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Sampling from large dataset is commonly used in the frequent patterns (FPs) mining. To tightly and theoretically guarantee the quality of the FPs obtained from samples, current methods theoretically stabilize the supports of all the patterns in random samples, despite only FPs do matter, so they always overestimate the sample size. We propose an algorithm called multiple sampling-based FPs mining (MSFP). The MSFP first generates the set of approximate frequent items (AFI), and uses the AFI to form the set of approximate FPs without supports ( AFP*), where it does not stabilize the value of any item's or pattern's support, but only stabilizes the relationship ≥ or < between the support and the minimum support, so the MSFP can use small samples to successively obtain the AFI and AFP*, and can successively prune the patterns not contained by the AFI and not in the AFP*. Then, the MSFP introduces the Bayesian statistics to only stabilize the values of supports of AFP*'s patterns. If a pattern's support in the original dataset is unknown, the MSFP regards it as random, and keeps updating its distribution by its approximations obtained from the samples taken in the progressive sampling, so the error probability can be bound better. Furthermore, to reduce the I/O processes in the progressive sampling, the MSFP stores a large enough random sample in memory in advance. The experiments show that the MSFP is reliable and efficient.

This paper considers linear panel data models with a grouped pattern of heterogeneity when the latent group membership structure and/or the values of slope coefficients change at a break point. We propose a least squares approach to jointly estimate the break point, group membership structure, and coefficients. The proposed estimators are consistent, and the asymptotic distribution of the coefficient estimators is identical to that under known break point and group structure even when the cross-sectional sample size is much larger than the length of time series. Monte Carlo simulations and an empirical example illustrate the use of the approach and associated inference.

This paper studies second-order properties of the many instruments robust t -ratios based on the limited information maximum likelihood and Fuller estimators for instrumental variable regression models with homoskedastic errors under the many instruments asymptotics, where the number of instruments may increase proportionally with the sample size n , and proposes second-order refinements to the t -ratios to improve the size and power properties. Based on asymptotic expansions of the null and non-null distributions of the t -ratios derived under the many instruments asymptotics, we show that the second-order terms of those expansions may have non-trivial impacts on the size as well as the power properties. Furthermore, we propose adjusted t -ratios whose approximation errors for the null rejection probabilities are of order O ( n − 1 ) in contrast to the ones for the unadjusted t -ratios of order O ( n − 1 / 2 ) , and show that these adjustments induce some desirable power properties in terms of the local maximinity. Although these results are derived under homoskedastic errors, we also establish a stochastic expansion for a heteroskedasticity robust t -ratio, and propose an analogous adjustment under slight deviations from homoskedasticity.

COVID-19 is a worldwide public health threat. Diagnosis by RT-PCR has been employed as the standard method to confirm viral infection. Sample pooling testing can optimize the resources by reducing the workload and reagents shortage, and be useful in laboratories and countries with limited resources. This study aims to evaluate SARS-CoV-2 detection by sample pooling testing in comparison with individual sample testing.We created 210 pools out of 245 samples, varying from 4 to 10 samples per pool, each containing a positive sample. We conducted detection of SARS-CoV-2-specific RdRp/E target sites.Pooling of three samples for SARS-CoV-2 detection might be an efficient strategy to perform without losing RT-PCR sensitivity.Considering the positivity rate in Dominican Republic and that larger sample pools have higher probabilities of obtaining false negative results, the optimal sample size to perform a pooling strategy shall be three samples.La COVID-19 es una amenaza de salud pública mundial. La RT-PCR es el método estándar para confirmar la infección. La estrategia de pruebas de muestras agrupadas puede reducir la carga de trabajo y la escasez de reactivos, y ser útil en países con escasos recursos. Evaluamos la detección del SARS-CoV-2 mediante esta estrategia en comparación con pruebas individuales.Creamos 210 grupos de 245 muestras, de 4 a 10 muestras por grupo, cada uno con una muestra positiva. Realizamos extracción de ARN y qRT-PCR para detectar la presencia de la diana RdRp/E.La combinación de hasta 3 muestras para la detección del SARS-CoV-2 podría ser una estrategia eficaz sin perder la sensibilidad.Considerando la tasa de positividad en República Dominicana y que los grupos con más muestras tienen mayor probabilidad de obtener resultados falsos negativos, el tamaño óptimo para realizar esta estrategia es de 3 muestras.

