Bootstrap Confidence Intervals Research Articles

On the study of the stress-strength reliability in Weibull-$F$ Models

In this paper, the problem of inferencing on the stress-strength reliability under Weibull-$F$ Models when the stress and strength systems belong to the different families of distributions from the Weibull-$F$ Model is considered. Some stochastic comparisons between the survival distribution functions of this model are obtained. Also, the asymptotic and several bootstrap confidence intervals of stress-strength reliability are studied. The efficiency of asymptotic and bootstrap confidence intervals are analyzed by simulation. The numerical example based on real-life data is displayed as an illustration.

The moderating role of perceived brand globalness on the effects of consumers’ attitude during periods of political controversies: three interconnected studies following marketing analytics’ best practices

AbstractThe purpose of this research is to test the moderating role of perceived brand globalness (PBG) on consumer unfavourable attitudes and buying intention towards brands associated with a conflicting political belief. Three studies were conducted. Study 1 (N = 228) examines the effect of a brand being associated with a conflicting political belief on attitude and buying intention. Study 2 (N = 212) tests the moderating role of PBG among these relationships. Study 3 (N = 126) tests the moderating role of PBG for a service brand and adds the mediating role of self-congruity. We followed marketing analytics best practices to examine causal effects of manipulated stimuli. We also examined the psychometric characteristics of the data-collection tool and tested direct relationships between variables. Additionally, we used percentile bootstrap confidence intervals, with PROCESS-Macro, to test mediating effects in the theoretical model. The studies include moderating analyses and analysis of variance. The results show that the association of a brand with a conflicting political belief negatively influences perceived self-congruity, which leads to an unfavourable attitude and, in turn, to a lower buying intention. Presenting a brand as global mitigates such unfavourable effects. The moderating effect of PBG is confirmed for a tangible product and for a service brand.

Linear mixed models and latent growth curve models for group comparison studies contaminated by outliers.

The linear mixed model (LMM) and latent growth model (LGM) are frequently applied to within-subject two-group comparison studies to investigate group differences in the time effect, supposedly due to differential group treatments. Yet, research about LMM and LGM in the presence of outliers (defined as observations with a very low probability of occurrence if assumed from a given distribution) is scarce. Moreover, when such research exists, it focuses on estimation properties (bias and efficiency), neglecting inferential characteristics (e.g., power and type-I error). We study power and type-I error rates of Wald-type and bootstrap confidence intervals (CIs), as well as coverage and length of CIs and mean absolute error (MAE) of estimates, associated with classical and robust estimations of LMM and LGM, applied to a within-subject two-group comparison design. We conduct a Monte Carlo simulation experiment to compare CIs and MAEs under different conditions: data (a) without contamination, (b) contaminated by within-subject outliers, (c) contaminated by between-subject outliers, and (d) both contaminated by within- and between-subject outliers. Results show that without contamination, methods perform similarly, except CIs based on S, a robust LMM estimator, which are slightly less close to nominal values in their coverage. However, in the presence of both within- and between-subject outliers, CIs based on robust estimators, especially S, performed better than those of classical methods. In particular, the percentile CI with the wild bootstrap applied to the robust LMM estimators outperformed all other methods, especially with between-subject outliers, when we found the classical Wald-type CI based on the t statistic with Satterthwaite approximation for LMM to be highly misleading. We provide R code to compute all methods presented here. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A study of the interactive mediating effect of ADHD and NSSI caused by co-disease mechanisms in males and females.

Non-suicidal self-injury (NSSI), of which the predisposing factors are complex and diverse, profoundly affects the physical and mental health of young people. Therefore, this work established an NSSI intermediary network model considering the interaction of multiple factors. A mediating effect between attention-deficit/hyperactivity disorder (ADHD) and NSSI, considering the influence of comorbidities, such as depression, anxiety, and impulsive personality, was proposed based on sex differences. A total of 2,689 middle school students in Ningbo City, Zhejiang Province, China, were randomly sampled and participated in this study. Data regarding their demographic characteristics, attention deficit, hyperactivity/impulsivity, NSSI, anxiety, depression, internet addiction, and other comorbid symptoms were collected and analyzed. After initially screening the data, variables were assessed for significance using a single-factor inter-group difference analytic method, and a binary logistic regression analysis was performed. The intermediary effect of factors influencing NSSI in males and females was also analyzed. The overall NSSI rate was 15.16%. The results showed that the impact of individual impulsivity characteristics (impulsiveness, the ADHD with hyperactivity/impulsivity subtype) on NSSI behavior was not significant (regression results, P>0.05). The degree of association between ADHD with attention deficit and ADHD with comprehension deficit subtypes, and other comorbid symptoms (depression, anxiety, and internet addiction disorder) and NSSI, with odds ratios (ORs) of 7.6/6.42/436.68/3.82/1.86, and 95% bootstrap confidence intervals (CIs) of 4.64, 12.87/3.46, 12.67/137.42, 2659.13/2.32, 6.37/1.31, 2.82, respectively. The results also showed significant effects of ADHD subtypes on comorbid symptoms and the path effects of NSSI (P<0.01). Among them, the mediating effect was the strongest when anxiety was the mediating variable, and the mediating effect of girls was higher than that of boys. The results of this work demonstrated the influence of ADHD symptoms on NSSI behavior. Among patients with ADHD, patients with subtypes with obvious attention deficit characteristics were more likely to exhibit NSSI behavior, whereas the hyperactive impulse subtype had no direct impact on NSSI. We conclude that adolescent impulsivity may not be directly related to NSSI behavior and that impulsive characteristics jointly affect NSSI behavior through a series of NSSI comorbid symptoms. Notably, the probability of symptom onset and the degree of comorbidity was significantly higher in girls than in boys of the same age, and girls were more prone to NSSI behavior. These findings provide effective theoretical support for the prevention and treatment of adolescent NSSI behavior.

The simultaneous presence of demoralization, apathy, and depression has a detrimental impact on both cognitive function and motor symptoms in Parkinson's disease patients.

Parkinson's disease (PD) is marked not only by motor symptoms but also by neuropsychiatric manifestations, including demoralization, apathy, and depression. Understanding the clinical distribution and characteristics of these co-occurring symptoms is crucial for improving quality of life of PD patients. This study enrolled 195 Chinese PD patients from Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine. The study involved analyzing the clinical characteristics related to the simultaneous presence of demoralization, apathy, and depression in PD patients. Linear regression was employed to elucidate the linear trend between the quantity of negative neuropsychiatric symptoms and cognitive function, as well as motor symptoms and motor complications. SPSS mediation models were utilized to investigate whether the severity of cognitive function mediated the connection between multiple negative neuropsychiatric symptoms and motor symptoms. Among PD patients, a notable 57.5% experience the presence of multiple concurrent negative neuropsychiatric symptoms. Our investigation unveiled a correlation where patients with more negative neuropsychiatric symptoms displayed heightened cognitive impairment (P=0.048) and more severe motor symptoms (P=0.024), following a linear trend with increasing symptom numbers. Additionally, cognitive impairment played a partial mediating role in the impact of multiple negative neuropsychiatric symptoms on motor symptoms (β=0.747; 95% bootstrap confidence interval: 0.195 to 1.532). The co-occurrence of these negative neuropsychiatric symptoms has the potential to worsen cognitive function and motor symptoms in PD patients. Moreover, cognitive impairment was identified as playing a partial mediating role in the relationship between multiple negative neuropsychiatric symptoms and motor symptoms.

MMPI-2-RF validity scales add utility for predicting treatment engagement during partial psychiatric hospitalizations.

Partial psychiatric hospitalizations are resource-intensive clinical services designed to stabilize patients in the short term, prevent inpatient hospitalizations, and encourage long-term recovery. Typically, providers base their referral decisions on categorical diagnoses and subjective impressions of patient distress without closely considering the evidence for reporting biases. The present study followed veterans (n = 430) participating in partial psychiatric hospitalization services. We evaluated the extent to which clinical diagnoses at intake predicted treatment variables and changes in later mental health care utilization. Using hierarchical linear regressions with bootstrap confidence intervals, Minnesota Multiphasic Personality Inventory-2-Restructured Form content-based validity scales demonstrated incremental utility for predicting patient outcomes beyond intake diagnoses. Elevated Fp-r ("Infrequent Psychopathology Responses") scores independently predicted an increased number of times arriving late for partial hospitalization programming, self-report of worse current functioning at intake, and a relative increase in mental health care encounters in the 12 months following discharge. Low K-r ("Adjustment Validity") scores independently predicted self-report of worse current functioning at both intake and later discharge from partial hospitalization. Thus, indicators of severe psychopathology overreporting as well as the unlikely disavowal of emotional adjustment (i.e., high Fp-r, low K-r) predicted engagement with health care services and self-presentations of symptoms over and above the diagnostic impressions from referring providers. We discuss how indicators of content-based invalid responding on the Minnesota Multiphasic Personality Inventory-2-Restructured Form have real-world value for understanding patient behavior and shaping clinical interventions among vulnerable populations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Statistical inference of inverted Nadarajah–Haghighi distribution under type-II generalized hybrid censoring competing risks data

Tahir et al. (J Stat Comput Simul 88(14):2775–2798, 2018) introduced the inverse Nadarajah–Haghighi distribution (INHD) and demonstrated its ability to model positive real data sets with decreasing and upside-down bathtub hazard rate shapes. This article focuses on the inference of unknown parameters using a generalized Type-II hybrid censoring scheme (GT-II HCS) for the INHD in the presence of competing risks. The maximum likelihood (ML) and Bayes approaches are used to estimate the model parameters. Based on the squared error loss function, we compute Bayes estimates using Markov Chain Monte Carlo (MCMC) by applying Metropolis-Hasting (M-H) algorithm. Furthermore, the asymptotic confidence intervals, bootstrap confidence intervals (BCIs) and the highest posterior density (HPD) credible intervals are constructed. Using real data sets and simulation studies, we examined the introduced methods of inference with different sample sizes.

Does Role Identity Mediate the Influence of Motivational Regulations on Physical Activity Behavior Among People 55Years or Older?

The objective of this study was to estimate the extent to which motivational regulations influence physical activity behavior through role identity among people 55years or older. Participants (N = 409; Mage = 66.29years [SD = 7.06]) completed online questionnaires to measure motivational regulations, role identity, and the frequency of physical activity in a typical week and in the past month. Mediation analysis using ordinary least squares path analysis revealed that autonomous forms of motivational regulation (positively) and controlled forms of motivational regulation (negatively) influenced role identity, which then positively influenced physical activity behavior. Bootstrap confidence intervals (95%) for the indirect effects (a × b) based on 5,000 bootstrap samples were entirely above or below zero. These findings point to future experimental evaluations of interventions aiming at both increasing and decreasing autonomous and controlled motivational regulations, respectively, to promote physical activity behavior through role identity.

Relationship Between Electronic Health Literacy and Self-Management in People With Type 2 Diabetes Using a Structural Equation Modeling Approach.

Electronic health (eHealth) literacy is a relatively new concept used to determine health outcomes. However, it is not well known how eHealth literacy relates to health outcomes such as diabetes self-management. This study was designed to examine the relationships among eHealth literacy, self-efficacy, social support, and self-management in people with Type 2 diabetes. A cross-sectional design was used to examine secondary data from a field survey of people with Type 2 diabetes recruited from outpatient clinics from August to December 2021 ( N = 453). A structural equation model was used that first analyzed the measurement model using confirmatory factor analysis and then tested the hypothesized structural model to estimate the expected relationships among the study variables. The significance of the statistical estimates for the model was assessed based on the 95% bias-corrected bootstrap confidence interval from 5,000 bootstrap resamples. Significant, indirect relationships were found between eHealth literacy and self-management via self-efficacy (β = 0.26, B = 0.17, 95% CI [0.10, 0.24]) and via social support and, in turn, self-efficacy (β = 0.08, B = 0.05, 95% CI [0.04, 0.08]). eHealth literacy, social support, and self-efficacy together explained 58.1% of the variance in self-management. This study provides new evidence regarding how eHealth literacy relates to self-management in people with Type 2 diabetes via two indirect pathways, including self-efficacy alone and social support and self-efficacy in series. An eHealth literacy program for self-management should be developed in clinical practice that includes strategies for inducing synergistic effects from self-efficacy and social support on self-management in people with Type 2 diabetes.

RATE-ADAPTIVE BOOTSTRAP FOR POSSIBLY MISSPECIFIED GMM

We consider inference for possibly misspecified GMM models based on possibly nonsmooth moment conditions. While it is well known that misspecified GMM estimators with smooth moments remain $\sqrt {n}$ consistent and asymptotically normal, globally misspecified nonsmooth GMM estimators are $n^{1/3}$ consistent when either the weighting matrix is fixed or when the weighting matrix is estimated at the $n^{1/3}$ rate or faster. Because the estimator’s nonstandard asymptotic distribution cannot be consistently estimated using the standard bootstrap, we propose an alternative rate-adaptive bootstrap procedure that consistently estimates the asymptotic distribution regardless of whether the GMM estimator is smooth or nonsmooth, correctly or incorrectly specified. Monte Carlo simulations for the smooth and nonsmooth cases confirm that our rate-adaptive bootstrap confidence intervals exhibit empirical coverage close to the nominal level.

Evidence of "Repeated Admission Bias" Among Those Who Use Injection Drugs Across 2 Decades of US Treatment Admissions: 2000-2020.

Encounter-based datasets like the Treatment Episode Dataset-Admissions (TEDS-A) are used for substance use-related research. Although TEDS-A reports the number of previous treatment admissions, a limitation is this reflects encounters, not people. We sought to quantify the methodologic bias incorporated by using all encounters versus initial encounters and assess if this risk is evenly distributed across all routes of drug administration. TEDS-A 2000-2020 dataset with nonmissing primary substance data was used. Of the data, 3.17% were missing the usual administration route, and 11.9% were missing prior admission data. Prior admissions are documented as 0 through 4, then binned for 5 or greater (5+). Risk of admission bias was defined as odds ratio (OR RAB ): odds of total admissions relative to the odds of the first admission. Bootstrap confidence intervals were generated (5000 iterations) across administration routes and demographics; however, their widths were <0.0055 and not reported. There were 38,238,586 admissions over the 21 years, with 13,865,517 (41.2%) first admissions. Of all admissions, 15.7% indicated injection drug use (IDU); 26.3% of encounters reporting IDU were in the 5+ group. This resulted in an OR RAB of 1.77. White enrollees had an elevated OR RAB (1.05), whereas among Latinos, OR RAB was low (0.74). Using encounter-based datasets can introduce bias when including all admissions versus exclusively initial treatment episodes. This report is the first to quantify this bias and shows that individuals with IDU are at highest risk for returning to treatment, thereby over-representing this method of use when all encounters are used.

Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach

This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong.

Generalizability of predictive models for Clostridioides difficile infection, severity and recurrence at an urban safety-net hospital

IntroductionPredictive models for Clostridioides difficile (C. difficile) infection can identify high risk patients and aid clinicians in preventing infection. Issues of generalisability regarding current predictive models have been acknowledged but to our knowledge have never been quantified. MethodsThrough case-control sampling from an urban safety-net hospital, we created C. difficile infection, severity, and recurrence predictive models using multivariate logistic regression. Models were validated using 5-fold cross-validation and inverse probability weights (IPW) based on two different catchment area definitions were used to improve external validity. Akaike Information Criterion (AIC), area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity with bootstrapped confidence intervals were used to assess and compare model fit and performance. ResultsChanges in performance before and after weighting were small across all models; although, differences were more apparent after weighting the recurrence model (AUROCs of 0.78, 0.76, 0.71 for the unweighted and two weighted models, respectively). Overall, the infection model performed the best (AUROC=0.82 [95%CI: 0.78, 0.85]) followed by the recurrence model (AUROC=0.78 [95%CI: 0.69, 0.86]) and then the severity model (AUROC=0.70 [95%CI: 0.63, 0.78]. ConclusionsThe performance of our models after weighting did not change drastically suggesting that our model predicting C. difficile infection, severity, and recurrence may not be impacted by patient selection factors; however, other researchers may want to consider addressing these catchment forces using IPW.

Diet Quality Mediates the Relationship Between Chronic Stress and Inflammation in Patients With Metabolic Syndrome.

Chronic stress is associated with promotion of inflammation and development of metabolic syndrome, as well as deterioration of diet quality. Inflammation can be modified by changes in dietary intake. The aim of this study was to test the hypothesis that diet quality mediates the relationship of chronic stress with inflammation in patients with metabolic syndrome. Participants with metabolic syndrome (n = 73, 62 ± 12 years old, 71% female) completed questionnaires on chronic stress (Perceived Stress Scale-10) and diet quality (Healthy Eating Index-2020). The Perceived Stress Scale-10 was dichotomized. The Healthy Eating Index-2020 score was used as a continuous variable, and higher scores indicate better diet quality. Inflammation was assessed using plasma high-sensitivity C-reactive protein (log-transformed). We used PROCESS in SPSS to test the hypothesis. Patients in the higher stress group had lower Healthy Eating Index-2020 scores (worse diet quality) than those in the lower stress group (57 ± 13 vs 64 ± 10, P = .01). Diet quality mediated the relationship between chronic stress and inflammation (indirect effect, 0.211; 95% bootstrap confidence interval, 0.006-0.496). Higher stress was associated with lower diet quality (effect, -7.152; 95% confidence interval, -13.168 to -1.137) that was associated with increased inflammation (effect, -0.030; 95% confidence interval, -0.052 to -0.007). Our findings show the important role of diet quality in the relationship of chronic stress with inflammation in patients with metabolic syndrome. Healthcare providers should encourage patients with higher stress to improve diet quality, which can decrease inflammation.

Automatically discovering ordinary differential equations from data with sparse regression

Discovering nonlinear differential equations that describe system dynamics from empirical data is a fundamental challenge in contemporary science. While current methods can identify such equations, they often require extensive manual hyperparameter tuning, limiting their applicability. Here, we propose a methodology to identify dynamical laws by integrating denoising techniques to smooth the signal, sparse regression to identify the relevant parameters, and bootstrap confidence intervals to quantify the uncertainty of the estimates. We evaluate our method on well-known ordinary differential equations with an ensemble of random initial conditions, time series of increasing length, and varying signal-to-noise ratios. Our algorithm consistently identifies three-dimensional systems, given moderately-sized time series and high levels of signal quality relative to background noise. By accurately discovering dynamical systems automatically, our methodology has the potential to impact the understanding of complex systems, especially in fields where data are abundant, but developing mathematical models demands considerable effort.

The bivariate exponentiated additive Weibull distribution and its multivariate extension with applications

Recently, Abd EL-Baset and Ghazal (2020) introduced the exponentiated additive Weibull distribution, which is very useful in modeling the reliability analysis of a product, such as reliability of decision-making and cost–benefit analysis. The products inherently decay with usage and age, resulting in more extreme failures. Fixing a failed product may take longer warranty servicing time, indicating that failure and warranty servicing times correlate to bivariate distributions. This paper introduces a bivariate exponentiated additive Weibull distribution such that the marginals have exponentiated additive Weibull distributions. Statistical properties, parameter estimation using the maximum likelihood, and the bootstrap confidence interval are presented. We also generalize the results for the multivariate case. A simulation study has been executed to evaluate the performance of the parameter estimation via mean square error and absolute bias. Finally, we use three datasets failure and warranty servicing times, American football league, and the air quality to compare the proposed bivariate model to other models. The results revealed that the proposed bivariate model delivers a better fit than other models.

Chronic Hyponatremia and Brain Structure and Function Before and After Treatment

Rationale & ObjectiveHyponatremia is the most common electrolyte disorder and is associated with significant morbidity and mortality. This study investigated neurocognitive impairment, brain volume, and alterations in MRI-based measures of cerebral function in patients before and after treatment for hyponatremia. Study DesignProspective cohort study. Setting & ParticipantsPatients with presumed chronic hyponatremia without signs of hypo- or hypervolemia treated in the Emergency Department of a German tertiary care hospital. ExposureHyponatremia ([Na+] <125 mmol/L) before and after treatment leading to [Na+] >130 mmol/L. OutcomesStandardized neuropsychological testing (Mini-Mental-State examination [MMSE], DemTect, Trail-Making Test [TMT] A/B, Beck’s Depression Inventory, Timed Up and Go) and resting-state MRI were performed prior to and following treatment of hyponatremia to assess volumes of total brain, white and gray matter, as well as neuronal activity and its synchronization. Analytical ApproachChanges in outcomes after treatment for hyponatremia assessed using bootstrapped confidence intervals and Cohen’s d. Associations between parameters were assessed using correlation analyses. ResultsDuring a 3.7-year period, 26 patients were enrolled. Complete data were available for 21 patients. Mean [Na+] was 118.4 mmol/L before treatment and 135.5 mmol/L following treatment. Most measures of cognition improved significantly. Comparison of MRI studies showed a decrease in brain tissue volumes, neuronal activity, and synchronization across all gray matter after normalization of [Na+]. Volume effects were particularly prominent in the hippocampus. During hyponatremia, synchronization of neuronal activity was negatively correlated with [Na+] (r=-.836, [-.979, -.446]) and cognitive function (MMSE: r=-.523, [-.805, -.069]; DemTect: r=-.744, [-.951, -.385] and TMT-A: r=.692, [.255, .922]). LimitationsSmall sample size, insufficient quality of several MRI scans due to motion artifact. ConclusionsResolution of hyponatremia was associated with improved cognition and reductions in brain volumes and neuronal activity. Impaired cognition during hyponatremia is closely linked to increased neuronal activity rather than to tissue volumes. Furthermore, the hippocampus appears to be particularly susceptible to hyponatremia, exhibiting pronounced changes in tissue volume.

Deciphering the environmental values behind green purchasing: A mixed-method exploration through regression analysis and fuzzy set qualitative comparative analysis

The effectiveness of promoting green products in an effort to reduce carbon emissions relies heavily on understanding the mechanisms that influence green purchasing behavior. These mechanisms, however, remain both controversial and unclear, necessitating a thorough investigation to identify and decipher their underlying impacts. This paper presents a comprehensive dual-study analysis employing a mixed-methods approach to demystify this subject. Our first study tests the “net effect” mechanism of a theoretical model based on responses from 611 questionnaires from Chinese customers. Utilizing hierarchical regression analysis and Bootstrap confidence intervals, we examine the main effect, mediating effect, moderating effect, and moderated mediating effect to construct a more distinct “net effect” model than previously available. In the second study, the fuzzy set qualitative comparative analysis method (fs-QCA) is employed to examine the different antecedent variables affecting green purchasing behavior. The findings show no single determinant is essential; instead, green purchasing behavior follows three distinct patterns - namely, internal autogenous, environment-oriented, and individual profit-driven. Further analysis reveals that each pattern hinges on “green trust” to facilitate green purchasing behavior. The findings offer significant contributions to the understanding of the role environmental values play in green purchasing behavior. Moreover, it furnishes empirical insights that fostering consumers toward low-carbon consumption.

The Evaluation of Hospitalist Care for Patients with Aspiration Pneumonia Using Risk-adjusted Performance Indicators Developed from a Nationwide Inpatient Database.

Background High-quality evidence proving the superiority of hospitalist services is lacking. We developed risk-adjusted performance indicators from a multilevel prediction model using a nationwide inpatient database to evaluate hospitalist medical care for patients with aspiration pneumonia. Methods We extracted cases diagnosed with aspiration pneumonia between 2014 and 2021 from the Diagnosis Procedure Combination (DPC) database. Hospital-level risk-adjusted performance indicators were the observed-to-expected ratio of the following outcomes using a multilevel prediction model containing both patient- and hospital-level variables: death or transfer in poor condition within 30 days (poor outcome), in-hospital death within 30 days, and discharges within the 25th and 50th percentiles for length of stay defined by the DPC system. Using the predicted numbers of each outcome without random intercept as denominators of both indicators, the numerators of Indicator 1 were observed numbers of each outcome, while those in Indicator 2 were "smoothed" predicted numbers of outcomes estimated by the fitted model with random intercept. The ratio of the number of outcomes for each hospital to the mean number of outcomes among participating hospitals was used as a reference. We applied these indicators to Takatsuki General Hospital (TGH) as a working example. Results A total of 526,245 patients were analyzed. Compared with indicator 1, indicator 2 showed greater stability in the mean ratio and bootstrapping confidence interval (CI). Indicator 2 of poor outcome and discharges within the 25th percentile in 2017 at TGH were 1.110 (95% CI 0.784-1.375) and 1.458 (95% CI 1.272-1.597), respectively. Conclusions Utilizing a nationwide inpatient database, we developed risk-adjusted performance indicators using a multilevel prediction model to evaluate hospitalist medical care for patients with aspiration pneumonia. Given the reliable results shown in the working example, these indicators have potential benefits for the accurate evaluation of the quality of medical care.

Sequential estimation of inactivation parameters and bootstrap confidence intervals in unsteady-state conduction-heated foods

Sequential estimation of inactivation parameters and bootstrap confidence intervals in unsteady-state conduction-heated foods