Standard Error Measures Research Articles

Checking procalcitonin suitability for prognosis and antimicrobial therapy monitoring in burn patients

BackgroundDue to greater infection susceptibility, sepsis is the main cause of death in burn patients. Quick diagnosis and patient stratification, early and appropriated antimicrobial therapy, and focus control are crucial for patients’ survival. On the other hand, superfluous extension of therapy is associated with adverse events and arousal of microbial resistance. The use of biomarkers, necessarily coupled with close clinical examination, may predict outcomes, stratifying patients who need more intensive care, and monitor the efficacy of antimicrobial therapy, allowing faster de-escalation or stop, reducing the development of resistance and possibly the financial burden, without increasing mortality. The aim of this work is to check the suitability of procalcitonin (PCT) to fulfill these goals in a large sample of septic burn patients.MethodsOne hundred and one patients, with 15% or more of total body surface area (TBSA) burned, admitted from January 2011 to December 2014 at Coimbra Burns Unit (CBU), in Portugal were included in the sample. All patients had a diagnosis of sepsis, according to the American Burn Association (ABA) criteria. The sample was factored by survival (68 survivors and 33 non-survivors). The maximum value of PCT in each day was used for statistical analysis. Data were summarized by location measures (mean, median, minimum, maximum, quartiles) and dispersion measures (standard error and range measures). Statistical analysis was performed with SPSS© 23.0 IBM© for Windows©.ResultsThere were statistically significant differences between PCT levels of patients from the survivor and non-survivor groups during the first and the last weeks of hospitalization as well as during the first week after sepsis suspicion, being slightly higher during this period. During the first 7 days of antimicrobial therapy, PCT was always higher in the non-survivor, still without reaching statistical significance, but when the analysis was extended till the 15th day, PCT increased significantly, rapidly, and steadily, denouncing therapy failure.ConclusionDespite being not an ideal biomarker, PCT proved to have good prognostic power in septic burn patients, paralleling the evolution of the infectious process and reflecting the efficacy of antimicrobial therapy, and the inclusion of its serial dosing may be advised to reinforce antimicrobial stewardship programs at burn units; meanwhile, more accurate approaches are not available.

Structure modal optimization of a strapdown inertial navigation system for an electric helicopter using an adaptive surrogate model

The purpose of this research is to prove the eventuality of using a novel adaptive surrogate model for optimization problems. The adaptive surrogate model is based on iteration sampling and extended radial basis function (ERBF). This method improves the precision by a means that new sample points is placed in the blank area and all the sample points is uniformly distributed in the design region. The precision of the surrogate model is checked using standard error measure to determine whether updating the surrogate model or not. Since the prediction of modal frequencies require structure modal simulations. In order to decrease the number of computer simulations, a Multi-Island GA approach is combined with the adaptive surrogate model to find the optimum modal frequencies of a strapdown inertial navigation system for electric helicopters. The strapdown inertial navigation system is comprised of damping material, counterweight material and inertial navigation sensor. This is a multi-objective functions optimization problem since the modal frequencies are considered from mode 1 to mode 6 in this paper. Several weights of multi-objective functions are utilized to research the modal frequencies. The whole number of 15 sampling points is picked to build the primary surrogate model using Latin hypercube sampling (LHS). The results of adaptive surrogate model show that two new sampling points are needed to reform the precision of the surrogate model under the condition of 2 % confidence bounds. The structure modal optimization results show that the choice of the weights for the multi-objective functions has a major effect on the final optimum modal frequencies. Time- and frequency-domain analysis indicated that the optimum modal frequencies are far away from the excitation frequencies to avoid strapdown inertial navigation system resonance as far as possible.

Replica analysis of overfitting in regression models for time-to-event data

Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.

Translation and validation of the shoulder algo-functional score WOSI (Western Ontario Shoulder Instability Index): WOSI-Fr

The Western Ontario Shoulder Instability Index (WOSI) is a specific self-questionnaire which measures the functional impact during patients’ everyday life activities in order to evaluate the therapeutic care face to a chronic scapula-humeral instability. In his original version in English, it is valid, reliable and sensible to change. The goal of this study is to translate and culturally adapt the original algo-functional score WOSI in French and to evaluate metrological quality of this version with patients suffering of chronic instability after scapula-humeral luxation. The WOSI has been translated and culturally adapted in French according to recommendations. Several groups were constituted (Non-operated group [NOG], Operated group [OG], TotG = NOG + OG, PPOG: chronic shoulder instability during pre- and postoperation, and TémG: control group) in order to analyze built version's validity by comparing WOSI French version to Rowe, WalchDuplay, QuickDASH scores, and Visual Analogical Scale (VAS) pain; and reliability by the reproducibility and the internal consistency. WOSI French version was done and accepted by an expert group ( n = 7). There was a significant correlation between the WOSI and the different algo-functional scores for GTot, for GO and for GNO (except for VAS pain and QuickDASH for the last group); correlation between the WOSI and the VAS pain was also significant ( r = 0.48 P < 0.01). The reproducibility ( n = 27) was good: the Intra-Class Correlation (ICC) for the total score was 0.88 (IC 95%: 0.47–0.98), variating from 0.80 to 0.94 including the 4 domains and from 0.70 to 0.94 for the different items separately. The Cronbach alpha was 0.953. As for TotG, the Standard Error Measure (SEM) and the Minimal Detectable Change (MDC) was respectively 12.2 (5.7%) and 333 (15.9%). WOSI French version is disponible, culturally adapted and translate, validate and reliable. We suggest to use it for the follow up of patient with chronic scapula-humeral instability.

Predictive quantification of surrogate model fidelity based on modal variations with sample density

It is generally challenging to quantify the fidelity of surrogate models without additional system evaluations. Standard error measures, such as the mean squared error and cross-validation error, often do not adequately capture the fidelity of the model trained using all available sample points. This paper introduces a new model-independent approach to quantify surrogate model fidelity, called Predictive Estimation of Model Fidelity (PEMF). In PEMF, intermediate surrogates are iteratively constructed over heuristic subsets of sample points. The median and the maximum errors estimated over the remaining points are used to determine the respective error distributions at each iteration. The estimated modes of the error distributions are represented as functions of the density of intermediate training points through nonlinear regression, assuming a smooth decreasing trend of errors with increasing sample density. These regression functions are then used to predict the expected median and maximum errors in the final surrogate model (trained using all available sample points). A Monotonic Trend criterion is defined to statistically test if the regression function is reasonably reliable in predicting the model fidelity, failing which a stable implementation of k-fold cross-validation (based on modal error) is used to predict the final surrogate error. To compare the accuracy and robustness of PEMF with that of the popular leave-one-out cross-validation, numerical experiments are performed using Kriging, RBF, and E-RBF models. It is observed that the model fidelities estimated by PEMF is up to two orders of magnitude more accurate and statistically more stable compared to those based on cross-validation.

Modeling and predicting measured response time of cloud-based web services using long-memory time series

Predicting cloud performance from user’s perspective is a complex task, because of several factors involved in providing the service to the consumer. In this work, the response time of 10 real-world services is analyzed. We have observed long memory in terms of the measured response time of the CPU-intensive services and statistically verified this observation using estimators of the Hurst exponent. Then, naïve, mean, autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) methods are used to forecast the future values of quality of service (QoS) at runtime. Results of the cross-validation over the 10 datasets show that the long-memory ARFIMA model provides the mean of 37.5 % and the maximum of 57.8 % reduction in the forecast error when compared to the short-memory ARIMA model according to the standard error measure of mean absolute percentage error. Our work implies that consideration of the long-range dependence in QoS data can help to improve the selection of services according to their possible future QoS values.

A Standard Error: Distinguishing Standard Deviation From Standard Error

A recent Perspective in Nature issued a call for more transparency in the reporting of preclinical research (1). Although this article focused primarily on experimental design, it emphasized the need for improved reporting in the scientific literature. Within the context of preclinical studies, there have been discussions regarding the appropriate reporting of standard error (SE) and standard deviation (SD) (2–5); however, despite the recommendations, opportunities remain to improve upon the reporting of these statistics in the literature. To first set the stage for the distinction between SD and SE, we start with the similarities. Both SD and SE measure variability or, informally, “spread.” As such, both statistics give a numerical summary of variability. Given this, how does one distinguish SE from SD? The distinction is that one summarizes the variability of data and the other describes the variability of …

A stochastic process approach of the drake equation parameters

AbstractThe number N of detectable (i.e. communicating) extraterrestrial civilizations in the Milky Way galaxy is usually calculated by using the Drake equation. This equation was established in 1961 by Frank Drake and was the first step to quantifying the Search for ExtraTerrestrial Intelligence (SETI) field. Practically, this equation is rather a simple algebraic expression and its simplistic nature leaves it open to frequent re-expression. An additional problem of the Drake equation is the time-independence of its terms, which for example excludes the effects of the physico-chemical history of the galaxy. Recently, it has been demonstrated that the main shortcoming of the Drake equation is its lack of temporal structure, i.e., it fails to take into account various evolutionary processes. In particular, the Drake equation does not provides any error estimation about the measured quantity. Here, we propose a first treatment of these evolutionary aspects by constructing a simple stochastic process that will be able to provide both a temporal structure to the Drake equation (i.e. introduce time in the Drake formula in order to obtain something like N(t)) and a first standard error measure.

Modelling precipitation cooling of overhead conductors

Abstract This paper presents a precipitation-based conductor cooling model for use in power line ampacity rating applications and line temperature calculations. It is aimed at better modelling of a conductor’s temperature by incorporating the line cooling caused by precipitation falling onto power lines. The expanded thermal model quantifies the additional gain of current-carrying capacity for power transmission networks incorporating advanced Dynamic Thermal Circuit Rating systems. The precipitation cooling model is verified against observations made by a power transmission utility using a commercial line current and temperature sensor clamped onto an actual power transmission line. The accuracy of the precipitation model is assessed both quantitatively using standard error measures, and qualitatively in terms of fit of the model to real world data. The precipitation-based cooling extension shows a significant improvement over IEEE Std. 738-2006 in the modelled accuracy of the conductor temperature. It suggests a substantial increase in available ampacity during periods of precipitation.

Precision and Validity of a Clinical Method for Pectoral Minor Length Assessment in Overhead-Throwing Athletes

Methods of assessing pectoral minor length are needed to determine the criterion validity, reliability, and precision of a clinical method for pectoralis minor length assessment. Twentynine overhead-throwing athletes volunteered for this study. All measures were taken with an electromagnetic motion analysis system and Palpation Meter. Pearson correlation coeffi cients were calculated for the average pectoralis minor length values from the electromagnetic motion analysis system and Palpation Meter (0.05) to determine the validity of pectoralis minor length. Intraclass correlation coeffi cients (ICC 2,k) and standard error measures were calculated to determine the intrarater reliability and precision of pectoralis minor length measures. Signifi cant correlations were found between pectoralis minor length measures from the electromagnetic motion analysis system and Palpation Meter for the dominant and nondominant arms. The Palpation Meter was also found to be reliable and precise when measuring pectoralis minor length for the dominant and nondominant arms.

Bootstrap Method for Dependent Data Structure and Measure of Statistical Precision

Problem statement: This article emphasized on the construction of valid inferential procedures for an estimator θ^ as a measure of its statistical precision for dependent data structure. Approach: The truncated geometric bootstrap estimates of standard error and other measures of statistical precision such as bias, coefficient of variation, ratio and root mean square error are considered. Results: We extend it to other measures of statistical precision such as bootstrap confidence interval for an estimator θ^ and illustrate with real geological data. Conclusion/Recommendations: The bootstrap estimates of standard error and other measures of statistical accuracy such as bias, ratio, coefficient of variation and root mean square error reveals the suitability of the method for dependent data structure.

Philippine Mitochondrial DNA Diversity: A Populated Viaduct between Taiwan and Indonesia?

Relatively little is known about the genetic diversity of the Philippine population, and this is an important gap in our understanding of Southeast Asian and Oceanic prehistory. Here we describe mitochondrial DNA (mtDNA) variation in 423 Philippine samples and analyze them in the context of the genetic diversity of other Southeast Asian populations. The majority of Philippine mtDNA types are shared with Taiwanese aboriginal groups and belong to haplogroups of postglacial and pre-Neolithic origin that have previously been identified in East Asian and Island Southeast Asian populations. Analysis of hypervariable segment I sequence variation within individual mtDNA haplogroups indicates a general decrease in the diversity of the most frequent types (B4a1a, E1a1a, and M7c3c) from the Taiwanese aborigines to the Philippines and Sulawesi, although calculated standard error measures overlap for these populations. This finding, together with the geographical distribution of ancestral and derived haplotypes of the B4a1a subclade including the Polynesian Motif, is consistent with southward dispersal of these lineages "Out of Taiwan" via the Philippines to Near Oceania and Polynesia. In addition to the mtDNA components shared with Taiwanese aborigines, complete sequence analyses revealed a minority of lineages in the Philippines that share their origins--possibly dating back to the Paleolithic--with haplogroups from Indonesia and New Guinea. Other rare lineages in the Philippines have no closely related types yet identified elsewhere.

PRECISE EYE AND MOUTH LOCALIZATION

The literature on the topic has shown a strong correlation between the degree of precision of face localization and the face recognition performance. Hence, there is a need for precise facial feature detectors, as well as objective measures for their evaluation and comparison. In this paper, we will present significant improvements to a previous method for precise eye center localization, by integrating a module for mouth localization. The technique is based on Support Vector Machines trained on optimally chosen Haar wavelet coefficients. The method has been tested on several public databases; the results are reported and compared according to a standard error measure. The tests show that the algorithm achieves high precision of localization.

Monotone and Conservative Cascade Remapping between Spherical Grids (CaRS): Regular Latitude–Longitude and Cubed-Sphere Grids

Abstract A high-order monotone and conservative cascade remapping algorithm between spherical grids (CaRS) is developed. This algorithm is specifically designed to remap between the cubed-sphere and regular latitude–longitude grids. The remapping approach is based on the conservative cascade method in which a two-dimensional remapping problem is split into two one-dimensional problems. This allows for easy implementation of high-order subgrid-cell reconstructions as well as the application of advanced monotone filters. The accuracy of CaRS is assessed by remapping analytic fields from the regular latitude–longitude grid to the cubed-sphere grid. In terms of standard error measures, CaRS is found to be competitive relative to an existing algorithm when regridding from a fine to a coarse grid and more accurate when regridding from a coarse to a fine grid.

Avaliação comparativa do padrão de normalidade do perfil facial em pacientes Brasileiros leucodermas e em Norte-Americanos

OBJETIVO: ciente de que a cirurgia ortognática moderna se preocupa em planejar e diagnosticar os casos clínicos utilizando medidas obtidas de grandezas do perfil tegumentar dos pacientes - com o auxílio de imagens digitais empregadas em softwares de planejamento -, no presente estudo foi proposto aferir as medidas de brasileiros leucodermas de descendência européia e compará-las com as medidas já padronizadas por Arnett, com o intuito de criar novas medidas a serem seguidas por brasileiros que utilizam o software de predição cirúrgica Dolphin Imaging 9.0. MÉTODOS: foram utilizadas radiografias cefalométricas de 31 pacientes com oclusão Classe I de Angle e harmonia facial. Todas as radiografias foram digitalizadas e inseridas no software Dolphin 9.0 e 16 pontos de tecido mole e 22 pontos do esqueleto facial foram marcados, seguindo-se exatamente as marcações da análise de Arnett e McLaughlin presentes no programa. RESULTADOS: os resultados obtidos foram avaliados estatisticamente e mostram que o perfil do brasileiro é quase totalmente diferente do perfil norte-americano, exceção feita a apenas quatro pontos para os homens e outros quatro para as mulheres. Os brasileiros apresentam uma face menos protruída, um perfil mais convexo e menor proeminência do queixo do que o grupo controle. CONCLUSÃO: esses dados mostram a necessidade de se realizar algumas mudanças nas grandezas numéricas para que um perfeito diagnóstico e planejamento possam ser realizados em brasileiros, criando assim o padrão do perfil facial do brasileiro leucoderma de descendência européia.

Percepção subjetivo do esforço. Classifi cação do esforço percebido: proposta de utilização da escala de faces

ABSTRACT Subjective exertion perception. Perceived exertion classification: face scale utilization proposal Subjective exertion perception (SEP) is an important strategy to control exercise intensity. Considering that Borg’s Ratings of Perceived Exertion (RPE) scale—a widely used instrument—is based on verbal communication. The aim of the present study was to investigate whether SEP using a scale based on non-verbal communication (i.e., the face scale) was able to provide reliable results. There-fore, our purpose (through a maximum progressive exertion) was to correlate the face scale and peak oxygen consumption (peak VO 2 ), Borg’s RPE scale and peak VO 2 , and the face scale and Borg’s RPE scale. The sample (n = 10) included young non-athletes male adults, apparently healthy. During the test performed in a cycle ergometer, PSEs (Faces and Borg’s RPE) were recorded and respiratory gases were collected, sampled and recorded. With the purpose of determining the profile of the data set, localization (mean and median) and dispersion (standard error and standard deviation) measures were estimated. The correlation between the Face Scale and Borg’s RPE scale in terms of VO

Scaled Symbolic Regression

Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression. It will be proven that the use of a scaled error measure, in which the error is calculated after scaling, is expected to perform better than its unscaled counterpart on all possible symbolic regression problems. As the method (i) does not introduce additional parameters to a symbolic regression run, (ii) is guaranteed to improve results on most symbolic regression problems (and is not worse on any other problem), and (iii) has a well-defined upper bound on the error, scaled squared error is an ideal candidate to become the standard error measure for practical applications of symbolic regression.

Neural network forecasting for airlines: A comparative analysis

This paper provides the first published research paper on the technique of neural network forecasting as applied to the airline industry. It compares this new method with the traditional forecasting techniques (moving averages, exponential smoothing, regression, etc.). The data were provided by a major international carrier. All the methods were compared on the basis of a standard error measure — mean absolute percentage error (MAPE). The results of the study are promising. The most basic neural network structures provided better forecasts than traditional forecasting methods.

Validity of an arterial system model: a quantitative evaluation.

The purpose of this investigation was to validate an arterial system model in a quantitative fashion. The model is based on a finite difference solution to the Navier-Stokes equations and retains certain nonlinear features of the system. Rather than apply standard error measures that test waveform correlation, the validation method used relies on the quantification of differences between the model and animals over a wide range of parametric changes. A series of functions was used to define the difference between the dependent variables of the model and corresponding hemodynamic variables of the animals. These functions were displayed as a family of three-dimensional difference surfaces which related the function value to parametric changes of the independent variables. Experimental data from three dogs were compared with model predictions. Regions of validity for the independent variables were then assessed by examining the difference surfaces.