Background Course of Alcohol Dependence Syndrome (ADS) is studded with multiple relapses. Personality factors are implicated as one of the influencing factors in the course of this disorder. Keeping in view of scarcity of Indian data available, the study was planned with the aim to find Personality traits more commonly associated with ADS patients, and identify specific traits, associated with relapses of ADS. Method With sample size of 100 consecutive cases and 100 controls, socio-demographic data was collected. Alcohol Use Disorder Identification Test (AUDIT) and Severity of Alcohol Use Disorder Test (SAD-Q) were administered to each of these patient. Personality dimensions were assessed with NEO-five factor inventory (NEO-FFI) (Costa and McCrae), for both groups and further compared for differences in the dimensions in each of its subscale. Results The NEO scores, showed statistically significant difference with Cases having higher scores in Neuroticism and control group in Openness and Conscientiousness. Neuroticism linked to higher scores of AUDIT and SADQ and also associated with relapses. Other traits also showed statistically significant association which are discussed. Conclusion As new factors are being explored for effective management, routine personality profiling is easily accomplished and can give delightful insight into focused and designed management plan.

Newton-step approximations to pseudo maximum likelihood estimates of spatial autoregressive models with a large number of parameters are examined, in the sense that the parameter space grows slowly as a function of sample size. These have the same asymptotic efficiency properties as maximum likelihood under Gaussianity but are of closed form. Hence they are computationally simple and free from compactness assumptions, thereby avoiding two notorious pitfalls of implicitly defined estimates of large spatial autoregressions. For an initial least squares estimate, the Newton step can also lead to weaker regularity conditions for a central limit theorem than those extant in the literature. A simulation study demonstrates excellent finite sample gains from Newton iterations, especially in large multiparameter models for which grid search is costly. A small empirical illustration shows improvements in estimation precision with real data., 36 pages

This article investigates the predictability of dry bulk shipments' physical shipping costs while testing the predictive significance of derivative products. Accordingly, a comprehensive grid search procedure is needed to simulate combinations of model structures subject to a cross-validation process. In this regard, the intelligent model search engine (IMSE) is implemented as the search algorithm. The IMSE algorithm is selected due to its broad model space naturally consisting most of the traditional econometric model structures and advanced features, such as sample size optimization (also detects structural breaks), lag structure optimization (also reflects seasonality), among others. In contrast to the previous studies in the shipping market research, IMSE executes a relaxation of statistical significance criteria to target the out of sample predictive accuracy (instead of the goodness of fit). Sparsification is retained by utilizing a dropout procedure in line with the cross-validation process. The empirical results indicate the fact that predictive accuracy can be achieved or improved with a shorter sample period. More than half of the entire dataset has not been utilized in the final selections for most simulations. One-month and two-month maturity derivative contracts are tested for predictive features, while none of them is selected for the sparse model selections.

We study the finite sample behavior of Lasso-based inference methods such as post double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso not selecting relevant controls. This phenomenon can occur even when the coefficients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern high-dimensional OLS-based methods and provide practical guidance., Comment: Final author version, accepted at The Review of Economics and Statistics

Machine learning research, particularly in genomics, is often based on wide shaped datasets, i.e. datasets having a large number of features, but a small number of samples. Such configurations raise the possibility of chance influence (the increase of measured accuracy due to chance correlations) on the learning process and the evaluation results. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, we investigate the influence of chance on prediction and show its significant effects on wide shaped datasets. First, we empirically demonstrate how significant the influence of chance in such datasets is by showing that prediction models trained on thousands of randomly generated datasets can achieve high accuracy. This is the case even when using cross-validation. We then provide a formal analysis of chance influence and design formal chance influence estimators based on the dataset parameters, namely its sample size, the number of features, the number of classes and the class distribution. Finally, we provide an in-depth discussion of the formal analysis including applications of the findings and recommendations on chance influence mitigation.

This paper studies the optimality of matched-pair designs in randomized controlled trials (RCTs). Matched-pair designs are examples of stratified randomization, in which the researcher partitions a set of units into strata based on their observed covariates and assign a fraction of units in each stratum to treatment. A matched-pair design is such a procedure with two units per stratum. Despite the prevalence of stratified randomization in RCTs, implementations differ vastly. We provide an econometric framework in which, among all stratified randomization procedures, the optimal one in terms of the mean-squared error of the difference-in-means estimator is a matched-pair design that orders units according to a scalar function of their covariates and matches adjacent units. Our framework captures a leading motivation for stratifying in the sense that it shows that the proposed matched-pair design additionally minimizes the magnitude of the ex-post bias, i.e., the bias of the estimator conditional on realized treatment status. We then consider empirical counterparts to the optimal stratification using data from pilot experiments and provide two different procedures depending on whether the sample size of the pilot is large or small. For each procedure, we develop methods for testing the null hypothesis that the average treatment effect equals a prespecified value. Each test we provide is asymptotically exact in the sense that the limiting rejection probability under the null equals the nominal level. We run an experiment on the Amazon Mechanical Turk using one of the proposed procedures, replicating one of the treatment arms in Dellavigna and Pope (2018), and find the standard error decreases by 29%, so that only half of the sample size is required to attain the same standard error.

OBJECTIVES: Observational studies report mixed findings regarding the association between vitamin D and JIA incidence or activity, however such studies are susceptible to considerable bias. Since low vitamin D levels are common within the general population and easily corrected, there is potential public health benefit in identifying a causal association between vitamin D insufficiency and JIA incidence. To limit bias due to confounding and reverse causation we examined the causal effect of the major circulating form of vitamin D, 25-(OH)D, on JIA incidence using Mendelian randomization (MR).METHODS: In this two sample MR analysis we used summary level data from the largest and most recent genome wide association study (GWAS) of 25-(OH)D levels (sample size 443,734), alongside summary data from two JIA GWASs (sample sizes 15,872 and 12,501), all from European populations. To test and account for potential bias due to pleiotropy we employed multiple MR methods and sensitivity analyses.RESULTS: We found no evidence of a causal relationship between genetically predicted 25-(OH)D levels and JIA incidence (OR 1.00, 95% CI 0.76-1.33 per standard deviation increase in standardised natural-log transformed 25-(OH)D levels). This estimate was consistent across all methods tested. Additonally there was no evidence that genetically predicted JIA causally influences 25-(OH)D levels (-0.002 standard deviation change in standardised natural-log transformed 25-(OH)D levels per doubling odds in genetically predicted JIA, 95% CI -0.006-0.002).CONCLUSION: Given the lack of a causal relationship between 25-(OH)D levels and JIA, population level vitamin D supplementation is unlikely to reduce JIA incidence.

A dilemma faced by classification is that the data is not collected at the same frequency in some applications. We investigate the mixed frequency data in a new way and recognize them as a special style of multi-view data, in which each view data is collected at a different sampling frequency. This paper proposes a discriminative dictionary pair learning method constrained by ordinal locality for mixed frequency data classification (shorted by DPLOL-MF). This method integrates synthesis dictionary and analysis dictionary into a dictionary pair, which not only improves computational cost caused by the ${\ell_0}$ or ${\ell_1}$ -norm constraint, but also can deal with the sampling frequency inconsistency. The DPLOL-MF utilizes a synthesis dictionary to learn class-specified reconstruction information and employs an analysis dictionary to generate coding coefficients by analyzing samples. Particularly, the ordinal locality preserving term is leveraged to constrain the atoms of dictionaries pair to further facilitate the learned dictionary pair to be more discriminative. Besides, we design a specific classification scheme for the inconsistent sample size of mixed frequency data. This paper illustrates a novel idea to solve the classification task of mixed frequency data and the experimental results demonstrate the effectiveness of the proposed method.

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these learning methods in repeated samples is assessed using the variance of the posterior distribution of the parameters of interest given the data. This may be permissible when the sample size is large because, under the conditions of the Bernstein--von Mises theorem, the posterior variance agrees asymptotically with the frequentist variance. In finite samples, however, things are less clear. In this paper we explore this issue by first considering the frequentist properties (bias and variance) of the posterior mean in the important case of the normal location model, which consists of a single observation on a univariate Gaussian distribution with unknown mean and known variance. Based on these results, we derive new estimators of the frequentist bias and variance of the WALS estimator in finite samples. We then study the finite-sample performance of the proposed estimators by a Monte Carlo experiment with design derived from a real data application about the effect of abortion on crime rates.

A number of powerful demographic inference methods have been developed in recent years, with the goal of fitting rich evolutionary models to genetic data obtained from many populations. In this paper we investigate the statistical performance of these methods in the specific case where there is continuous migration between populations. Compared with earlier work, migration significantly complicates the theoretical analysis and requires new techniques. We employ the theories of phase-type distributions and concentration of measure in order to study the two-island and isolation-with-migration models, resulting in both upper and lower bounds on rates of convergence for parametric estimators in migration models. For the upper bounds, we consider inferring rates of coalescent and migration on the basis of directly observing pairwise coalescent times, and, more realistically, when (conditionally) Poisson-distributed mutations dropped on latent trees are observed. We complement these upper bounds with information-theoretic lower bounds which establish a limit, in terms of sample size, below which inference is effectively impossible.

How variable is the functionally-defined structure of early visual areas in human cortex and how much variability is shared between twins? Here we quantify individual differences in the best understood functionally-defined regions of cortex: V1, V2, V3. The Human Connectome Project 7T Retinotopy Dataset includes retinotopic measurements from 181 subjects, including many twins. We trained four “anatomists” to manually define V1-V3 using retinotopic features. These definitions were more accurate than automated anatomical templates and showed that surface areas for these maps varied more than three-fold across individuals. This three-fold variation was little changed when normalizing visual area size by the surface area of the entire cerebral cortex. In addition to varying in size, we find that visual areas vary in how they sample the visual field. Specifically, the cortical magnification function differed substantially among individuals, with the relative amount of cortex devoted to central vision varying by more than a factor of 2. To complement the variability analysis, we examined the similarity of visual area size and structure across twins. Whereas the twin sample sizes are too small to make precise heritability estimates (50 monozygotic pairs, 34 dizygotic pairs), they nonetheless reveal high correlations, consistent with strong effects of the combination of shared genes and environment on visual area size. Collectively, these results provide the most comprehensive account of individual variability in visual area structure to date, and provide a robust population benchmark against which new individuals and developmental and clinical populations can be compared.Significance StatementAreas V1, V2, and V3 are among the best studied functionally-defined regions in human cortex. Using the largest retinotopy dataset to date, we characterized the variability of these regions across individuals and the similarity between twin pairs. We find that the size of visual areas varies dramatically (up to 3.5x) across healthy young adults, far more than the variability of the cerebral cortex size as a whole. Much of this variability appears to arise from inherited factors, as we find very high correlations in visual area size between monozygotic twin-pairs, and lower but still substantial correlations between dizygotic twin pairs. These results provide the most comprehensive assessment of how functionally defined visual cortex varies across the population to date.

During the COVID-19 pandemic, there has been an increasing rollout of non-contact fever screening solutions to assist in curbing the spread of disease. This study begins by describing how screening for disease has historically been performed. It proposes four measurement characteristics of an ideal screening solution: non-contact, effective, rapid and low-cost measurements. Next, it reviews the existing literature on fever-screening using non-contact infrared thermometer (NCIT) devices as well as infrared thermography (IRT) devices, as these are two technologies which have experienced increasing use. For this review, 185 research papers were identified, 21 research studies were included after inclusion and exclusion criteria were applied. A total of 35 experiments were identified for analysis and their results tabulated. Of these studies, 66% are IRT and 34% are NCIT, with a median sample size of 430 subjects. 26 experiments involve febrile participants, with a median febrile percentage of 11.22 % of population. The reported sensitivity of febrile detection using NCIT varies from 3.7% to 97% and when using IRT it varies from 15% to 100%. Both indoor and outdoor studies are investigated, as well as those conducted in acute and non-acute settings. The results of this review show a clear lack of consensus on the effectiveness of these systems. Overall, these results indicate that sensitivity and specificity are reduced when using IRT and NCIT technologies compared to other thermometers used in medical practice. Their use should be carefully assessed based on the risks present in each particular measurement scenario. CCBY

We study the rapidly growing literature on the causal effects of financial education programs in a meta-analysis of 76 randomized experiments with a total sample size of over 160,000 individuals. Many of these experiments are published in top economics and finance journals. The evidence shows that financial education programs have, on average, positive causal treatment effects on financial knowledge and downstream financial behaviors. Treatment effects are economically meaningful in size, similar to those realized by educational interventions in other domains, and robust to accounting for publication bias in the literature. We also discuss the cost-effectiveness of financial education interventions.

High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For the convenience of theoretical analyses, classical methods usually assume independent observations and sub-Gaussian-tailed errors. However, neither of them hold in many real high-dimensional time-series data. Recently [Sun, Zhou, Fan, 2019, J. Amer. Stat. Assoc., in press] proposed Adaptive Huber Regression (AHR) to address the issue of heavy-tailed errors. They discover that the robustification parameter of the Huber loss should adapt to the sample size, the dimensionality, and the moments of the heavy-tailed errors. We progress in a vertical direction and justify AHR on dependent observations. Specifically, we consider an important dependence structure -- Markov dependence. Our results show that the Markov dependence impacts on the adaption of the robustification parameter and the estimation of regression coefficients in the way that the sample size should be discounted by a factor depending on the spectral gap of the underlying Markov chain.

BACKGROUND Donepezil had been recognized to have impact on sleep quality in demented patients. However, there was insufficient evidences about the actual effect of donepezil in the sleep architectures. Our meta-analysis aimed to evaluate the changes of sleep architectures related to donepezil use. METHODS Followed the PRISMA2020 and AMSTAR2 guidelines, electronic search had been performed on the databases of PubMed, Embase, ScienceDirect, ClinicalKey, Cochrane CENTRAL, ProQuest, Web of Science, and ClinicalTrials.gov. The outcome measurement was changes of sleep parameters detected by polysomnography. A random-effects meta-analysis was conducted. RESULTS Total twelve studies had been involved. The percentage of REM sleep would significantly increase after donepezil treatment (Hedges' g = 0.694, p

Context Athletes with anterior cruciate ligament (ACL) reconstruction (ACLR) exhibit increased cortical motor planning during simple sensorimotor tasks compared with healthy athletes serving as control groups. This may interfere with proper decision making during time-constrained movements, elevating the reinjury risk. Objective To compare cortical motor planning and biomechanical stability during jump landings between participants with ACLR and healthy individuals. Design Cross-sectional study. Setting Laboratory. Patients or Other Participants Ten men with ACLR (age = 28 ± 4 years, time after surgery = 63 ± 35 months) and 17 healthy men (age = 28 ± 4 years) completed 43 ± 4 preplanned (landing leg shown before takeoff) and 51 ± 5 unplanned (visual cue during flight) countermovement jumps with single-legged landings. Main Outcome Measure(s) Movement-related cortical potentials (MRCPs) and frontal θ frequency power before the jump were analyzed using electroencephalography. Movement-related cortical potentials were subdivided into 3 successive 0.5-second time periods (readiness potential [RP]-1, RP-2, and negative slope [NS]) relative to movement onset, with higher values indicating more motor planning. Theta power was calculated for the last 0.5 second before movement onset, with higher values demonstrating more focused attention. Biomechanical landing stability was measured via peak vertical ground reaction force, time to stabilization, and center of pressure. Results Both the ACLR and healthy groups evoked MRCPs at all 3 time periods. During the unplanned task analyzed using P values and Cohen d, the ACLR group exhibited slightly higher but not different MRCPs, achieving medium effect sizes (RP-1: P = .25, d = 0.44; RP-2: P = .20, d = 0.53; NS: P = .28, d = 0.47). The ACLR group also showed slightly higher θ power values that were not different during the preplanned (P = .18, d = 0.29) or unplanned (P = .42, d = 0.07) condition, achieving small effect sizes. The groups did not differ in their biomechanical outcomes (P values > .05). No condition × group interactions occurred (P values > .05). Conclusions Our jump-landing task evoked MRCPs. Although not different between groups, the observed effect sizes provided the first indication that men with ACLR might have consistently relied on more cortical motor planning associated with unplanned jump landings. Confirmatory studies with larger sample sizes are warranted.

Central obesity is influenced by many factors such as changes in age, gender, economic status, life habits including lack of physical activity, low fiber consumption, consumption of simple carbohydrates, consumption of fatty foods, and smoking behavior. Smoking behavior is thought to be a significant factor in forming central obesity in adult males. The purpose of this study was to determine the relationship between smoking behavior and central obesity in healthy adults. This study used an analytic observational design with a cross-sectional research design. The research was conducted in Suradadi Village, Suradadi District, Tegal Regency. Sampling was carried out by stratified sampling in November 2020 – January 2021. The sample size was 90 men aged 25-60 years. Data were collected by measuring the waist circumference of the respondents and recording other personal data such as age, occupation, and smoking status. Data processing is done descriptively and cross-tabulation. Testing the relationship between smoking behavior and obesity was carried out using the chi-square statistical test with the help of the SPSS version 22 application. The majority of respondents in this study had a smoking status of 68.9% and did not have central obesity 64.4%. The results of the test of the relationship between smoking status and central obesity got a p-value of 0.813. There is no significant relationship between smoking status and central obesity.

Chronic Kidney Disease patients undergoing hemodialysis can cause stress to the patient. The longer the patient suffers from Chronic Kidney Disease, the more the patient has a variety of experiences with stressors due to the disease. These experiences can be used as an anticipatory effort in dealing with stressors experienced by patients. The purpose of this was the relationship between the duration of hemodialysis and the coping mechanism of patients with chronic kidney disease undergoing hemodialysis. This study used descriptive quantitative, with a correlation design and a cross-sectional approach. The sample was 111 patients with chronic kidney disease undergoing hemodialysis. Sampling technique used the purposive sampling technique. The data collection instrument used a jalowiec coping scale questionnaire and length of underwent hemodialysis. The results showed that most respondents underwent hemodialysis in the category

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B , not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate.

Voting rules can be assessed from quite different perspectives: the axiomatic, the pragmatic, in terms of computational or conceptual simplicity, susceptibility to manipulation, and many others aspects. In this paper, we take the machine learning perspective and ask how prominent voting rules compare in terms of their learnability by a neural network. To address this question, we train the neural network to choosing Condorcet, Borda, and plurality winners, respectively. Remarkably, our statistical results show that, when trained on a limited (but still reasonably large) sample, the neural network mimics most closely the Borda rule, no matter on which rule it was previously trained. The main overall conclusion is that the necessary training sample size for a neural network varies significantly with the voting rule, and we rank a number of popular voting rules in terms of the sample size required.

The status of malnutrition and malnutrition is caused by various interrelated factors, including family behavior factors in providing food, breastfeeding and complementary feeding, and nutrition service efforts. This study aims to analyze the relationship between nutritionally conscious families (Kadarzi) and the nutritional status of children under five in the Work Area of the Puskesmas Pelambuan, Banjarmasin. This study used an analytic method with a cross sectional approach. The sampling technique used was proportional random sampling with a sample size of 82 parents of children under five. The measuring instrument used was a questionnaire and observation with weighing tools, the data analysis used the Spearman rank test. The location of this research was conducted in the Working Area of the Pelambuan Health Center, Banjarmasin City. Based on the results of the study, families who were aware of nutrition tended to have normal nutritional status totaling 27 (79.4%) and more than 7 (20.6%). Meanwhile, families who were not aware of nutrition tended to have malnutrition status, amounting to 31 people (64.6%) and malnutrition status 17 (35.4%). The spearman rank statistical test obtained p value 0.000 smaller than α = 0.05. There is a relationship between nutritionally conscious families (adarzi) with the nutritional status of children under five in the working area of Puskesmas Pelambuan, Banjarmasin City. Keywords : Nutritional awareness family, nutritional status, children

Objective There is an unmet need for therapies that target the underlying pathophysiology of osteoarthritis (OA). However, defining appropriate measures for clinical trials of such therapies is challenging. Our objective is to propose concept clinical endpoints that directly capture clinical benefit in this setting and evaluate the feasibility of their use. Methods This analysis used the multi-center, longitudinal, observational Osteoarthritis Initiative (OAI) database. OAI participants primarily had knee OA, with follow-up of up to nine years and assessments of joints, surgical interventions, performance outcomes, and patient-reported outcomes (PROs). We examined this dataset to identify existing outcome measures of direct clinical benefit. We evaluated the feasibility of conducting trials using these candidate endpoints by estimating incidence rates and resulting required sample sizes and study durations in time-to-event analyses. Results We identified candidate endpoints based on total knee replacement (TKR) and composite endpoints defined by TKR and conservative thresholds of PROs of pain and function. Using time to TKR as an endpoint, a study with an average follow-up time of three years requires approximately 3,000 to 18,000 subjects depending on effect size. Alternatively, a composite endpoint such as 'time to TKR or severe pain or severely impaired functioning', the required sample sizes ranged from approximately 2,000 to 11,000 for a three-year study. Conclusion The proposed concept endpoints can reliably and feasibly evaluate effectiveness of therapies for this unmet need. In particular, the composite endpoint approach can substantially reduce sample sizes (up to approximately 40%) compared to the use of TKR alone.

Conditional value at risk (CVaR) is a popular measure for quantifying portfolio risk. Sensitivity analysis of CVaR is common in risk management and gradient-based optimization algorithms. In this paper, we study the infinitesimal perturbation analysis estimator for CVaR sensitivity using randomized quasi-Monte Carlo (RQMC) simulation. RQMC has proved valuable in financial option pricing with a better rate of convergence compared to Monte Carlo sampling, but theoretical guarantees for this new application of RQMC shall be studied. To this end, we first prove that the RQMC-based estimator is strongly consistent under very mild conditions. Under some technical conditions, RQMC yields a mean error rate of O ( n − 1 / 2 − 1 / ( 4 d − 2 ) + ϵ ) for arbitrarily small ϵ > 0 , where d represents the dimension of RQMC points and n is the sample size. Some typical applications of CVaR sensitivity estimation are conducted to both show how the theoretical results can be applied, as well as to provide numerical results documenting the superiority of the RQMC estimator.

Contains fulltext : 235093.pdf (Publisher’s version ) (Open Access) Proper use of neuropsychological tests in Indonesia is hindered by a lack of properly adapted neurocognitive tests as well as an absence of normative data. In 2016, we started adapting ten of these tests for use in Indonesia and collected data from healthy participants in Java. Here we introduce and propose a system that will facilitate the proper usage and interpretation of test scores: an online platform and a dynamic database. Newly collected data (492 healthy adults) of the Indonesian version of the Boston Naming Test (I-BNT) were used to illustrate the usefulness of the two functions. Analysis of variances, post-hoc tests, and a simulation study revealed the effects of age and education on the I-BNT, indicating that it is imperative to fine-tune the reference group based on these demographic factors. Putative inadequate sample size issues for obtaining reliable normative scores were overcome by employing regression analyses and the prediction of normative scores. It can be concluded that a flexible online platform is available for the calculation of normative scores either based on the whole population, on fine-tuned reference groups, or on predicted scores. The dynamic database's growth will allow to obtain even more fine-tuned and more reliable reference data as well as more accurate predictions. Fine-tuned reference data are badly needed for the heterogenous Indonesian population. 10 p.

The number needed to treat (NNT) is a method of reporting outcomes from clinical trials.1 Treatment efficacy is determined by evaluating the outcome of one treatment relative to another treatment or to a control group when the only difference between the groups is the intervention of interest. The NNT can also be used to express the size of the outcome of one treatment relative to another. The NNT is expressed in terms designed to help decide whether the intervention might be valuable in clinical practice: the number of patients who need to be treated before a therapist can be sure that one patient improved who would not have improved without the intervention. For example, when comparing treatment X and treatment Y, an NNT score of 5 for treatment X indicates that, on average, after treating 5 patients, treatment X will have achieved one more positive outcome than if treatment Y had been used. The NNT does not tell the clinician which of those 5 patients will respond, only that 1 patient is likely to do so. The NNT was described in 1988 by Laupacis et al,2 and, although its use is becoming more popular, it is still not widely used. A search of MEDLINE back to 1991 using the search terms “number needed to treat” or “NNT” identified 121 citations reporting NNT information. Of these citations, only 3 concerned outcomes of physical therapy3–5 and, of these, only one5 described the use of NNT in a journal with content relevant to physical therapists. The NNT can be used to gauge the relative effectiveness of different treatments in restoring normal function or preventing future disability. If the treatment has a potentially harmful outcome (eg, stroke, headache, death from cervical manipulation), a similar calculation can be used …

The accuracy of smartphone-based positioning systems using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several distribution features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time for getting enough samples is quite long. Therefore, this paper proposes three new real-time NLOS/LOS identification methods for smartphone-based indoor positioning systems using WiFi RSS and RTT distance measurement (RDM). Based on our extensive analysis of RSS and RDM dispersion features, three machine learning algorithms were chosen and developed to separate the samples for NLOS/LOS conditions. Experiments show that our best method achieves a discrimination accuracy of over 96% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.

The key statistical properties of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) estimators were derived in this study for zero mean symmetric error distributions. A density function, named the Approximate Root Normal Distribution (ARND), was developed to approximate the distribution of a square root of a normal random variable. This enabled approximating the distribution of the RMSE estimator. The theoretical derivations and the demonstrations on common distributions, a benchmark time series, and a real world data set (with prediction errors generated from ANN and ARIMA models) lead to the following practically useful findings. When comparing errors having the same distribution type, RMSE was shown to be preferred for platykurtic distributions, MAE for leptokurtic distributions, and either RMSE or MAE for mesokurtic distributions. For different distribution types, however, using the two estimators alone was shown to lead to erroneous conclusions. The revelation that the estimated RMSE/MSE ratio could identify whether the errors came from platykurtic/mesokurtic/leptokurtic distributions was a useful complementary result. Comparison of errors based on, error distributions, sample size and the standard errors of the estimators, was discussed. The proposed procedure for deriving the statistical properties of the two estimators has scope for extension for other distribution types.

Physical activity (PA) and exercise have numerous benefits in Cystic Fibrosis (CF) including improved lung function, exercise capacity and quality of life. Despite these benefits, the effectiveness of interventions to promote PA in this population are still largely unknown. The objective of this review was to synthesise existing research and determine whether exercise interventions are effective in promoting PA in people with CF. Using the PRISMA guidelines, a comprehensive search was conducted. Fifteen studies (463 participants) met the inclusion criteria. Eleven studies demonstrated improvements in PA in both short- and long-term interventions. However, the interventions were variable across the included studies, with a large inconsistency in PA assessment tools used. Aerobic training and activity counselling were the two elements identified in this review which most consistently improved PA. Future research should consider larger sample sizes and the use of accurate instruments to assess and track PA levels longitudinally.

Objectives To investigate the general population’s view on artificial intelligence (AI) in medicine with specific emphasis on 3 areas that have experienced major progress in AI research in the past few years, namely radiology, robotic surgery, and dermatology. Methods For this prospective study, the April 2020 Online Longitudinal Internet Studies for the Social Sciences Panel Wave was used. Of the 3117 Longitudinal Internet Studies For The Social Sciences panel members contacted, 2411 completed the full questionnaire (77.4% response rate), after combining data from earlier waves, the final sample size was 1909. A total of 3 scales focusing on trust in the implementation of AI in radiology, robotic surgery, and dermatology were used. Repeated-measures analysis of variance and multivariate analysis of variance was used for comparison. Results The overall means show that respondents have slightly more trust in AI in dermatology than in radiology and surgery. The means show that higher educated males, employed or student, of Western background, and those not admitted to a hospital in the past 12 months have more trust in AI. The trust in AI in radiology, robotic surgery, and dermatology is positively associated with belief in the efficiency of AI and these specific domains were negatively associated with distrust and accountability in AI in general. Conclusions The general population is more distrustful of AI in medicine unlike the overall optimistic views posed in the media. The level of trust is dependent on what medical area is subject to scrutiny. Certain demographic characteristics and individuals with a generally positive view on AI and its efficiency are significantly associated with higher levels of trust in AI.

A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We assess the performance of MAGNA in large-scale simulation studies. Finally, we exemplify the method using four datasets of post-traumatic stress disorder (PTSD) symptoms, and summarize findings from a larger meta-analysis of PTSD symptom.

Background Misclassification bias is introduced into medical claims-based research because of reliance on diagnostic coding rather than full medical record review. We sought to characterize this bias for idiopathic intracranial hypertension (IIH) and evaluate strategies to reduce it. Methods A retrospective review of medical records was conducted using a clinical data warehouse containing medical records and administrative data from an academic medical center. Patients with 1 or more instances of International Classification of Diseases (ICD)-9 or -10 codes for IIH (348.2 or G93.2) between 1989 and 2017 and original results of neuroimaging (head CT or MRI), lumbar puncture, and optic nerve examination were included in the study. Diagnosis of IIH was classified as definite, probable, possible, or inaccurate based on review of medical records. The positive predictive value (PPV) for IIH ICD codes was calculated for all subjects, subjects with an IIH code after all testing was completed, subjects with high numbers of IIH ICD codes and codes spanning longer periods, subjects with IIH ICD codes associated with expert encounters (ophthalmology, neurology, or neurosurgery), and subjects with acetazolamide treatment. Results Of 1,005 patients with ICD codes for IIH, 103 patients had complete testing results and were included in the study. PPV of ICD-9/-10 codes for IIH was 0.63. PPV in restricted samples was 0.82 (code by an ophthalmologist n = 57), 0.70 (acetazolamide treatment n = 87), and 0.72 (code after all testing, n = 78). High numbers of code instances and longer duration between the first and last code instance also increased the PPV. Conclusions An ICD-9 or -10 code for IIH had a PPV of 63% for probable or definite IIH in patients with necessary diagnostic testing performed at a single institution. Coding accuracy was improved in patients with an IIH ICD code assigned by an ophthalmologist. Use of coding algorithms considering treatment providers, number of codes, and treatment is a potential strategy to reduce misclassification bias in medical claims-based research on IIH. However, these are associated with a reduced sample size.

Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical analysis of the greedy approaches for the quality of the returned results is still lacking. In this paper, we first fill this gap by conducting a nontrivial theoretical analysis of the approximation ratio of the returned results. To speed up query processing, a sampling-based method, S toc P res G reed , is developed to reduce the evaluation cost. In addition, a theoretical analysis of the required sample size is conducted to bound the quality of the returned results. Finally, comprehensive experiments are conducted on both real and synthetic datasets to demonstrate the efficiency and effectiveness of the proposed methods.