does not cross a pre-specified limit. The definition of such pre-specified limit, the so called "non-inferiority margin", is a pivotal point when planning non-inferiority trials. It denotes the maximally tolerated worse effect of the alternative strategy, compared with the traditional one, required to conclude that an alternative strategy is non-inferior to the traditional "gold standard". The non-inferiority margin is derived from previous trials evaluating the efficacy of the traditional strategy vs placebo. We reviewed the principles and the practical aspects in the design and conduct of non-inferiority trials.

DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling

Many applications use computer vision to detect and count objects in massive image collections. However, automated methods may fail to deliver accurate counts, especially when the task is very difficult or requires a fast response time. For example, during disaster response, aid organizations aim to quickly count damaged buildings in satellite images to plan relief missions, but pre-trained building and damage detectors often perform poorly due to domain shifts. In such cases, there is a need for human-in-the-loop approaches to accurately count with minimal human effort. We propose DISCount -- a detector-based importance sampling framework for counting in large image collections. DISCount uses an imperfect detector and human screening to estimate low-variance unbiased counts. We propose techniques for counting over multiple spatial or temporal regions using a small amount of screening and estimate confidence intervals. This enables end-users to stop screening when estimates are sufficiently accurate, which is often the goal in real-world applications. We demonstrate our method with two applications: counting birds in radar imagery to understand responses to climate change, and counting damaged buildings in satellite imagery for damage assessment in regions struck by a natural disaster. On the technical side we develop variance reduction techniques based on control variates and prove the (conditional) unbiasedness of the estimators. DISCount leads to a 9-12x reduction in the labeling costs to obtain the same error rates compared to naive screening for tasks we consider, and surpasses alternative covariate-based screening approaches.

Temporal variability is a major source of uncertainty in soil carbon measurements

AbstractMonitoring changes in soil organic carbon over time is important to many agricultural and environmental goals. Despite decades of measurements, temporal variability in soil carbon measurements has not been studied extensively. In this report, we examine five sets of monthly samples extending up to 3 years each that were collected from field experiments at four locations representative of dryland farming in the Pacific Northwest. The variance from month‐to‐month was 15%–32% of the random error, averaging 20%. This was often greater than the variance between replicate experimental units (2%–42%, averaging 17%). At certain sites, sequential samples were found to be temporally autocorrelated, but no consistent trend patterned on seasonal factors like precipitation was found. This suggests that a single point‐in‐time sample can deviate substantially from the long‐term average soil carbon at the site. We illustrate this problem with the results of repeated soil samples taken from 12 commercial farm fields. We recommend that confidence intervals for soil organic carbon estimates should include variance based on a large population of samples rather than from a single sample set at one timepoint.

A Note on Simultaneous Confidence Intervals for Direct, Indirect and Synthetic Estimators

Direct, indirect and synthetic estimators have a long history in official statistics. While model-based or model-assisted approaches have become very popular, direct and indirect estimators remain the predominant standard and are therefore important tools in practice. This is mainly due to their simplicity, including low data requirements, assumptions and straightforward inference. With the increasing use of domain estimates in policy, the demands on these tools have also increased. Today, they are frequently used for comparative statistics. This requires appropriate tools for simultaneous inference. We study devices for constructing simultaneous confidence intervals and show that simple tools like the Bonferroni correction can easily fail. In contrast, uniform inference based on max-type statistics in combination with bootstrap methods, appropriate for finite populations, work reasonably well. We illustrate our methods with frequently applied estimators of totals and means.

Coping and psychological well-being among persons with relapsing-remitting multiple sclerosis: The role of perceived self-efficacy

ObjectiveTo explore self-efficacy as a possible mediator in the relationship between adaptive coping and psychological well-being (PWB) in persons with relapsing-remitting multiple sclerosis (RRMS). Method174 persons with RRMS (91% Female) completed an online survey consisting of psychosocial questionnaires. The COPE Inventory was used to create an adaptive coping composite that accounts for the relative contributions of both active and avoidant coping. Self-efficacy was measured using the General Self-Efficacy Scale. The Ryff Psychological Well-Being Scales captured several domains of PWB: Positive relations with others, autonomy, purpose in life, environmental mastery, self-acceptance, and personal growth. The SPSS PROCESS macro was used to test whether self-efficacy mediated the association between adaptive coping and each PWB subscale, using 5,000 bootstrap samples to estimate confidence intervals. ResultsIn each mediational model, adaptive coping was significantly associated with each PWB subscale and self-efficacy (all p’s ≤ 0.001). Self-efficacy was also significantly associated with each PWB subscale (p ≤ 0.001). After controlling for self-efficacy, the associations between adaptive coping and positive relations with others (indirect effect = 0.021, 95% CI [0.011, 0.033]) and autonomy (indirect effect = 0.019, 95% CI [0.011, 0.028]) became non-significant. The separate associations between adaptive coping and purpose in life (p = .004), self-acceptance (p = .026), and personal growth (p < .001) remained significant, despite controlling for self-efficacy. ConclusionsSelf-efficacy fully mediated the relationships between adaptive coping and positive relations with others and autonomy in our sample of persons with RRMS. Interventions aimed at increasing adaptive coping skills and self-efficacy may improve relations with others and autonomy among persons with MS.

Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications.