Sample size planning for composite reliability coefficients: accuracy in parameter estimation via narrow confidence intervals.

PubMed

Terry, Leann; Kelley, Ken

2012-11-01

Composite measures play an important role in psychology and related disciplines. Composite measures almost always have error. Correspondingly, it is important to understand the reliability of the scores from any particular composite measure. However, the point estimates of the reliability of composite measures are fallible and thus all such point estimates should be accompanied by a confidence interval. When confidence intervals are wide, there is much uncertainty in the population value of the reliability coefficient. Given the importance of reporting confidence intervals for estimates of reliability, coupled with the undesirability of wide confidence intervals, we develop methods that allow researchers to plan sample size in order to obtain narrow confidence intervals for population reliability coefficients. We first discuss composite reliability coefficients and then provide a discussion on confidence interval formation for the corresponding population value. Using the accuracy in parameter estimation approach, we develop two methods to obtain accurate estimates of reliability by planning sample size. The first method provides a way to plan sample size so that the expected confidence interval width for the population reliability coefficient is sufficiently narrow. The second method ensures that the confidence interval width will be sufficiently narrow with some desired degree of assurance (e.g., 99% assurance that the 95% confidence interval for the population reliability coefficient will be less than W units wide). The effectiveness of our methods was verified with Monte Carlo simulation studies. We demonstrate how to easily implement the methods with easy-to-use and freely available software. ©2011 The British Psychological Society.

Confidence Interval Coverage for Cohen's Effect Size Statistic

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2006-01-01

Kelley compared three methods for setting a confidence interval (CI) around Cohen's standardized mean difference statistic: the noncentral-"t"-based, percentile (PERC) bootstrap, and biased-corrected and accelerated (BCA) bootstrap methods under three conditions of nonnormality, eight cases of sample size, and six cases of population…

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

PubMed Central

2014-01-01

Background Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented. Methods Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions. Results Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples. Conclusions Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose. PMID:25196829

More accurate, calibrated bootstrap confidence intervals for correlating two autocorrelated climate time series

NASA Astrophysics Data System (ADS)

Olafsdottir, Kristin B.; Mudelsee, Manfred

2013-04-01

Estimation of the Pearson's correlation coefficient between two time series to evaluate the influences of one time depended variable on another is one of the most often used statistical method in climate sciences. Various methods are used to estimate confidence interval to support the correlation point estimate. Many of them make strong mathematical assumptions regarding distributional shape and serial correlation, which are rarely met. More robust statistical methods are needed to increase the accuracy of the confidence intervals. Bootstrap confidence intervals are estimated in the Fortran 90 program PearsonT (Mudelsee, 2003), where the main intention was to get an accurate confidence interval for correlation coefficient between two time series by taking the serial dependence of the process that generated the data into account. However, Monte Carlo experiments show that the coverage accuracy for smaller data sizes can be improved. Here we adapt the PearsonT program into a new version called PearsonT3, by calibrating the confidence interval to increase the coverage accuracy. Calibration is a bootstrap resampling technique, which basically performs a second bootstrap loop or resamples from the bootstrap resamples. It offers, like the non-calibrated bootstrap confidence intervals, robustness against the data distribution. Pairwise moving block bootstrap is used to preserve the serial correlation of both time series. The calibration is applied to standard error based bootstrap Student's t confidence intervals. The performances of the calibrated confidence intervals are examined with Monte Carlo simulations, and compared with the performances of confidence intervals without calibration, that is, PearsonT. The coverage accuracy is evidently better for the calibrated confidence intervals where the coverage error is acceptably small (i.e., within a few percentage points) already for data sizes as small as 20. One form of climate time series is output from numerical models

Confidence intervals for the population mean tailored to small sample sizes, with applications to survey sampling.

PubMed

Rosenblum, Michael A; Laan, Mark J van der

2009-01-07

The validity of standard confidence intervals constructed in survey sampling is based on the central limit theorem. For small sample sizes, the central limit theorem may give a poor approximation, resulting in confidence intervals that are misleading. We discuss this issue and propose methods for constructing confidence intervals for the population mean tailored to small sample sizes. We present a simple approach for constructing confidence intervals for the population mean based on tail bounds for the sample mean that are correct for all sample sizes. Bernstein's inequality provides one such tail bound. The resulting confidence intervals have guaranteed coverage probability under much weaker assumptions than are required for standard methods. A drawback of this approach, as we show, is that these confidence intervals are often quite wide. In response to this, we present a method for constructing much narrower confidence intervals, which are better suited for practical applications, and that are still more robust than confidence intervals based on standard methods, when dealing with small sample sizes. We show how to extend our approaches to much more general estimation problems than estimating the sample mean. We describe how these methods can be used to obtain more reliable confidence intervals in survey sampling. As a concrete example, we construct confidence intervals using our methods for the number of violent deaths between March 2003 and July 2006 in Iraq, based on data from the study "Mortality after the 2003 invasion of Iraq: A cross sectional cluster sample survey," by Burnham et al. (2006).

Interpretation of Confidence Interval Facing the Conflict

ERIC Educational Resources Information Center

Andrade, Luisa; Fernández, Felipe

2016-01-01

As literature has reported, it is usual that university students in statistics courses, and even statistics teachers, interpret the confidence level associated with a confidence interval as the probability that the parameter value will be between the lower and upper interval limits. To confront this misconception, class activities have been…

Reducing the width of confidence intervals for the difference between two population means by inverting adaptive tests.

PubMed

O'Gorman, Thomas W

2018-05-01

In the last decade, it has been shown that an adaptive testing method could be used, along with the Robbins-Monro search procedure, to obtain confidence intervals that are often narrower than traditional confidence intervals. However, these confidence interval limits require a great deal of computation and some familiarity with stochastic search methods. We propose a method for estimating the limits of confidence intervals that uses only a few tests of significance. We compare these limits to those obtained by a lengthy Robbins-Monro stochastic search and find that the proposed method is nearly as accurate as the Robbins-Monro search. Adaptive confidence intervals that are produced by the proposed method are often narrower than traditional confidence intervals when the distributions are long-tailed, skewed, or bimodal. Moreover, the proposed method of estimating confidence interval limits is easy to understand, because it is based solely on the p-values from a few tests of significance.

Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number.

PubMed

Fragkos, Konstantinos C; Tsagris, Michail; Frangos, Christos C

2014-01-01

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal's fail-safe number. Although Rosenthal's estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal's fail-safe number. This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal's estimator.

Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number

PubMed Central

Fragkos, Konstantinos C.; Tsagris, Michail; Frangos, Christos C.

2014-01-01

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal's fail-safe number. Although Rosenthal's estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal's fail-safe number. This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal's estimator. PMID:27437470

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input

Four Bootstrap Confidence Intervals for the Binomial-Error Model.

ERIC Educational Resources Information Center

Lin, Miao-Hsiang; Hsiung, Chao A.

1992-01-01

Four bootstrap methods are identified for constructing confidence intervals for the binomial-error model. The extent to which similar results are obtained and the theoretical foundation of each method and its relevance and ranges of modeling the true score uncertainty are discussed. (SLD)

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

NASA Astrophysics Data System (ADS)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

Confidence intervals for expected moments algorithm flood quantile estimates

USGS Publications Warehouse

Cohn, Timothy A.; Lane, William L.; Stedinger, Jery R.

2001-01-01

Historical and paleoflood information can substantially improve flood frequency estimates if appropriate statistical procedures are properly applied. However, the Federal guidelines for flood frequency analysis, set forth in Bulletin 17B, rely on an inefficient “weighting” procedure that fails to take advantage of historical and paleoflood information. This has led researchers to propose several more efficient alternatives including the Expected Moments Algorithm (EMA), which is attractive because it retains Bulletin 17B's statistical structure (method of moments with the Log Pearson Type 3 distribution) and thus can be easily integrated into flood analyses employing the rest of the Bulletin 17B approach. The practical utility of EMA, however, has been limited because no closed‐form method has been available for quantifying the uncertainty of EMA‐based flood quantile estimates. This paper addresses that concern by providing analytical expressions for the asymptotic variance of EMA flood‐quantile estimators and confidence intervals for flood quantile estimates. Monte Carlo simulations demonstrate the properties of such confidence intervals for sites where a 25‐ to 100‐year streamgage record is augmented by 50 to 150 years of historical information. The experiments show that the confidence intervals, though not exact, should be acceptable for most purposes.

Quantifying uncertainty on sediment loads using bootstrap confidence intervals

NASA Astrophysics Data System (ADS)

Slaets, Johanna I. F.; Piepho, Hans-Peter; Schmitter, Petra; Hilger, Thomas; Cadisch, Georg

2017-01-01

Load estimates are more informative than constituent concentrations alone, as they allow quantification of on- and off-site impacts of environmental processes concerning pollutants, nutrients and sediment, such as soil fertility loss, reservoir sedimentation and irrigation channel siltation. While statistical models used to predict constituent concentrations have been developed considerably over the last few years, measures of uncertainty on constituent loads are rarely reported. Loads are the product of two predictions, constituent concentration and discharge, integrated over a time period, which does not make it straightforward to produce a standard error or a confidence interval. In this paper, a linear mixed model is used to estimate sediment concentrations. A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations are required. The method was tested for a small watershed in Northwest Vietnam for the period 2010-2011. The results showed that confidence intervals were asymmetric, with the highest uncertainty in the upper limit, and that a load of 6262 Mg year-1 had a 95 % confidence interval of (4331, 12 267) in 2010 and a load of 5543 Mg an interval of (3593, 8975) in 2011. Additionally, the approach demonstrated that direct estimates from the data were biased downwards compared to bootstrap median estimates. These results imply that constituent loads predicted from regression-type water quality models could frequently be underestimating sediment yields and their environmental impact.

Confidence Intervals Make a Difference: Effects of Showing Confidence Intervals on Inferential Reasoning

ERIC Educational Resources Information Center

Hoekstra, Rink; Johnson, Addie; Kiers, Henk A. L.

2012-01-01

The use of confidence intervals (CIs) as an addition or as an alternative to null hypothesis significance testing (NHST) has been promoted as a means to make researchers more aware of the uncertainty that is inherent in statistical inference. Little is known, however, about whether presenting results via CIs affects how readers judge the…

Confidence Intervals for True Scores Using the Skew-Normal Distribution

ERIC Educational Resources Information Center

Garcia-Perez, Miguel A.

2010-01-01

A recent comparative analysis of alternative interval estimation approaches and procedures has shown that confidence intervals (CIs) for true raw scores determined with the Score method--which uses the normal approximation to the binomial distribution--have actual coverage probabilities that are closest to their nominal level. It has also recently…

Confidence intervals for a difference between lognormal means in cluster randomization trials.

PubMed

Poirier, Julia; Zou, G Y; Koval, John

2017-04-01

Cluster randomization trials, in which intact social units are randomized to different interventions, have become popular in the last 25 years. Outcomes from these trials in many cases are positively skewed, following approximately lognormal distributions. When inference is focused on the difference between treatment arm arithmetic means, existent confidence interval procedures either make restricting assumptions or are complex to implement. We approach this problem by assuming log-transformed outcomes from each treatment arm follow a one-way random effects model. The treatment arm means are functions of multiple parameters for which separate confidence intervals are readily available, suggesting that the method of variance estimates recovery may be applied to obtain closed-form confidence intervals. A simulation study showed that this simple approach performs well in small sample sizes in terms of empirical coverage, relatively balanced tail errors, and interval widths as compared to existing methods. The methods are illustrated using data arising from a cluster randomization trial investigating a critical pathway for the treatment of community acquired pneumonia.

Bootstrapping Confidence Intervals for Robust Measures of Association.

ERIC Educational Resources Information Center

King, Jason E.

A Monte Carlo simulation study was conducted to determine the bootstrap correction formula yielding the most accurate confidence intervals for robust measures of association. Confidence intervals were generated via the percentile, adjusted, BC, and BC(a) bootstrap procedures and applied to the Winsorized, percentage bend, and Pearson correlation…

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data: Methods and Software.

PubMed

Zhang, Zhiyong; Yuan, Ke-Hai

2016-06-01

Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega.

Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling

ERIC Educational Resources Information Center

Banjanovic, Erin S.; Osborne, Jason W.

2016-01-01

Confidence intervals for effect sizes (CIES) provide readers with an estimate of the strength of a reported statistic as well as the relative precision of the point estimate. These statistics offer more information and context than null hypothesis statistic testing. Although confidence intervals have been recommended by scholars for many years,…

Procedures for estimating confidence intervals for selected method performance parameters.

PubMed

McClure, F D; Lee, J K

2001-01-01

Procedures for estimating confidence intervals (CIs) for the repeatability variance (sigmar2), reproducibility variance (sigmaR2 = sigmaL2 + sigmar2), laboratory component (sigmaL2), and their corresponding standard deviations sigmar, sigmaR, and sigmaL, respectively, are presented. In addition, CIs for the ratio of the repeatability component to the reproducibility variance (sigmar2/sigmaR2) and the ratio of the laboratory component to the reproducibility variance (sigmaL2/sigmaR2) are also presented.

Confidence intervals from single observations in forest research

Treesearch

Harry T. Valentine; George M. Furnival; Timothy G. Gregoire

1991-01-01

A procedure for constructing confidence intervals and testing hypothese from a single trial or observation is reviewed. The procedure requires a prior, fixed estimate or guess of the outcome of an experiment or sampling. Two examples of applications are described: a confidence interval is constructed for the expected outcome of a systematic sampling of a forested tract...

Calculation of the confidence intervals for transformation parameters in the registration of medical images

PubMed Central

Bansal, Ravi; Staib, Lawrence H.; Laine, Andrew F.; Xu, Dongrong; Liu, Jun; Posecion, Lainie F.; Peterson, Bradley S.

2010-01-01

Images from different individuals typically cannot be registered precisely because anatomical features within the images differ across the people imaged and because the current methods for image registration have inherent technological limitations that interfere with perfect registration. Quantifying the inevitable error in image registration is therefore of crucial importance in assessing the effects that image misregistration may have on subsequent analyses in an imaging study. We have developed a mathematical framework for quantifying errors in registration by computing the confidence intervals of the estimated parameters (3 translations, 3 rotations, and 1 global scale) for the similarity transformation. The presence of noise in images and the variability in anatomy across individuals ensures that estimated registration parameters are always random variables. We assume a functional relation among intensities across voxels in the images, and we use the theory of nonlinear, least-squares estimation to show that the parameters are multivariate Gaussian distributed. We then use the covariance matrix of this distribution to compute the confidence intervals of the transformation parameters. These confidence intervals provide a quantitative assessment of the registration error across the images. Because transformation parameters are nonlinearly related to the coordinates of landmark points in the brain, we subsequently show that the coordinates of those landmark points are also multivariate Gaussian distributed. Using these distributions, we then compute the confidence intervals of the coordinates for landmark points in the image. Each of these confidence intervals in turn provides a quantitative assessment of the registration error at a particular landmark point. Because our method is computationally intensive, however, its current implementation is limited to assessing the error of the parameters in the similarity transformation across images. We assessed the

Confidence Intervals from Realizations of Simulated Nuclear Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younes, W.; Ratkiewicz, A.; Ressler, J. J.

2017-09-28

Various statistical techniques are discussed that can be used to assign a level of confidence in the prediction of models that depend on input data with known uncertainties and correlations. The particular techniques reviewed in this paper are: 1) random realizations of the input data using Monte-Carlo methods, 2) the construction of confidence intervals to assess the reliability of model predictions, and 3) resampling techniques to impose statistical constraints on the input data based on additional information. These techniques are illustrated with a calculation of the keff value, based on the 235U(n, f) and 239Pu (n, f) cross sections.

Robust Confidence Interval for a Ratio of Standard Deviations

ERIC Educational Resources Information Center

Bonett, Douglas G.

2006-01-01

Comparing variability of test scores across alternate forms, test conditions, or subpopulations is a fundamental problem in psychometrics. A confidence interval for a ratio of standard deviations is proposed that performs as well as the classic method with normal distributions and performs dramatically better with nonnormal distributions. A simple…

Asymptotic confidence intervals for the Pearson correlation via skewness and kurtosis.

PubMed

Bishara, Anthony J; Li, Jiexiang; Nash, Thomas

2018-02-01

When bivariate normality is violated, the default confidence interval of the Pearson correlation can be inaccurate. Two new methods were developed based on the asymptotic sampling distribution of Fisher's z' under the general case where bivariate normality need not be assumed. In Monte Carlo simulations, the most successful of these methods relied on the (Vale & Maurelli, 1983, Psychometrika, 48, 465) family to approximate a distribution via the marginal skewness and kurtosis of the sample data. In Simulation 1, this method provided more accurate confidence intervals of the correlation in non-normal data, at least as compared to no adjustment of the Fisher z' interval, or to adjustment via the sample joint moments. In Simulation 2, this approximate distribution method performed favourably relative to common non-parametric bootstrap methods, but its performance was mixed relative to an observed imposed bootstrap and two other robust methods (PM1 and HC4). No method was completely satisfactory. An advantage of the approximate distribution method, though, is that it can be implemented even without access to raw data if sample skewness and kurtosis are reported, making the method particularly useful for meta-analysis. Supporting information includes R code. © 2017 The British Psychological Society.

Likelihood-based confidence intervals for estimating floods with given return periods

NASA Astrophysics Data System (ADS)

Martins, Eduardo Sávio P. R.; Clarke, Robin T.

1993-06-01

This paper discusses aspects of the calculation of likelihood-based confidence intervals for T-year floods, with particular reference to (1) the two-parameter gamma distribution; (2) the Gumbel distribution; (3) the two-parameter log-normal distribution, and other distributions related to the normal by Box-Cox transformations. Calculation of the confidence limits is straightforward using the Nelder-Mead algorithm with a constraint incorporated, although care is necessary to ensure convergence either of the Nelder-Mead algorithm, or of the Newton-Raphson calculation of maximum-likelihood estimates. Methods are illustrated using records from 18 gauging stations in the basin of the River Itajai-Acu, State of Santa Catarina, southern Brazil. A small and restricted simulation compared likelihood-based confidence limits with those given by use of the central limit theorem; for the same confidence probability, the confidence limits of the simulation were wider than those of the central limit theorem, which failed more frequently to contain the true quantile being estimated. The paper discusses possible applications of likelihood-based confidence intervals in other areas of hydrological analysis.

Reporting Confidence Intervals and Effect Sizes: Collecting the Evidence

ERIC Educational Resources Information Center

Zientek, Linda Reichwein; Ozel, Z. Ebrar Yetkiner; Ozel, Serkan; Allen, Jeff

2012-01-01

Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…

Reliability of confidence intervals calculated by bootstrap and classical methods using the FIA 1-ha plot design

Treesearch

H. T. Schreuder; M. S. Williams

2000-01-01

In simulation sampling from forest populations using sample sizes of 20, 40, and 60 plots respectively, confidence intervals based on the bootstrap (accelerated, percentile, and t-distribution based) were calculated and compared with those based on the classical t confidence intervals for mapped populations and subdomains within those populations. A 68.1 ha mapped...

Closed-form confidence intervals for functions of the normal mean and standard deviation.

PubMed

Donner, Allan; Zou, G Y

2012-08-01

Confidence interval methods for a normal mean and standard deviation are well known and simple to apply. However, the same cannot be said for important functions of these parameters. These functions include the normal distribution percentiles, the Bland-Altman limits of agreement, the coefficient of variation and Cohen's effect size. We present a simple approach to this problem by using variance estimates recovered from confidence limits computed for the mean and standard deviation separately. All resulting confidence intervals have closed forms. Simulation results demonstrate that this approach performs very well for limits of agreement, coefficients of variation and their differences.

The Distribution of the Product Explains Normal Theory Mediation Confidence Interval Estimation.

PubMed

Kisbu-Sakarya, Yasemin; MacKinnon, David P; Miočević, Milica

2014-05-01

The distribution of the product has several useful applications. One of these applications is its use to form confidence intervals for the indirect effect as the product of 2 regression coefficients. The purpose of this article is to investigate how the moments of the distribution of the product explain normal theory mediation confidence interval coverage and imbalance. Values of the critical ratio for each random variable are used to demonstrate how the moments of the distribution of the product change across values of the critical ratio observed in research studies. Results of the simulation study showed that as skewness in absolute value increases, coverage decreases. And as skewness in absolute value and kurtosis increases, imbalance increases. The difference between testing the significance of the indirect effect using the normal theory versus the asymmetric distribution of the product is further illustrated with a real data example. This article is the first study to show the direct link between the distribution of the product and indirect effect confidence intervals and clarifies the results of previous simulation studies by showing why normal theory confidence intervals for indirect effects are often less accurate than those obtained from the asymmetric distribution of the product or from resampling methods.

Exact Scheffé-type confidence intervals for output from groundwater flow models: 1. Use of hydrogeologic information

USGS Publications Warehouse

Cooley, Richard L.

1993-01-01

A new method is developed to efficiently compute exact Scheffé-type confidence intervals for output (or other function of parameters) g(β) derived from a groundwater flow model. The method is general in that parameter uncertainty can be specified by any statistical distribution having a log probability density function (log pdf) that can be expanded in a Taylor series. However, for this study parameter uncertainty is specified by a statistical multivariate beta distribution that incorporates hydrogeologic information in the form of the investigator's best estimates of parameters and a grouping of random variables representing possible parameter values so that each group is defined by maximum and minimum bounds and an ordering according to increasing value. The new method forms the confidence intervals from maximum and minimum limits of g(β) on a contour of a linear combination of (1) the quadratic form for the parameters used by Cooley and Vecchia (1987) and (2) the log pdf for the multivariate beta distribution. Three example problems are used to compare characteristics of the confidence intervals for hydraulic head obtained using different weights for the linear combination. Different weights generally produced similar confidence intervals, whereas the method of Cooley and Vecchia (1987) often produced much larger confidence intervals.

Introduction to Sample Size Choice for Confidence Intervals Based on "t" Statistics

ERIC Educational Resources Information Center

Liu, Xiaofeng Steven; Loudermilk, Brandon; Simpson, Thomas

2014-01-01

Sample size can be chosen to achieve a specified width in a confidence interval. The probability of obtaining a narrow width given that the confidence interval includes the population parameter is defined as the power of the confidence interval, a concept unfamiliar to many practitioners. This article shows how to utilize the Statistical Analysis…

Accuracy in parameter estimation for targeted effects in structural equation modeling: sample size planning for narrow confidence intervals.

PubMed

Lai, Keke; Kelley, Ken

2011-06-01

In addition to evaluating a structural equation model (SEM) as a whole, often the model parameters are of interest and confidence intervals for those parameters are formed. Given a model with a good overall fit, it is entirely possible for the targeted effects of interest to have very wide confidence intervals, thus giving little information about the magnitude of the population targeted effects. With the goal of obtaining sufficiently narrow confidence intervals for the model parameters of interest, sample size planning methods for SEM are developed from the accuracy in parameter estimation approach. One method plans for the sample size so that the expected confidence interval width is sufficiently narrow. An extended procedure ensures that the obtained confidence interval will be no wider than desired, with some specified degree of assurance. A Monte Carlo simulation study was conducted that verified the effectiveness of the procedures in realistic situations. The methods developed have been implemented in the MBESS package in R so that they can be easily applied by researchers. © 2011 American Psychological Association

Methods for estimating confidence intervals in interrupted time series analyses of health interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Soumerai, Stephen B; Ross-Degnan, Dennis

2009-02-01

Interrupted time series (ITS) is a strong quasi-experimental research design, which is increasingly applied to estimate the effects of health services and policy interventions. We describe and illustrate two methods for estimating confidence intervals (CIs) around absolute and relative changes in outcomes calculated from segmented regression parameter estimates. We used multivariate delta and bootstrapping methods (BMs) to construct CIs around relative changes in level and trend, and around absolute changes in outcome based on segmented linear regression analyses of time series data corrected for autocorrelated errors. Using previously published time series data, we estimated CIs around the effect of prescription alerts for interacting medications with warfarin on the rate of prescriptions per 10,000 warfarin users per month. Both the multivariate delta method (MDM) and the BM produced similar results. BM is preferred for calculating CIs of relative changes in outcomes of time series studies, because it does not require large sample sizes when parameter estimates are obtained correctly from the model. Caution is needed when sample size is small.

Standardized likelihood ratio test for comparing several log-normal means and confidence interval for the common mean.

PubMed

Krishnamoorthy, K; Oral, Evrim

2017-12-01

Standardized likelihood ratio test (SLRT) for testing the equality of means of several log-normal distributions is proposed. The properties of the SLRT and an available modified likelihood ratio test (MLRT) and a generalized variable (GV) test are evaluated by Monte Carlo simulation and compared. Evaluation studies indicate that the SLRT is accurate even for small samples, whereas the MLRT could be quite liberal for some parameter values, and the GV test is in general conservative and less powerful than the SLRT. Furthermore, a closed-form approximate confidence interval for the common mean of several log-normal distributions is developed using the method of variance estimate recovery, and compared with the generalized confidence interval with respect to coverage probabilities and precision. Simulation studies indicate that the proposed confidence interval is accurate and better than the generalized confidence interval in terms of coverage probabilities. The methods are illustrated using two examples.

[Confidence interval or p-value--similarities and differences between two important methods of statistical inference of quantitative studies].

PubMed

Harari, Gil

2014-01-01

Statistic significance, also known as p-value, and CI (Confidence Interval) are common statistics measures and are essential for the statistical analysis of studies in medicine and life sciences. These measures provide complementary information about the statistical probability and conclusions regarding the clinical significance of study findings. This article is intended to describe the methodologies, compare between the methods, assert their suitability for the different needs of study results analysis and to explain situations in which each method should be used.

Using an R Shiny to Enhance the Learning Experience of Confidence Intervals

ERIC Educational Resources Information Center

Williams, Immanuel James; Williams, Kelley Kim

2018-01-01

Many students find understanding confidence intervals difficult, especially because of the amalgamation of concepts such as confidence levels, standard error, point estimates and sample sizes. An R Shiny application was created to assist the learning process of confidence intervals using graphics and data from the US National Basketball…

Teach a Confidence Interval for the Median in the First Statistics Course

ERIC Educational Resources Information Center

Howington, Eric B.

2017-01-01

Few introductory statistics courses consider statistical inference for the median. This article argues in favour of adding a confidence interval for the median to the first statistics course. Several methods suitable for introductory statistics students are identified and briefly reviewed.

Quantitative imaging biomarkers: Effect of sample size and bias on confidence interval coverage.

PubMed

Obuchowski, Nancy A; Bullen, Jennifer

2017-01-01

Introduction Quantitative imaging biomarkers (QIBs) are being increasingly used in medical practice and clinical trials. An essential first step in the adoption of a quantitative imaging biomarker is the characterization of its technical performance, i.e. precision and bias, through one or more performance studies. Then, given the technical performance, a confidence interval for a new patient's true biomarker value can be constructed. Estimating bias and precision can be problematic because rarely are both estimated in the same study, precision studies are usually quite small, and bias cannot be measured when there is no reference standard. Methods A Monte Carlo simulation study was conducted to assess factors affecting nominal coverage of confidence intervals for a new patient's quantitative imaging biomarker measurement and for change in the quantitative imaging biomarker over time. Factors considered include sample size for estimating bias and precision, effect of fixed and non-proportional bias, clustered data, and absence of a reference standard. Results Technical performance studies of a quantitative imaging biomarker should include at least 35 test-retest subjects to estimate precision and 65 cases to estimate bias. Confidence intervals for a new patient's quantitative imaging biomarker measurement constructed under the no-bias assumption provide nominal coverage as long as the fixed bias is <12%. For confidence intervals of the true change over time, linearity must hold and the slope of the regression of the measurements vs. true values should be between 0.95 and 1.05. The regression slope can be assessed adequately as long as fixed multiples of the measurand can be generated. Even small non-proportional bias greatly reduces confidence interval coverage. Multiple lesions in the same subject can be treated as independent when estimating precision. Conclusion Technical performance studies of quantitative imaging biomarkers require moderate sample sizes in

Intervals for posttest probabilities: a comparison of 5 methods.

PubMed

Mossman, D; Berger, J O

2001-01-01

Several medical articles discuss methods of constructing confidence intervals for single proportions and the likelihood ratio, but scant attention has been given to the systematic study of intervals for the posterior odds, or the positive predictive value, of a test. The authors describe 5 methods of constructing confidence intervals for posttest probabilities when estimates of sensitivity, specificity, and the pretest probability of a disorder are derived from empirical data. They then evaluate each method to determine how well the intervals' coverage properties correspond to their nominal value. When the estimates of pretest probabilities, sensitivity, and specificity are derived from more than 80 subjects and are not close to 0 or 1, all methods generate intervals with appropriate coverage properties. When these conditions are not met, however, the best-performing method is an objective Bayesian approach implemented by a simple simulation using a spreadsheet. Physicians and investigators can generate accurate confidence intervals for posttest probabilities in small-sample situations using the objective Bayesian approach.

Confidence intervals for the between-study variance in random-effects meta-analysis using generalised heterogeneity statistics: should we use unequal tails?

PubMed

Jackson, Dan; Bowden, Jack

2016-09-07

Confidence intervals for the between study variance are useful in random-effects meta-analyses because they quantify the uncertainty in the corresponding point estimates. Methods for calculating these confidence intervals have been developed that are based on inverting hypothesis tests using generalised heterogeneity statistics. Whilst, under the random effects model, these new methods furnish confidence intervals with the correct coverage, the resulting intervals are usually very wide, making them uninformative. We discuss a simple strategy for obtaining 95 % confidence intervals for the between-study variance with a markedly reduced width, whilst retaining the nominal coverage probability. Specifically, we consider the possibility of using methods based on generalised heterogeneity statistics with unequal tail probabilities, where the tail probability used to compute the upper bound is greater than 2.5 %. This idea is assessed using four real examples and a variety of simulation studies. Supporting analytical results are also obtained. Our results provide evidence that using unequal tail probabilities can result in shorter 95 % confidence intervals for the between-study variance. We also show some further results for a real example that illustrates how shorter confidence intervals for the between-study variance can be useful when performing sensitivity analyses for the average effect, which is usually the parameter of primary interest. We conclude that using unequal tail probabilities when computing 95 % confidence intervals for the between-study variance, when using methods based on generalised heterogeneity statistics, can result in shorter confidence intervals. We suggest that those who find the case for using unequal tail probabilities convincing should use the '1-4 % split', where greater tail probability is allocated to the upper confidence bound. The 'width-optimal' interval that we present deserves further investigation.

Coefficient Omega Bootstrap Confidence Intervals: Nonnormal Distributions

ERIC Educational Resources Information Center

Padilla, Miguel A.; Divers, Jasmin

2013-01-01

The performance of the normal theory bootstrap (NTB), the percentile bootstrap (PB), and the bias-corrected and accelerated (BCa) bootstrap confidence intervals (CIs) for coefficient omega was assessed through a Monte Carlo simulation under conditions not previously investigated. Of particular interests were nonnormal Likert-type and binary items.…

Confidence Intervals for the Mean: To Bootstrap or Not to Bootstrap

ERIC Educational Resources Information Center

Calzada, Maria E.; Gardner, Holly

2011-01-01

The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…

Toward Using Confidence Intervals to Compare Correlations

ERIC Educational Resources Information Center

Zou, Guang Yong

2007-01-01

Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate…

The P Value Problem in Otolaryngology: Shifting to Effect Sizes and Confidence Intervals.

PubMed

Vila, Peter M; Townsend, Melanie Elizabeth; Bhatt, Neel K; Kao, W Katherine; Sinha, Parul; Neely, J Gail

2017-06-01

There is a lack of reporting effect sizes and confidence intervals in the current biomedical literature. The objective of this article is to present a discussion of the recent paradigm shift encouraging the use of reporting effect sizes and confidence intervals. Although P values help to inform us about whether an effect exists due to chance, effect sizes inform us about the magnitude of the effect (clinical significance), and confidence intervals inform us about the range of plausible estimates for the general population mean (precision). Reporting effect sizes and confidence intervals is a necessary addition to the biomedical literature, and these concepts are reviewed in this article.

Exact Scheffé-type confidence intervals for output from groundwater flow models: 2. Combined use of hydrogeologic information and calibration data

USGS Publications Warehouse

Cooley, Richard L.

1993-01-01

Calibration data (observed values corresponding to model-computed values of dependent variables) are incorporated into a general method of computing exact Scheffé-type confidence intervals analogous to the confidence intervals developed in part 1 (Cooley, this issue) for a function of parameters derived from a groundwater flow model. Parameter uncertainty is specified by a distribution of parameters conditioned on the calibration data. This distribution was obtained as a posterior distribution by applying Bayes' theorem to the hydrogeologically derived prior distribution of parameters from part 1 and a distribution of differences between the calibration data and corresponding model-computed dependent variables. Tests show that the new confidence intervals can be much smaller than the intervals of part 1 because the prior parameter variance-covariance structure is altered so that combinations of parameters that give poor model fit to the data are unlikely. The confidence intervals of part 1 and the new confidence intervals can be effectively employed in a sequential method of model construction whereby new information is used to reduce confidence interval widths at each stage.

Calculation of Confidence Intervals for the Maximum Magnitude of Earthquakes in Different Seismotectonic Zones of Iran

NASA Astrophysics Data System (ADS)

Salamat, Mona; Zare, Mehdi; Holschneider, Matthias; Zöller, Gert

2017-03-01

The problem of estimating the maximum possible earthquake magnitude m_max has attracted growing attention in recent years. Due to sparse data, the role of uncertainties becomes crucial. In this work, we determine the uncertainties related to the maximum magnitude in terms of confidence intervals. Using an earthquake catalog of Iran, m_max is estimated for different predefined levels of confidence in six seismotectonic zones. Assuming the doubly truncated Gutenberg-Richter distribution as a statistical model for earthquake magnitudes, confidence intervals for the maximum possible magnitude of earthquakes are calculated in each zone. While the lower limit of the confidence interval is the magnitude of the maximum observed event,the upper limit is calculated from the catalog and the statistical model. For this aim, we use the original catalog which no declustering methods applied on as well as a declustered version of the catalog. Based on the study by Holschneider et al. (Bull Seismol Soc Am 101(4):1649-1659, 2011), the confidence interval for m_max is frequently unbounded, especially if high levels of confidence are required. In this case, no information is gained from the data. Therefore, we elaborate for which settings finite confidence levels are obtained. In this work, Iran is divided into six seismotectonic zones, namely Alborz, Azerbaijan, Zagros, Makran, Kopet Dagh, Central Iran. Although calculations of the confidence interval in Central Iran and Zagros seismotectonic zones are relatively acceptable for meaningful levels of confidence, results in Kopet Dagh, Alborz, Azerbaijan and Makran are not that much promising. The results indicate that estimating m_max from an earthquake catalog for reasonable levels of confidence alone is almost impossible.

Using Screencast Videos to Enhance Undergraduate Students' Statistical Reasoning about Confidence Intervals

ERIC Educational Resources Information Center

Strazzeri, Kenneth Charles

2013-01-01

The purposes of this study were to investigate (a) undergraduate students' reasoning about the concepts of confidence intervals (b) undergraduate students' interactions with "well-designed" screencast videos on sampling distributions and confidence intervals, and (c) how screencast videos improve undergraduate students' reasoning ability…

Applying Bootstrap Resampling to Compute Confidence Intervals for Various Statistics with R

ERIC Educational Resources Information Center

Dogan, C. Deha

2017-01-01

Background: Most of the studies in academic journals use p values to represent statistical significance. However, this is not a good indicator of practical significance. Although confidence intervals provide information about the precision of point estimation, they are, unfortunately, rarely used. The infrequent use of confidence intervals might…

Methods for the accurate estimation of confidence intervals on protein folding ϕ-values

PubMed Central

Ruczinski, Ingo; Sosnick, Tobin R.; Plaxco, Kevin W.

2006-01-01

ϕ-Values provide an important benchmark for the comparison of experimental protein folding studies to computer simulations and theories of the folding process. Despite the growing importance of ϕ measurements, however, formulas to quantify the precision with which ϕ is measured have seen little significant discussion. Moreover, a commonly employed method for the determination of standard errors on ϕ estimates assumes that estimates of the changes in free energy of the transition and folded states are independent. Here we demonstrate that this assumption is usually incorrect and that this typically leads to the underestimation of ϕ precision. We derive an analytical expression for the precision of ϕ estimates (assuming linear chevron behavior) that explicitly takes this dependence into account. We also describe an alternative method that implicitly corrects for the effect. By simulating experimental chevron data, we show that both methods accurately estimate ϕ confidence intervals. We also explore the effects of the commonly employed techniques of calculating ϕ from kinetics estimated at non-zero denaturant concentrations and via the assumption of parallel chevron arms. We find that these approaches can produce significantly different estimates for ϕ (again, even for truly linear chevron behavior), indicating that they are not equivalent, interchangeable measures of transition state structure. Lastly, we describe a Web-based implementation of the above algorithms for general use by the protein folding community. PMID:17008714

Using Asymptotic Results to Obtain a Confidence Interval for the Population Median

ERIC Educational Resources Information Center

Jamshidian, M.; Khatoonabadi, M.

2007-01-01

Almost all introductory and intermediate level statistics textbooks include the topic of confidence interval for the population mean. Almost all these texts introduce the median as a robust measure of central tendency. Only a few of these books, however, cover inference on the population median and in particular confidence interval for the median.…

Improved confidence intervals when the sample is counted an integer times longer than the blank.

PubMed

Potter, William Edward; Strzelczyk, Jadwiga Jodi

2011-05-01

Past computer solutions for confidence intervals in paired counting are extended to the case where the ratio of the sample count time to the blank count time is taken to be an integer, IRR. Previously, confidence intervals have been named Neyman-Pearson confidence intervals; more correctly they should have been named Neyman confidence intervals or simply confidence intervals. The technique utilized mimics a technique used by Pearson and Hartley to tabulate confidence intervals for the expected value of the discrete Poisson and Binomial distributions. The blank count and the contribution of the sample to the gross count are assumed to be Poisson distributed. The expected value of the blank count, in the sample count time, is assumed known. The net count, OC, is taken to be the gross count minus the product of IRR with the blank count. The probability density function (PDF) for the net count can be determined in a straightforward manner.

Likelihood-Based Confidence Intervals in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Oort, Frans J.

2011-01-01

In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by…

Confidence intervals for population allele frequencies: the general case of sampling from a finite diploid population of any size.

PubMed

Fung, Tak; Keenan, Kevin

2014-01-01

The estimation of population allele frequencies using sample data forms a central component of studies in population genetics. These estimates can be used to test hypotheses on the evolutionary processes governing changes in genetic variation among populations. However, existing studies frequently do not account for sampling uncertainty in these estimates, thus compromising their utility. Incorporation of this uncertainty has been hindered by the lack of a method for constructing confidence intervals containing the population allele frequencies, for the general case of sampling from a finite diploid population of any size. In this study, we address this important knowledge gap by presenting a rigorous mathematical method to construct such confidence intervals. For a range of scenarios, the method is used to demonstrate that for a particular allele, in order to obtain accurate estimates within 0.05 of the population allele frequency with high probability (> or = 95%), a sample size of > 30 is often required. This analysis is augmented by an application of the method to empirical sample allele frequency data for two populations of the checkerspot butterfly (Melitaea cinxia L.), occupying meadows in Finland. For each population, the method is used to derive > or = 98.3% confidence intervals for the population frequencies of three alleles. These intervals are then used to construct two joint > or = 95% confidence regions, one for the set of three frequencies for each population. These regions are then used to derive a > or = 95%% confidence interval for Jost's D, a measure of genetic differentiation between the two populations. Overall, the results demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in studies of population genetics, important for scientific hypothesis-testing and also for risk-based natural resource management.

Profile-likelihood Confidence Intervals in Item Response Theory Models.

PubMed

Chalmers, R Philip; Pek, Jolynn; Liu, Yang

2017-01-01

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.

Statistical variability and confidence intervals for planar dose QA pass rates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bailey, Daniel W.; Nelms, Benjamin E.; Attwood, Kristopher

Purpose: The most common metric for comparing measured to calculated dose, such as for pretreatment quality assurance of intensity-modulated photon fields, is a pass rate (%) generated using percent difference (%Diff), distance-to-agreement (DTA), or some combination of the two (e.g., gamma evaluation). For many dosimeters, the grid of analyzed points corresponds to an array with a low areal density of point detectors. In these cases, the pass rates for any given comparison criteria are not absolute but exhibit statistical variability that is a function, in part, on the detector sampling geometry. In this work, the authors analyze the statistics ofmore » various methods commonly used to calculate pass rates and propose methods for establishing confidence intervals for pass rates obtained with low-density arrays. Methods: Dose planes were acquired for 25 prostate and 79 head and neck intensity-modulated fields via diode array and electronic portal imaging device (EPID), and matching calculated dose planes were created via a commercial treatment planning system. Pass rates for each dose plane pair (both centered to the beam central axis) were calculated with several common comparison methods: %Diff/DTA composite analysis and gamma evaluation, using absolute dose comparison with both local and global normalization. Specialized software was designed to selectively sample the measured EPID response (very high data density) down to discrete points to simulate low-density measurements. The software was used to realign the simulated detector grid at many simulated positions with respect to the beam central axis, thereby altering the low-density sampled grid. Simulations were repeated with 100 positional iterations using a 1 detector/cm{sup 2} uniform grid, a 2 detector/cm{sup 2} uniform grid, and similar random detector grids. For each simulation, %/DTA composite pass rates were calculated with various %Diff/DTA criteria and for both local and global %Diff normalization

Monte Carlo simulation of parameter confidence intervals for non-linear regression analysis of biological data using Microsoft Excel.

PubMed

Lambert, Ronald J W; Mytilinaios, Ioannis; Maitland, Luke; Brown, Angus M

2012-08-01

This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. However, a disadvantage of using the Excel method was the inability to return confidence intervals for the computed parameters or the correlations between them. Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data

PubMed Central

Zhang, Zhiyong; Yuan, Ke-Hai

2015-01-01

Cronbach’s coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald’s omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega. PMID:29795870

Confidence intervals and sample size calculations for the standardized mean difference effect size between two normal populations under heteroscedasticity.

PubMed

Shieh, G

2013-12-01

The use of effect sizes and associated confidence intervals in all empirical research has been strongly emphasized by journal publication guidelines. To help advance theory and practice in the social sciences, this article describes an improved procedure for constructing confidence intervals of the standardized mean difference effect size between two independent normal populations with unknown and possibly unequal variances. The presented approach has advantages over the existing formula in both theoretical justification and computational simplicity. In addition, simulation results show that the suggested one- and two-sided confidence intervals are more accurate in achieving the nominal coverage probability. The proposed estimation method provides a feasible alternative to the most commonly used measure of Cohen's d and the corresponding interval procedure when the assumption of homogeneous variances is not tenable. To further improve the potential applicability of the suggested methodology, the sample size procedures for precise interval estimation of the standardized mean difference are also delineated. The desired precision of a confidence interval is assessed with respect to the control of expected width and to the assurance probability of interval width within a designated value. Supplementary computer programs are developed to aid in the usefulness and implementation of the introduced techniques.

Another look at confidence intervals: Proposal for a more relevant and transparent approach

NASA Astrophysics Data System (ADS)

Biller, Steven D.; Oser, Scott M.

2015-02-01

The behaviors of various confidence/credible interval constructions are explored, particularly in the region of low event numbers where methods diverge most. We highlight a number of challenges, such as the treatment of nuisance parameters, and common misconceptions associated with such constructions. An informal survey of the literature suggests that confidence intervals are not always defined in relevant ways and are too often misinterpreted and/or misapplied. This can lead to seemingly paradoxical behaviors and flawed comparisons regarding the relevance of experimental results. We therefore conclude that there is a need for a more pragmatic strategy which recognizes that, while it is critical to objectively convey the information content of the data, there is also a strong desire to derive bounds on model parameter values and a natural instinct to interpret things this way. Accordingly, we attempt to put aside philosophical biases in favor of a practical view to propose a more transparent and self-consistent approach that better addresses these issues.

Confidence intervals for the first crossing point of two hazard functions.

PubMed

Cheng, Ming-Yen; Qiu, Peihua; Tan, Xianming; Tu, Dongsheng

2009-12-01

The phenomenon of crossing hazard rates is common in clinical trials with time to event endpoints. Many methods have been proposed for testing equality of hazard functions against a crossing hazards alternative. However, there has been relatively few approaches available in the literature for point or interval estimation of the crossing time point. The problem of constructing confidence intervals for the first crossing time point of two hazard functions is considered in this paper. After reviewing a recent procedure based on Cox proportional hazard modeling with Box-Cox transformation of the time to event, a nonparametric procedure using the kernel smoothing estimate of the hazard ratio is proposed. The proposed procedure and the one based on Cox proportional hazard modeling with Box-Cox transformation of the time to event are both evaluated by Monte-Carlo simulations and applied to two clinical trial datasets.

Confidence Intervals for Proportion Estimates in Complex Samples. Research Report. ETS RR-06-21

ERIC Educational Resources Information Center

Oranje, Andreas

2006-01-01

Confidence intervals are an important tool to indicate uncertainty of estimates and to give an idea of probable values of an estimate if a different sample from the population was drawn or a different sample of measures was used. Standard symmetric confidence intervals for proportion estimates based on a normal approximation can yield bounds…

Evaluating Independent Proportions for Statistical Difference, Equivalence, Indeterminacy, and Trivial Difference Using Inferential Confidence Intervals

ERIC Educational Resources Information Center

Tryon, Warren W.; Lewis, Charles

2009-01-01

Tryon presented a graphic inferential confidence interval (ICI) approach to analyzing two independent and dependent means for statistical difference, equivalence, replication, indeterminacy, and trivial difference. Tryon and Lewis corrected the reduction factor used to adjust descriptive confidence intervals (DCIs) to create ICIs and introduced…

Confidence Intervals for Weighted Composite Scores under the Compound Binomial Error Model

ERIC Educational Resources Information Center

Kim, Kyung Yong; Lee, Won-Chan

2018-01-01

Reporting confidence intervals with test scores helps test users make important decisions about examinees by providing information about the precision of test scores. Although a variety of estimation procedures based on the binomial error model are available for computing intervals for test scores, these procedures assume that items are randomly…

Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models

ERIC Educational Resources Information Center

Wagler, Amy E.

2014-01-01

Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for…

Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for Heritability.

PubMed

Schweiger, Regev; Fisher, Eyal; Rahmani, Elior; Shenhav, Liat; Rosset, Saharon; Halperin, Eran

2018-06-22

Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense single-nucleotide polymorphism (SNP)-heritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard errors (SEs), which rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals (CIs). In addition, for larger data sets (e.g., tens of thousands of individuals), the construction of SEs itself may require considerable time, as it requires expensive matrix inversions and multiplications. Here, we present FIESTA (Fast confidence IntErvals using STochastic Approximation), a method for constructing accurate CIs. FIESTA is based on parametric bootstrap sampling, and, therefore, avoids unjustified assumptions on the distribution of the heritability estimator. FIESTA uses stochastic approximation techniques, which accelerate the construction of CIs by several orders of magnitude, compared with previous approaches as well as to the analytical approximation used by SEs. FIESTA builds accurate CIs rapidly, for example, requiring only several seconds for data sets of tens of thousands of individuals, making FIESTA a very fast solution to the problem of building accurate CIs for heritability for all data set sizes.

Bootstrap Confidence Intervals for Ordinary Least Squares Factor Loadings and Correlations in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Zhang, Guangjian; Preacher, Kristopher J.; Luo, Shanhong

2010-01-01

This article is concerned with using the bootstrap to assign confidence intervals for rotated factor loadings and factor correlations in ordinary least squares exploratory factor analysis. Coverage performances of "SE"-based intervals, percentile intervals, bias-corrected percentile intervals, bias-corrected accelerated percentile…

Robust misinterpretation of confidence intervals.

PubMed

Hoekstra, Rink; Morey, Richard D; Rouder, Jeffrey N; Wagenmakers, Eric-Jan

2014-10-01

Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students-all in the field of psychology-were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers' performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.

Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection.

PubMed

Carnegie, Nicole Bohme

2011-04-15

The incidence of new infections is a key measure of the status of the HIV epidemic, but accurate measurement of incidence is often constrained by limited data. Karon et al. (Statist. Med. 2008; 27:4617–4633) developed a model to estimate the incidence of HIV infection from surveillance data with biologic testing for recent infection for newly diagnosed cases. This method has been implemented by public health departments across the United States and is behind the new national incidence estimates, which are about 40 per cent higher than previous estimates. We show that the delta method approximation given for the variance of the estimator is incomplete, leading to an inflated variance estimate. This contributes to the generation of overly conservative confidence intervals, potentially obscuring important differences between populations. We demonstrate via simulation that an innovative model-based bootstrap method using the specified model for the infection and surveillance process improves confidence interval coverage and adjusts for the bias in the point estimate. Confidence interval coverage is about 94–97 per cent after correction, compared with 96–99 per cent before. The simulated bias in the estimate of incidence ranges from −6.3 to +14.6 per cent under the original model but is consistently under 1 per cent after correction by the model-based bootstrap. In an application to data from King County, Washington in 2007 we observe correction of 7.2 per cent relative bias in the incidence estimate and a 66 per cent reduction in the width of the 95 per cent confidence interval using this method. We provide open-source software to implement the method that can also be extended for alternate models.

ScoreRel CI: An Excel Program for Computing Confidence Intervals for Commonly Used Score Reliability Coefficients

ERIC Educational Resources Information Center

Barnette, J. Jackson

2005-01-01

An Excel program developed to assist researchers in the determination and presentation of confidence intervals around commonly used score reliability coefficients is described. The software includes programs to determine confidence intervals for Cronbachs alpha, Pearson r-based coefficients such as those used in test-retest and alternate forms…

Calculating Confidence Intervals for Regional Economic Impacts of Recreastion by Bootstrapping Visitor Expenditures

Treesearch

Donald B.K. English

2000-01-01

In this paper I use bootstrap procedures to develop confidence intervals for estimates of total industrial output generated per thousand tourist visits. Mean expenditures from replicated visitor expenditure data included weights to correct for response bias. Impacts were estimated with IMPLAN. Ninety percent interval endpoints were 6 to 16 percent above or below the...

A numerical approach to 14C wiggle-match dating of organic deposits: best fits and confidence intervals

NASA Astrophysics Data System (ADS)

Blaauw, Maarten; Heuvelink, Gerard B. M.; Mauquoy, Dmitri; van der Plicht, Johannes; van Geel, Bas

2003-06-01

14C wiggle-match dating (WMD) of peat deposits uses the non-linear relationship between 14C age and calendar age to match the shape of a sequence of closely spaced peat 14C dates with the 14C calibration curve. A numerical approach to WMD enables the quantitative assessment of various possible wiggle-match solutions and of calendar year confidence intervals for sequences of 14C dates. We assess the assumptions, advantages, and limitations of the method. Several case-studies show that WMD results in more precise chronologies than when individual 14C dates are calibrated. WMD is most successful during periods with major excursions in the 14C calibration curve (e.g., in one case WMD could narrow down confidence intervals from 230 to 36 yr).

Performing Contrast Analysis in Factorial Designs: From NHST to Confidence Intervals and Beyond

PubMed Central

Wiens, Stefan; Nilsson, Mats E.

2016-01-01

Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful because it can be used to test specific questions of central interest in studies with factorial designs. It weighs several means and combines them into one or two sets that can be tested with t tests. The effect size produced by a contrast analysis is simply the difference between means. The CI of the effect size informs directly about direction, hypothesis exclusion, and the relevance of the effects of interest. However, any interpretation in terms of precision or likelihood requires the use of likelihood intervals or credible intervals (Bayesian). These various intervals and even a Bayesian t test can be obtained easily with free software. This tutorial reviews these methods to guide researchers in answering the following questions: When I analyze mean differences in factorial designs, where can I find the effects of central interest, and what can I learn about their effect sizes? PMID:29805179

Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications.

PubMed

du Prel, Jean-Baptist; Hommel, Gerhard; Röhrig, Bernd; Blettner, Maria

2009-05-01

An understanding of p-values and confidence intervals is necessary for the evaluation of scientific articles. This article will inform the reader of the meaning and interpretation of these two statistical concepts. The uses of these two statistical concepts and the differences between them are discussed on the basis of a selective literature search concerning the methods employed in scientific articles. P-values in scientific studies are used to determine whether a null hypothesis formulated before the performance of the study is to be accepted or rejected. In exploratory studies, p-values enable the recognition of any statistically noteworthy findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. This enables conclusions to be drawn about the statistical plausibility and clinical relevance of the study findings. It is often useful for both statistical measures to be reported in scientific articles, because they provide complementary types of information.

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

NASA Astrophysics Data System (ADS)

Zhang, Li

With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman

A comparison of confidence interval methods for the concordance correlation coefficient and intraclass correlation coefficient with small number of raters.

PubMed

Feng, Dai; Svetnik, Vladimir; Coimbra, Alexandre; Baumgartner, Richard

2014-01-01

The intraclass correlation coefficient (ICC) with fixed raters or, equivalently, the concordance correlation coefficient (CCC) for continuous outcomes is a widely accepted aggregate index of agreement in settings with small number of raters. Quantifying the precision of the CCC by constructing its confidence interval (CI) is important in early drug development applications, in particular in qualification of biomarker platforms. In recent years, there have been several new methods proposed for construction of CIs for the CCC, but their comprehensive comparison has not been attempted. The methods consisted of the delta method and jackknifing with and without Fisher's Z-transformation, respectively, and Bayesian methods with vague priors. In this study, we carried out a simulation study, with data simulated from multivariate normal as well as heavier tailed distribution (t-distribution with 5 degrees of freedom), to compare the state-of-the-art methods for assigning CI to the CCC. When the data are normally distributed, the jackknifing with Fisher's Z-transformation (JZ) tended to provide superior coverage and the difference between it and the closest competitor, the Bayesian method with the Jeffreys prior was in general minimal. For the nonnormal data, the jackknife methods, especially the JZ method, provided the coverage probabilities closest to the nominal in contrast to the others which yielded overly liberal coverage. Approaches based upon the delta method and Bayesian method with conjugate prior generally provided slightly narrower intervals and larger lower bounds than others, though this was offset by their poor coverage. Finally, we illustrated the utility of the CIs for the CCC in an example of a wake after sleep onset (WASO) biomarker, which is frequently used in clinical sleep studies of drugs for treatment of insomnia.

On the appropriateness of applying chi-square distribution based confidence intervals to spectral estimates of helicopter flyover data

NASA Technical Reports Server (NTRS)

Rutledge, Charles K.

1988-01-01

The validity of applying chi-square based confidence intervals to far-field acoustic flyover spectral estimates was investigated. Simulated data, using a Kendall series and experimental acoustic data from the NASA/McDonnell Douglas 500E acoustics test, were analyzed. Statistical significance tests to determine the equality of distributions of the simulated and experimental data relative to theoretical chi-square distributions were performed. Bias and uncertainty errors associated with the spectral estimates were easily identified from the data sets. A model relating the uncertainty and bias errors to the estimates resulted, which aided in determining the appropriateness of the chi-square distribution based confidence intervals. Such confidence intervals were appropriate for nontonally associated frequencies of the experimental data but were inappropriate for tonally associated estimate distributions. The appropriateness at the tonally associated frequencies was indicated by the presence of bias error and noncomformity of the distributions to the theoretical chi-square distribution. A technique for determining appropriate confidence intervals at the tonally associated frequencies was suggested.

Assessing Interval Estimation Methods for Hill Model ...

EPA Pesticide Factsheets

The Hill model of concentration-response is ubiquitous in toxicology, perhaps because its parameters directly relate to biologically significant metrics of toxicity such as efficacy and potency. Point estimates of these parameters obtained through least squares regression or maximum likelihood are commonly used in high-throughput risk assessment, but such estimates typically fail to include reliable information concerning confidence in (or precision of) the estimates. To address this issue, we examined methods for assessing uncertainty in Hill model parameter estimates derived from concentration-response data. In particular, using a sample of ToxCast concentration-response data sets, we applied four methods for obtaining interval estimates that are based on asymptotic theory, bootstrapping (two varieties), and Bayesian parameter estimation, and then compared the results. These interval estimation methods generally did not agree, so we devised a simulation study to assess their relative performance. We generated simulated data by constructing four statistical error models capable of producing concentration-response data sets comparable to those observed in ToxCast. We then applied the four interval estimation methods to the simulated data and compared the actual coverage of the interval estimates to the nominal coverage (e.g., 95%) in order to quantify performance of each of the methods in a variety of cases (i.e., different values of the true Hill model paramet

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

NASA Technical Reports Server (NTRS)

Murphy, Patrick Charles

1985-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

Using Confidence Intervals and Recurrence Intervals to Determine Precipitation Delivery Mechanisms Responsible for Mass Wasting Events.

NASA Astrophysics Data System (ADS)

Ulizio, T. P.; Bilbrey, C.; Stoyanoff, N.; Dixon, J. L.

2015-12-01

Mass wasting events are geologic hazards that impact human life and property across a variety of landscapes. These movements can be triggered by tectonic activity, anomalous precipitation events, or both; acting to decrease the factor of safety ratio on a hillslope to the point of failure. There exists an active hazard landscape in the West Boulder River drainage of Park Co., MT in which the mechanisms of slope failure are unknown. It is known that region has not seen significant tectonic activity within the last decade, leaving anomalous precipitation events as the likely trigger for slope failures in the landscape. Precipitation can be delivered to a landscape via rainfall or snow; it was the aim of this study to determine the precipitation delivery mechanism most likely responsible for movements in the West Boulder drainage following the Jungle Wildfire of 2006. Data was compiled from four SNOTEL sites in the surrounding area, spanning 33 years, focusing on, but not limited to; maximum snow water equivalent (SWE) values in a water year, median SWE values on the date which maximum SWE was recorded in a water year, the total precipitation accumulated in a water year, etc. Means were computed and 99% confidence intervals were constructed around these means. Recurrence intervals and exceedance probabilities were computed for maximum SWE values and total precipitation accumulated in a water year to determine water years with anomalous precipitation. It was determined that the water year 2010-2011 received an anomalously high amount of SWE, and snow melt in the spring of this water year likely triggered recent mass waste movements. This data is further supported by Google Earth imagery, showing movements between 2009 and 2011. Return intervals for the maximum SWE value in 2010-11 for the Placer Basin SNOTEL site was 34 years, while return intervals for the Box Canyon and Monument Peak SNOTEL sites were 17.5 and 17 years respectively. Max SWE values lie outside the

WASP (Write a Scientific Paper) using Excel - 6: Standard error and confidence interval.

PubMed

Grech, Victor

2018-03-01

The calculation of descriptive statistics includes the calculation of standard error and confidence interval, an inevitable component of data analysis in inferential statistics. This paper provides pointers as to how to do this in Microsoft Excel™. Copyright © 2018 Elsevier B.V. All rights reserved.

CI2 for creating and comparing confidence-intervals for time-series bivariate plots.

PubMed

Mullineaux, David R

2017-02-01

Currently no method exists for calculating and comparing the confidence-intervals (CI) for the time-series of a bivariate plot. The study's aim was to develop 'CI2' as a method to calculate the CI on time-series bivariate plots, and to identify if the CI between two bivariate time-series overlap. The test data were the knee and ankle angles from 10 healthy participants running on a motorised standard-treadmill and non-motorised curved-treadmill. For a recommended 10+ trials, CI2 involved calculating 95% confidence-ellipses at each time-point, then taking as the CI the points on the ellipses that were perpendicular to the direction vector between the means of two adjacent time-points. Consecutive pairs of CI created convex quadrilaterals, and any overlap of these quadrilaterals at the same time or ±1 frame as a time-lag calculated using cross-correlations, indicated where the two time-series differed. CI2 showed no group differences between left and right legs on both treadmills, but the same legs between treadmills for all participants showed differences of less knee extension on the curved-treadmill before heel-strike. To improve and standardise the use of CI2 it is recommended to remove outlier time-series, use 95% confidence-ellipses, and scale the ellipse by the fixed Chi-square value as opposed to the sample-size dependent F-value. For practical use, and to aid in standardisation or future development of CI2, Matlab code is provided. CI2 provides an effective method to quantify the CI of bivariate plots, and to explore the differences in CI between two bivariate time-series. Copyright © 2016 Elsevier B.V. All rights reserved.

Using confidence intervals to evaluate the focus alignment of spectrograph detector arrays.

PubMed

Sawyer, Travis W; Hawkins, Kyle S; Damento, Michael

2017-06-20

High-resolution spectrographs extract detailed spectral information of a sample and are frequently used in astronomy, laser-induced breakdown spectroscopy, and Raman spectroscopy. These instruments employ dispersive elements such as prisms and diffraction gratings to spatially separate different wavelengths of light, which are then detected by a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) detector array. Precise alignment along the optical axis (focus position) of the detector array is critical to maximize the instrumental resolution; however, traditional approaches of scanning the detector through focus lack a quantitative measure of precision, limiting the repeatability and relying on one's experience. Here we propose a method to evaluate the focus alignment of spectrograph detector arrays by establishing confidence intervals to measure the alignment precision. We show that propagation of uncertainty can be used to estimate the variance in an alignment, thus providing a quantitative and repeatable means to evaluate the precision and confidence of an alignment. We test the approach by aligning the detector array of a prototype miniature echelle spectrograph. The results indicate that the procedure effectively quantifies alignment precision, enabling one to objectively determine when an alignment has reached an acceptable level. This quantitative approach also provides a foundation for further optimization, including automated alignment. Furthermore, the procedure introduced here can be extended to other alignment techniques that rely on numerically fitting data to a model, providing a general framework for evaluating the precision of alignment methods.

Spacecraft utility and the development of confidence intervals for criticality of anomalies

NASA Technical Reports Server (NTRS)

Williams, R. E.

1980-01-01

The concept of spacecraft utility, a measure of its performance in orbit, is discussed and its formulation is described. Performance is defined in terms of the malfunctions that occur and the criticality to the mission of these malfunctions. Different approaches to establishing average or expected values of criticality are discussed and confidence intervals are developed for parameters used in the computation of utility.

Characterizing the Mathematics Anxiety Literature Using Confidence Intervals as a Literature Review Mechanism

ERIC Educational Resources Information Center

Zientek, Linda Reichwein; Yetkiner, Z. Ebrar; Thompson, Bruce

2010-01-01

The authors report the contextualization of effect sizes within mathematics anxiety research, and more specifically within research using the Mathematics Anxiety Rating Scale (MARS) and the MARS for Adolescents (MARS-A). The effect sizes from 45 studies were characterized by graphing confidence intervals (CIs) across studies involving (a) adults…

Tablet potency of Tianeptine in coated tablets by near infrared spectroscopy: model optimisation, calibration transfer and confidence intervals.

PubMed

Boiret, Mathieu; Meunier, Loïc; Ginot, Yves-Michel

2011-02-20

A near infrared (NIR) method was developed for determination of tablet potency of active pharmaceutical ingredient (API) in a complex coated tablet matrix. The calibration set contained samples from laboratory and production scale batches. The reference values were obtained by high performance liquid chromatography (HPLC) and partial least squares (PLS) regression was used to establish a model. The model was challenged by calculating tablet potency of two external test sets. Root mean square errors of prediction were respectively equal to 2.0% and 2.7%. To use this model with a second spectrometer from the production field, a calibration transfer method called piecewise direct standardisation (PDS) was used. After the transfer, the root mean square error of prediction of the first test set was 2.4% compared to 4.0% without transferring the spectra. A statistical technique using bootstrap of PLS residuals was used to estimate confidence intervals of tablet potency calculations. This method requires an optimised PLS model, selection of the bootstrap number and determination of the risk. In the case of a chemical analysis, the tablet potency value will be included within the confidence interval calculated by the bootstrap method. An easy to use graphical interface was developed to easily determine if the predictions, surrounded by minimum and maximum values, are within the specifications defined by the regulatory organisation. Copyright © 2010 Elsevier B.V. All rights reserved.

Performing Contrast Analysis in Factorial Designs: From NHST to Confidence Intervals and Beyond

ERIC Educational Resources Information Center

Wiens, Stefan; Nilsson, Mats E.

2017-01-01

Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful…

SIMREL: Software for Coefficient Alpha and Its Confidence Intervals with Monte Carlo Studies

ERIC Educational Resources Information Center

Yurdugul, Halil

2009-01-01

This article describes SIMREL, a software program designed for the simulation of alpha coefficients and the estimation of its confidence intervals. SIMREL runs on two alternatives. In the first one, if SIMREL is run for a single data file, it performs descriptive statistics, principal components analysis, and variance analysis of the item scores…

How to Avoid Errors in Error Propagation: Prediction Intervals and Confidence Intervals in Forest Biomass

NASA Astrophysics Data System (ADS)

Lilly, P.; Yanai, R. D.; Buckley, H. L.; Case, B. S.; Woollons, R. C.; Holdaway, R. J.; Johnson, J.

2016-12-01

Calculations of forest biomass and elemental content require many measurements and models, each contributing uncertainty to the final estimates. While sampling error is commonly reported, based on replicate plots, error due to uncertainty in the regression used to estimate biomass from tree diameter is usually not quantified. Some published estimates of uncertainty due to the regression models have used the uncertainty in the prediction of individuals, ignoring uncertainty in the mean, while others have propagated uncertainty in the mean while ignoring individual variation. Using the simple case of the calcium concentration of sugar maple leaves, we compare the variation among individuals (the standard deviation) to the uncertainty in the mean (the standard error) and illustrate the declining importance in the prediction of individual concentrations as the number of individuals increases. For allometric models, the analogous statistics are the prediction interval (or the residual variation in the model fit) and the confidence interval (describing the uncertainty in the best fit model). The effect of propagating these two sources of error is illustrated using the mass of sugar maple foliage. The uncertainty in individual tree predictions was large for plots with few trees; for plots with 30 trees or more, the uncertainty in individuals was less important than the uncertainty in the mean. Authors of previously published analyses have reanalyzed their data to show the magnitude of these two sources of uncertainty in scales ranging from experimental plots to entire countries. The most correct analysis will take both sources of uncertainty into account, but for practical purposes, country-level reports of uncertainty in carbon stocks, as required by the IPCC, can ignore the uncertainty in individuals. Ignoring the uncertainty in the mean will lead to exaggerated estimates of confidence in estimates of forest biomass and carbon and nutrient contents.

Proportion of general factor variance in a hierarchical multiple-component measuring instrument: a note on a confidence interval estimation procedure.

PubMed

Raykov, Tenko; Zinbarg, Richard E

2011-05-01

A confidence interval construction procedure for the proportion of explained variance by a hierarchical, general factor in a multi-component measuring instrument is outlined. The method provides point and interval estimates for the proportion of total scale score variance that is accounted for by the general factor, which could be viewed as common to all components. The approach may also be used for testing composite (one-tailed) or simple hypotheses about this proportion, and is illustrated with a pair of examples. ©2010 The British Psychological Society.

Applications of asymptotic confidence intervals with continuity corrections for asymmetric comparisons in noninferiority trials.

PubMed

Soulakova, Julia N; Bright, Brianna C

2013-01-01

A large-sample problem of illustrating noninferiority of an experimental treatment over a referent treatment for binary outcomes is considered. The methods of illustrating noninferiority involve constructing the lower two-sided confidence bound for the difference between binomial proportions corresponding to the experimental and referent treatments and comparing it with the negative value of the noninferiority margin. The three considered methods, Anbar, Falk-Koch, and Reduced Falk-Koch, handle the comparison in an asymmetric way, that is, only the referent proportion out of the two, experimental and referent, is directly involved in the expression for the variance of the difference between two sample proportions. Five continuity corrections (including zero) are considered with respect to each approach. The key properties of the corresponding methods are evaluated via simulations. First, the uncorrected two-sided confidence intervals can, potentially, have smaller coverage probability than the nominal level even for moderately large sample sizes, for example, 150 per group. Next, the 15 testing methods are discussed in terms of their Type I error rate and power. In the settings with a relatively small referent proportion (about 0.4 or smaller), the Anbar approach with Yates' continuity correction is recommended for balanced designs and the Falk-Koch method with Yates' correction is recommended for unbalanced designs. For relatively moderate (about 0.6) and large (about 0.8 or greater) referent proportion, the uncorrected Reduced Falk-Koch method is recommended, although in this case, all methods tend to be over-conservative. These results are expected to be used in the design stage of a noninferiority study when asymmetric comparisons are envisioned. Copyright © 2013 John Wiley & Sons, Ltd.

Prediction of the distillation temperatures of crude oils using ¹H NMR and support vector regression with estimated confidence intervals.

PubMed

Filgueiras, Paulo R; Terra, Luciana A; Castro, Eustáquio V R; Oliveira, Lize M S L; Dias, Júlio C M; Poppi, Ronei J

2015-09-01

This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using (1)H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6°C was obtained in comparison with 15.6°C for PLS, 15.1°C for ePLS and 28.4°C for SVR. The RMSEPs for T50% were 24.2°C, 23.4°C, 22.8°C and 14.4°C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0°C, 39.9°C and 39.9°C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS. Copyright © 2015 Elsevier B.V. All rights reserved.

Experimental optimization of the number of blocks by means of algorithms parameterized by confidence interval in popcorn breeding.

PubMed

Paula, T O M; Marinho, C D; Amaral Júnior, A T; Peternelli, L A; Gonçalves, L S A

2013-06-27

The objective of this study was to determine the optimal number of repetitions to be used in competition trials of popcorn traits related to production and quality, including grain yield and expansion capacity. The experiments were conducted in 3 environments representative of the north and northwest regions of the State of Rio de Janeiro with 10 Brazilian genotypes of popcorn, consisting by 4 commercial hybrids (IAC 112, IAC 125, Zélia, and Jade), 4 improved varieties (BRS Ângela, UFVM-2 Barão de Viçosa, Beija-flor, and Viçosa) and 2 experimental populations (UNB2U-C3 and UNB2U-C4). The experimental design utilized was a randomized complete block design with 7 repetitions. The Bootstrap method was employed to obtain samples of all of the possible combinations within the 7 blocks. Subsequently, the confidence intervals of the parameters of interest were calculated for all simulated data sets. The optimal number of repetition for all of the traits was considered when all of the estimates of the parameters in question were encountered within the confidence interval. The estimates of the number of repetitions varied according to the parameter estimated, variable evaluated, and environment cultivated, ranging from 2 to 7. It is believed that only the expansion capacity traits in the Colégio Agrícola environment (for residual variance and coefficient of variation), and number of ears per plot, in the Itaocara environment (for coefficient of variation) needed 7 repetitions to fall within the confidence interval. Thus, for the 3 studies conducted, we can conclude that 6 repetitions are optimal for obtaining high experimental precision.

A mathematical framework to quantify the masking effect associated with the confidence intervals of measures of disproportionality

PubMed Central

Maignen, François; Hauben, Manfred; Dogné, Jean-Michel

2017-01-01

Background: The lower bound of the 95% confidence interval of measures of disproportionality (Lower95CI) is widely used in signal detection. Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other medicines. The primary objective of our study is to develop and validate a mathematical framework for assessing the masking effect of Lower95CI. Methods: We have developed our new algorithm based on the masking ratio (MR) developed for the measures of disproportionality. A MR for the Lower95CI (MRCI) is proposed. A simulation study to validate this algorithm was also conducted. Results: We have established the existence of a very close mathematical relation between MR and MRCI. For a given drug–event pair, the same product will be responsible for the highest masking effect with the measure of disproportionality and its Lower95CI. The extent of masking is likely to be very similar across the two methods. An important proportion of identical drug–event associations affected by the presence of an important masking effect is revealed by the unmasking exercise, whether the proportional reporting ratio (PRR) or its confidence interval are used. Conclusion: The detection of the masking effect of Lower95CI can be automated. The real benefits of this unmasking in terms of new true-positive signals (rate of true-positive/false-positive) or time gained by the revealing of signals using this method have not been fully assessed. These benefits should be demonstrated in the context of prospective studies. PMID:28845231

Optimal and Most Exact Confidence Intervals for Person Parameters in Item Response Theory Models

ERIC Educational Resources Information Center

Doebler, Anna; Doebler, Philipp; Holling, Heinz

2013-01-01

The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter [theta] is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given…

Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient.

PubMed

Rocco, Paolo; Cilurzo, Francesco; Minghetti, Paola; Vistoli, Giulio; Pedretti, Alessandro

2017-10-01

The data presented in this article are related to the article titled "Molecular Dynamics as a tool for in silico screening of skin permeability" (Rocco et al., 2017) [1]. Knowledge of the confidence interval and maximum theoretical value of the correlation coefficient r can prove useful to estimate the reliability of developed predictive models, in particular when there is great variability in compiled experimental datasets. In this Data in Brief article, data from purposely designed numerical simulations are presented to show how much the maximum r value is worsened by increasing the data uncertainty. The corresponding confidence interval of r is determined by using the Fisher r → Z transform.

Generalized additive models and Lucilia sericata growth: assessing confidence intervals and error rates in forensic entomology.

PubMed

Tarone, Aaron M; Foran, David R

2008-07-01

Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.

Comprehension of confidence intervals - development and piloting of patient information materials for people with multiple sclerosis: qualitative study and pilot randomised controlled trial.

PubMed

Rahn, Anne C; Backhus, Imke; Fuest, Franz; Riemann-Lorenz, Karin; Köpke, Sascha; van de Roemer, Adrianus; Mühlhauser, Ingrid; Heesen, Christoph

2016-09-20

Presentation of confidence intervals alongside information about treatment effects can support informed treatment choices in people with multiple sclerosis. We aimed to develop and pilot-test different written patient information materials explaining confidence intervals in people with relapsing-remitting multiple sclerosis. Further, a questionnaire on comprehension of confidence intervals was developed and piloted. We developed different patient information versions aiming to explain confidence intervals. We used an illustrative example to test three different approaches: (1) short version, (2) "average weight" version and (3) "worm prophylaxis" version. Interviews were conducted using think-aloud and teach-back approaches to test feasibility and analysed using qualitative content analysis. To assess comprehension of confidence intervals, a six-item multiple choice questionnaire was developed and tested in a pilot randomised controlled trial using the online survey software UNIPARK. Here, the average weight version (intervention group) was tested against a standard patient information version on confidence intervals (control group). People with multiple sclerosis were invited to take part using existing mailing-lists of people with multiple sclerosis in Germany and were randomised using the UNIPARK algorithm. Participants were blinded towards group allocation. Primary endpoint was comprehension of confidence intervals, assessed with the six-item multiple choice questionnaire with six points representing perfect knowledge. Feasibility of the patient information versions was tested with 16 people with multiple sclerosis. For the pilot randomised controlled trial, 64 people with multiple sclerosis were randomised (intervention group: n = 36; control group: n = 28). More questions were answered correctly in the intervention group compared to the control group (mean 4.8 vs 3.8, mean difference 1.1 (95 % CI 0.42-1.69), p = 0.002). The questionnaire

A Comparison of Various Stress Rupture Life Models for Orbiter Composite Pressure Vessels and Confidence Intervals

NASA Technical Reports Server (NTRS)

Grimes-Ledesma, Lorie; Murthy, Pappu L. N.; Phoenix, S. Leigh; Glaser, Ronald

2007-01-01

In conjunction with a recent NASA Engineering and Safety Center (NESC) investigation of flight worthiness of Kevlar Overwrapped Composite Pressure Vessels (COPVs) on board the Orbiter, two stress rupture life prediction models were proposed independently by Phoenix and by Glaser. In this paper, the use of these models to determine the system reliability of 24 COPVs currently in service on board the Orbiter is discussed. The models are briefly described, compared to each other, and model parameters and parameter uncertainties are also reviewed to understand confidence in reliability estimation as well as the sensitivities of these parameters in influencing overall predicted reliability levels. Differences and similarities in the various models will be compared via stress rupture reliability curves (stress ratio vs. lifetime plots). Also outlined will be the differences in the underlying model premises, and predictive outcomes. Sources of error and sensitivities in the models will be examined and discussed based on sensitivity analysis and confidence interval determination. Confidence interval results and their implications will be discussed for the models by Phoenix and Glaser.

Confidence intervals for differences between volumes under receiver operating characteristic surfaces (VUS) and generalized Youden indices (GYIs).

PubMed

Yin, Jingjing; Nakas, Christos T; Tian, Lili; Reiser, Benjamin

2018-03-01

This article explores both existing and new methods for the construction of confidence intervals for differences of indices of diagnostic accuracy of competing pairs of biomarkers in three-class classification problems and fills the methodological gaps for both parametric and non-parametric approaches in the receiver operating characteristic surface framework. The most widely used such indices are the volume under the receiver operating characteristic surface and the generalized Youden index. We describe implementation of all methods and offer insight regarding the appropriateness of their use through a large simulation study with different distributional and sample size scenarios. Methods are illustrated using data from the Alzheimer's Disease Neuroimaging Initiative study, where assessment of cognitive function naturally results in a three-class classification setting.

Test Statistics and Confidence Intervals to Establish Noninferiority between Treatments with Ordinal Categorical Data.

PubMed

Zhang, Fanghong; Miyaoka, Etsuo; Huang, Fuping; Tanaka, Yutaka

2015-01-01

The problem for establishing noninferiority is discussed between a new treatment and a standard (control) treatment with ordinal categorical data. A measure of treatment effect is used and a method of specifying noninferiority margin for the measure is provided. Two Z-type test statistics are proposed where the estimation of variance is constructed under the shifted null hypothesis using U-statistics. Furthermore, the confidence interval and the sample size formula are given based on the proposed test statistics. The proposed procedure is applied to a dataset from a clinical trial. A simulation study is conducted to compare the performance of the proposed test statistics with that of the existing ones, and the results show that the proposed test statistics are better in terms of the deviation from nominal level and the power.

Confidence interval estimation of the difference between two sensitivities to the early disease stage.

PubMed

Dong, Tuochuan; Kang, Le; Hutson, Alan; Xiong, Chengjie; Tian, Lili

2014-03-01

Although most of the statistical methods for diagnostic studies focus on disease processes with binary disease status, many diseases can be naturally classified into three ordinal diagnostic categories, that is normal, early stage, and fully diseased. For such diseases, the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. Because the early disease stage is most likely the optimal time window for therapeutic intervention, the sensitivity to the early diseased stage has been suggested as another diagnostic measure. For the purpose of comparing the diagnostic abilities on early disease detection between two markers, it is of interest to estimate the confidence interval of the difference between sensitivities to the early diseased stage. In this paper, we present both parametric and non-parametric methods for this purpose. An extensive simulation study is carried out for a variety of settings for the purpose of evaluating and comparing the performance of the proposed methods. A real example of Alzheimer's disease (AD) is analyzed using the proposed approaches. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Confidence intervals for single-case effect size measures based on randomization test inversion.

PubMed

Michiels, Bart; Heyvaert, Mieke; Meulders, Ann; Onghena, Patrick

2017-02-01

In the current paper, we present a method to construct nonparametric confidence intervals (CIs) for single-case effect size measures in the context of various single-case designs. We use the relationship between a two-sided statistical hypothesis test at significance level α and a 100 (1 - α) % two-sided CI to construct CIs for any effect size measure θ that contain all point null hypothesis θ values that cannot be rejected by the hypothesis test at significance level α. This method of hypothesis test inversion (HTI) can be employed using a randomization test as the statistical hypothesis test in order to construct a nonparametric CI for θ. We will refer to this procedure as randomization test inversion (RTI). We illustrate RTI in a situation in which θ is the unstandardized and the standardized difference in means between two treatments in a completely randomized single-case design. Additionally, we demonstrate how RTI can be extended to other types of single-case designs. Finally, we discuss a few challenges for RTI as well as possibilities when using the method with other effect size measures, such as rank-based nonoverlap indices. Supplementary to this paper, we provide easy-to-use R code, which allows the user to construct nonparametric CIs according to the proposed method.

A confidence interval analysis of sampling effort, sequencing depth, and taxonomic resolution of fungal community ecology in the era of high-throughput sequencing.

PubMed

Oono, Ryoko

2017-01-01

High-throughput sequencing technology has helped microbial community ecologists explore ecological and evolutionary patterns at unprecedented scales. The benefits of a large sample size still typically outweigh that of greater sequencing depths per sample for accurate estimations of ecological inferences. However, excluding or not sequencing rare taxa may mislead the answers to the questions 'how and why are communities different?' This study evaluates the confidence intervals of ecological inferences from high-throughput sequencing data of foliar fungal endophytes as case studies through a range of sampling efforts, sequencing depths, and taxonomic resolutions to understand how technical and analytical practices may affect our interpretations. Increasing sampling size reliably decreased confidence intervals across multiple community comparisons. However, the effects of sequencing depths on confidence intervals depended on how rare taxa influenced the dissimilarity estimates among communities and did not significantly decrease confidence intervals for all community comparisons. A comparison of simulated communities under random drift suggests that sequencing depths are important in estimating dissimilarities between microbial communities under neutral selective processes. Confidence interval analyses reveal important biases as well as biological trends in microbial community studies that otherwise may be ignored when communities are only compared for statistically significant differences.

Evaluating the utility of hexapod species for calculating a confidence interval about a succession based postmortem interval estimate.

PubMed

Perez, Anne E; Haskell, Neal H; Wells, Jeffrey D

2014-08-01

Carrion insect succession patterns have long been used to estimate the postmortem interval (PMI) during a death investigation. However, no published carrion succession study included sufficient replication to calculate a confidence interval about a PMI estimate based on occurrence data. We exposed 53 pig carcasses (16±2.5 kg), near the likely minimum needed for such statistical analysis, at a site in north-central Indiana, USA, over three consecutive summer seasons. Insects and Collembola were sampled daily from each carcass for a total of 14 days, by this time each was skeletonized. The criteria for judging a life stage of a given species to be potentially useful for succession-based PMI estimation were (1) nonreoccurrence (observed during a single period of presence on a corpse), and (2) found in a sufficiently large proportion of carcasses to support a PMI confidence interval. For this data set that proportion threshold is 45/53. Of the 266 species collected and identified, none was nonreoccuring in that each showed at least a gap of one day on a single carcass. If the definition of nonreoccurrence is relaxed to include such a single one-day gap the larval forms of Necrophilaamericana, Fanniascalaris, Cochliomyia macellaria, Phormiaregina, and Luciliaillustris satisfied these two criteria. Adults of Creophilus maxillosus, Necrobiaruficollis, and Necrodessurinamensis were common and showed only a few, single-day gaps in occurrence. C.maxillosus, P.regina, and L.illustris displayed exceptional forensic utility in that they were observed on every carcass. Although these observations were made at a single site during one season of the year, the species we found to be useful have large geographic ranges. We suggest that future carrion insect succession research focus only on a limited set of species with high potential forensic utility so as to reduce sample effort per carcass and thereby enable increased experimental replication. Copyright © 2014 Elsevier Ireland

Empirical likelihood-based confidence intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity.

PubMed

Gengsheng Qin; Davis, Angela E; Jing, Bing-Yi

2011-06-01

For a continuous-scale diagnostic test, it is often of interest to find the range of the sensitivity of the test at the cut-off that yields a desired specificity. In this article, we first define a profile empirical likelihood ratio for the sensitivity of a continuous-scale diagnostic test and show that its limiting distribution is a scaled chi-square distribution. We then propose two new empirical likelihood-based confidence intervals for the sensitivity of the test at a fixed level of specificity by using the scaled chi-square distribution. Simulation studies are conducted to compare the finite sample performance of the newly proposed intervals with the existing intervals for the sensitivity in terms of coverage probability. A real example is used to illustrate the application of the recommended methods.

The confidence-accuracy relationship for eyewitness identification decisions: Effects of exposure duration, retention interval, and divided attention.

PubMed

Palmer, Matthew A; Brewer, Neil; Weber, Nathan; Nagesh, Ambika

2013-03-01

Prior research points to a meaningful confidence-accuracy (CA) relationship for positive identification decisions. However, there are theoretical grounds for expecting that different aspects of the CA relationship (calibration, resolution, and over/underconfidence) might be undermined in some circumstances. This research investigated whether the CA relationship for eyewitness identification decisions is affected by three, forensically relevant variables: exposure duration, retention interval, and divided attention at encoding. In Study 1 (N = 986), a field experiment, we examined the effects of exposure duration (5 s vs. 90 s) and retention interval (immediate testing vs. a 1-week delay) on the CA relationship. In Study 2 (N = 502), we examined the effects of attention during encoding on the CA relationship by reanalyzing data from a laboratory experiment in which participants viewed a stimulus video under full or divided attention conditions and then attempted to identify two targets from separate lineups. Across both studies, all three manipulations affected identification accuracy. The central analyses concerned the CA relation for positive identification decisions. For the manipulations of exposure duration and retention interval, overconfidence was greater in the more difficult conditions (shorter exposure; delayed testing) than the easier conditions. Only the exposure duration manipulation influenced resolution (which was better for 5 s than 90 s), and only the retention interval manipulation affected calibration (which was better for immediate testing than delayed testing). In all experimental conditions, accuracy and diagnosticity increased with confidence, particularly at the upper end of the confidence scale. Implications for theory and forensic settings are discussed.

A confidence interval analysis of sampling effort, sequencing depth, and taxonomic resolution of fungal community ecology in the era of high-throughput sequencing

PubMed Central

2017-01-01

High-throughput sequencing technology has helped microbial community ecologists explore ecological and evolutionary patterns at unprecedented scales. The benefits of a large sample size still typically outweigh that of greater sequencing depths per sample for accurate estimations of ecological inferences. However, excluding or not sequencing rare taxa may mislead the answers to the questions ‘how and why are communities different?’ This study evaluates the confidence intervals of ecological inferences from high-throughput sequencing data of foliar fungal endophytes as case studies through a range of sampling efforts, sequencing depths, and taxonomic resolutions to understand how technical and analytical practices may affect our interpretations. Increasing sampling size reliably decreased confidence intervals across multiple community comparisons. However, the effects of sequencing depths on confidence intervals depended on how rare taxa influenced the dissimilarity estimates among communities and did not significantly decrease confidence intervals for all community comparisons. A comparison of simulated communities under random drift suggests that sequencing depths are important in estimating dissimilarities between microbial communities under neutral selective processes. Confidence interval analyses reveal important biases as well as biological trends in microbial community studies that otherwise may be ignored when communities are only compared for statistically significant differences. PMID:29253889

Knowledge level of effect size statistics, confidence intervals and meta-analysis in Spanish academic psychologists.

PubMed

Badenes-Ribera, Laura; Frias-Navarro, Dolores; Pascual-Soler, Marcos; Monterde-I-Bort, Héctor

2016-11-01

The statistical reform movement and the American Psychological Association (APA) defend the use of estimators of the effect size and its confidence intervals, as well as the interpretation of the clinical significance of the findings. A survey was conducted in which academic psychologists were asked about their behavior in designing and carrying out their studies. The sample was composed of 472 participants (45.8% men). The mean number of years as a university professor was 13.56 years (SD= 9.27). The use of effect-size estimators is becoming generalized, as well as the consideration of meta-analytic studies. However, several inadequate practices still persist. A traditional model of methodological behavior based on statistical significance tests is maintained, based on the predominance of Cohen’s d and the unadjusted R2/η2, which are not immune to outliers or departure from normality and the violations of statistical assumptions, and the under-reporting of confidence intervals of effect-size statistics. The paper concludes with recommendations for improving statistical practice.

’Exact’ Two-Sided Confidence Intervals on Nonnegative Linear Combinations of Variances.

DTIC Science & Technology

1980-07-01

Colorado State University ( 042_402) II. CONTrOLLING OFFICE NAME AND ADDRESS It. REPORT OAT Office of Naval Rsearch -// 1 Jul MjW80 Statistics and...MONNEGATIVE LINEAR COMBINATIONS OF VARIANCES by Franklin A. Graybill Colorado State University and Chih-Ming Wang SPSS Inc. 1. Introduction In a paper to soon...1 + a2’ called the Nodf Led Lace Sample (HLS) confidence interval, is in 2. Aoce-3Ion For DDC TAO u*.- *- -. n c edI Ju.-’I if iction_, i !~BV . . I

A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems. Ph.D. Thesis - George Washington Univ., Apr. 1985

NASA Technical Reports Server (NTRS)

Murphy, P. C.

1986-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates.

Confidence in Altman-Bland plots: a critical review of the method of differences.

PubMed

Ludbrook, John

2010-02-01

1. Altman and Bland argue that the virtue of plotting differences against averages in method-comparison studies is that 95% confidence limits for the differences can be constructed. These allow authors and readers to judge whether one method of measurement could be substituted for another. 2. The technique is often misused. So I have set out, by statistical argument and worked examples, to advise pharmacologists and physiologists how best to construct these limits. 3. First, construct a scattergram of differences on averages, then calculate the line of best fit for the linear regression of differences on averages. If the slope of the regression is shown to differ from zero, there is proportional bias. 4. If there is no proportional bias and if the scatter of differences is uniform (homoscedasticity), construct 'classical' 95% confidence limits. 5. If there is proportional bias yet homoscedasticity, construct hyperbolic 95% confidence limits (prediction interval) around the line of best fit. 6. If there is proportional bias and the scatter of values for differences increases progressively as the average values increase (heteroscedasticity), log-transform the raw values from the two methods and replot differences against averages. If this eliminates proportional bias and heteroscedasticity, construct 'classical' 95% confidence limits. Otherwise, construct horizontal V-shaped 95% confidence limits around the line of best fit of differences on averages or around the weighted least products line of best fit to the original data. 7. In designing a method-comparison study, consult a qualified biostatistician, obey the rules of randomization and make replicate observations.

Uncertainty in Population Growth Rates: Determining Confidence Intervals from Point Estimates of Parameters

PubMed Central

Devenish Nelson, Eleanor S.; Harris, Stephen; Soulsbury, Carl D.; Richards, Shane A.; Stephens, Philip A.

2010-01-01

Background Demographic models are widely used in conservation and management, and their parameterisation often relies on data collected for other purposes. When underlying data lack clear indications of associated uncertainty, modellers often fail to account for that uncertainty in model outputs, such as estimates of population growth. Methodology/Principal Findings We applied a likelihood approach to infer uncertainty retrospectively from point estimates of vital rates. Combining this with resampling techniques and projection modelling, we show that confidence intervals for population growth estimates are easy to derive. We used similar techniques to examine the effects of sample size on uncertainty. Our approach is illustrated using data on the red fox, Vulpes vulpes, a predator of ecological and cultural importance, and the most widespread extant terrestrial mammal. We show that uncertainty surrounding estimated population growth rates can be high, even for relatively well-studied populations. Halving that uncertainty typically requires a quadrupling of sampling effort. Conclusions/Significance Our results compel caution when comparing demographic trends between populations without accounting for uncertainty. Our methods will be widely applicable to demographic studies of many species. PMID:21049049

Bootstrap Signal-to-Noise Confidence Intervals: An Objective Method for Subject Exclusion and Quality Control in ERP Studies

PubMed Central

Parks, Nathan A.; Gannon, Matthew A.; Long, Stephanie M.; Young, Madeleine E.

2016-01-01

Analysis of event-related potential (ERP) data includes several steps to ensure that ERPs meet an appropriate level of signal quality. One such step, subject exclusion, rejects subject data if ERP waveforms fail to meet an appropriate level of signal quality. Subject exclusion is an important quality control step in the ERP analysis pipeline as it ensures that statistical inference is based only upon those subjects exhibiting clear evoked brain responses. This critical quality control step is most often performed simply through visual inspection of subject-level ERPs by investigators. Such an approach is qualitative, subjective, and susceptible to investigator bias, as there are no standards as to what constitutes an ERP of sufficient signal quality. Here, we describe a standardized and objective method for quantifying waveform quality in individual subjects and establishing criteria for subject exclusion. The approach uses bootstrap resampling of ERP waveforms (from a pool of all available trials) to compute a signal-to-noise ratio confidence interval (SNR-CI) for individual subject waveforms. The lower bound of this SNR-CI (SNRLB) yields an effective and objective measure of signal quality as it ensures that ERP waveforms statistically exceed a desired signal-to-noise criterion. SNRLB provides a quantifiable metric of individual subject ERP quality and eliminates the need for subjective evaluation of waveform quality by the investigator. We detail the SNR-CI methodology, establish the efficacy of employing this approach with Monte Carlo simulations, and demonstrate its utility in practice when applied to ERP datasets. PMID:26903849

Confidence Intervals for Effect Sizes: Compliance and Clinical Significance in the "Journal of Consulting and Clinical Psychology"

ERIC Educational Resources Information Center

Odgaard, Eric C.; Fowler, Robert L.

2010-01-01

Objective: In 2005, the "Journal of Consulting and Clinical Psychology" ("JCCP") became the first American Psychological Association (APA) journal to require statistical measures of clinical significance, plus effect sizes (ESs) and associated confidence intervals (CIs), for primary outcomes (La Greca, 2005). As this represents the single largest…

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

Self-confidence and affect responses to short-term sprint interval training.

PubMed

Selmi, Walid; Rebai, Haithem; Chtara, Mokhtar; Naceur, Abdelmajid; Sahli, Sonia

2018-05-01

The study aimed to investigate the effects of repeated sprint (RS) training on somatic anxiety (SA), cognitive anxiety (CA), self-confidence (SC), rating of perceived exertion (RPE) and repeated sprint ability (RSA) indicators in elite young soccer players. Thirty elite soccer players in the first football league (age: 17.8±0.9years) volunteered to participate in this study. They were randomly assigned to one of two groups: a repeated sprint training group (RST-G; n=15) and a control group (CON-G; n=15). RST-G participated in 6weeks of intensive training based on RS (6×(20+20m) runs, with 20s passive recovery interval between sprints, 3 times/week). Before and after the 6-week intervention, all participants performed a RSA test and completed a Competitive Scale Anxiety Inventory (CSAI-2) and the RPE. After training RST-G showed a very significant (p<0.000) increase in RSA total time performance relative to controls. Despite the faster sprint pace, the RPE also decreased significantly (p<0.005) in RST-G, and their self confidence was significantly greater (p<0.01), while the cognitive (p<0.01) and somatic (p<0.000) components of their anxiety state decreased. When practiced regularly, short bouts of sprint exercises improve anaerobic performance associated with a reduction in anxiety state and an increase in SC which may probably boost competitive performance. Copyright © 2018 Elsevier Inc. All rights reserved.

Confidence Intervals for Laboratory Sonic Boom Annoyance Tests

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Christian, Andrew

2016-01-01

Commercial supersonic flight is currently forbidden over land because sonic booms have historically caused unacceptable annoyance levels in overflown communities. NASA is providing data and expertise to noise regulators as they consider relaxing the ban for future quiet supersonic aircraft. One deliverable NASA will provide is a predictive model for indoor annoyance to aid in setting an acceptable quiet sonic boom threshold. A laboratory study was conducted to determine how indoor vibrations caused by sonic booms affect annoyance judgments. The test method required finding the point of subjective equality (PSE) between sonic boom signals that cause vibrations and signals not causing vibrations played at various amplitudes. This presentation focuses on a few statistical techniques for estimating the interval around the PSE. The techniques examined are the Delta Method, Parametric and Nonparametric Bootstrapping, and Bayesian Posterior Estimation.

Confidence limit calculation for antidotal potency ratio derived from lethal dose 50

PubMed Central

Manage, Ananda; Petrikovics, Ilona

2013-01-01

AIM: To describe confidence interval calculation for antidotal potency ratios using bootstrap method. METHODS: We can easily adapt the nonparametric bootstrap method which was invented by Efron to construct confidence intervals in such situations like this. The bootstrap method is a resampling method in which the bootstrap samples are obtained by resampling from the original sample. RESULTS: The described confidence interval calculation using bootstrap method does not require the sampling distribution antidotal potency ratio. This can serve as a substantial help for toxicologists, who are directed to employ the Dixon up-and-down method with the application of lower number of animals to determine lethal dose 50 values for characterizing the investigated toxic molecules and eventually for characterizing the antidotal protections by the test antidotal systems. CONCLUSION: The described method can serve as a useful tool in various other applications. Simplicity of the method makes it easier to do the calculation using most of the programming software packages. PMID:25237618

A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size.

PubMed

Feng, Dai; Cortese, Giuliana; Baumgartner, Richard

2017-12-01

The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann-Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.

R package to estimate intracluster correlation coefficient with confidence interval for binary data.

PubMed

Chakraborty, Hrishikesh; Hossain, Akhtar

2018-03-01

The Intracluster Correlation Coefficient (ICC) is a major parameter of interest in cluster randomized trials that measures the degree to which responses within the same cluster are correlated. There are several types of ICC estimators and its confidence intervals (CI) suggested in the literature for binary data. Studies have compared relative weaknesses and advantages of ICC estimators as well as its CI for binary data and suggested situations where one is advantageous in practical research. The commonly used statistical computing systems currently facilitate estimation of only a very few variants of ICC and its CI. To address the limitations of current statistical packages, we developed an R package, ICCbin, to facilitate estimating ICC and its CI for binary responses using different methods. The ICCbin package is designed to provide estimates of ICC in 16 different ways including analysis of variance methods, moments based estimation, direct probabilistic methods, correlation based estimation, and resampling method. CI of ICC is estimated using 5 different methods. It also generates cluster binary data using exchangeable correlation structure. ICCbin package provides two functions for users. The function rcbin() generates cluster binary data and the function iccbin() estimates ICC and it's CI. The users can choose appropriate ICC and its CI estimate from the wide selection of estimates from the outputs. The R package ICCbin presents very flexible and easy to use ways to generate cluster binary data and to estimate ICC and it's CI for binary response using different methods. The package ICCbin is freely available for use with R from the CRAN repository (https://cran.r-project.org/package=ICCbin). We believe that this package can be a very useful tool for researchers to design cluster randomized trials with binary outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Reliability and Confidence Interval Analysis of a CMC Turbine Stator Vane

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Gyekenyesi, John P.; Mital, Subodh K.

2008-01-01

an economical manner. The methods to accurately determine the service life of an engine component with associated variability have become increasingly difficult. This results, in part, from the complex missions which are now routinely considered during the design process. These missions include large variations of multi-axial stresses and temperatures experienced by critical engine parts. There is a need for a convenient design tool that can accommodate various loading conditions induced by engine operating environments, and material data with their associated uncertainties to estimate the minimum predicted life of a structural component. A probabilistic composite micromechanics technique in combination with woven composite micromechanics, structural analysis and Fast Probability Integration (FPI) techniques has been used to evaluate the maximum stress and its probabilistic distribution in a CMC turbine stator vane. Furthermore, input variables causing scatter are identified and ranked based upon their sensitivity magnitude. Since the measured data for the ceramic matrix composite properties is very limited, obtaining a probabilistic distribution with their corresponding parameters is difficult. In case of limited data, confidence bounds are essential to quantify the uncertainty associated with the distribution. Usually 90 and 95% confidence intervals are computed for material properties. Failure properties are then computed with the confidence bounds. Best estimates and the confidence bounds on the best estimate of the cumulative probability function for R-S (strength - stress) are plotted. The methodologies and the results from these analyses will be discussed in the presentation.

Chosen interval methods for solving linear interval systems with special type of matrix

NASA Astrophysics Data System (ADS)

Szyszka, Barbara

2013-10-01

The paper is devoted to chosen direct interval methods for solving linear interval systems with special type of matrix. This kind of matrix: band matrix with a parameter, from finite difference problem is obtained. Such linear systems occur while solving one dimensional wave equation (Partial Differential Equations of hyperbolic type) by using the central difference interval method of the second order. Interval methods are constructed so as the errors of method are enclosed in obtained results, therefore presented linear interval systems contain elements that determining the errors of difference method. The chosen direct algorithms have been applied for solving linear systems because they have no errors of method. All calculations were performed in floating-point interval arithmetic.

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

PubMed

Ishwaran, Hemant; Lu, Min

2018-06-04

Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric measure of variable importance (VIMP). A current limitation of VIMP, however, is that no systematic method exists for estimating its variance. As a solution, we propose a subsampling approach that can be used to estimate the variance of VIMP and for constructing confidence intervals. The method is general enough that it can be applied to many useful settings, including regression, classification, and survival problems. Using extensive simulations, we demonstrate the effectiveness of the subsampling estimator and in particular find that the delete-d jackknife variance estimator, a close cousin, is especially effective under low subsampling rates due to its bias correction properties. These 2 estimators are highly competitive when compared with the .164 bootstrap estimator, a modified bootstrap procedure designed to deal with ties in out-of-sample data. Most importantly, subsampling is computationally fast, thus making it especially attractive for big data settings. Copyright © 2018 John Wiley & Sons, Ltd.

Neutron multiplicity counting: Confidence intervals for reconstruction parameters

DOE PAGES

Verbeke, Jerome M.

2016-03-09

From nuclear materials accountability to homeland security, the need for improved nuclear material detection, assay, and authentication has grown over the past decades. Starting in the 1940s, neutron multiplicity counting techniques have enabled quantitative evaluation of masses and multiplications of fissile materials. In this paper, we propose a new method to compute uncertainties on these parameters using a model-based sequential Bayesian processor, resulting in credible regions in the fissile material mass and multiplication space. These uncertainties will enable us to evaluate quantitatively proposed improvements to the theoretical fission chain model. Additionally, because the processor can calculate uncertainties in real time,more » it is a useful tool in applications such as portal monitoring: monitoring can stop as soon as a preset confidence of non-threat is reached.« less

UNDERSTANDING SYSTEMATIC MEASUREMENT ERROR IN THERMAL-OPTICAL ANALYSIS FOR PM BLACK CARBON USING RESPONSE SURFACES AND SURFACE CONFIDENCE INTERVALS

EPA Science Inventory

Results from a NIST-EPA Interagency Agreement on Understanding Systematic Measurement Error in Thermal-Optical Analysis for PM Black Carbon Using Response Surfaces and Surface Confidence Intervals will be presented at the American Association for Aerosol Research (AAAR) 24th Annu...

Accurate estimation of normal incidence absorption coefficients with confidence intervals using a scanning laser Doppler vibrometer

NASA Astrophysics Data System (ADS)

Vuye, Cedric; Vanlanduit, Steve; Guillaume, Patrick

2009-06-01

When using optical measurements of the sound fields inside a glass tube, near the material under test, to estimate the reflection and absorption coefficients, not only these acoustical parameters but also confidence intervals can be determined. The sound fields are visualized using a scanning laser Doppler vibrometer (SLDV). In this paper the influence of different test signals on the quality of the results, obtained with this technique, is examined. The amount of data gathered during one measurement scan makes a thorough statistical analysis possible leading to the knowledge of confidence intervals. The use of a multi-sine, constructed on the resonance frequencies of the test tube, shows to be a very good alternative for the traditional periodic chirp. This signal offers the ability to obtain data for multiple frequencies in one measurement, without the danger of a low signal-to-noise ratio. The variability analysis in this paper clearly shows the advantages of the proposed multi-sine compared to the periodic chirp. The measurement procedure and the statistical analysis are validated by measuring the reflection ratio at a closed end and comparing the results with the theoretical value. Results of the testing of two building materials (an acoustic ceiling tile and linoleum) are presented and compared to supplier data.

The Logic of Summative Confidence

ERIC Educational Resources Information Center

Gugiu, P. Cristian

2007-01-01

The constraints of conducting evaluations in real-world settings often necessitate the implementation of less than ideal designs. Unfortunately, the standard method for estimating the precision of a result (i.e., confidence intervals [CI]) cannot be used for evaluative conclusions that are derived from multiple indicators, measures, and data…

Practical Advice on Calculating Confidence Intervals for Radioprotection Effects and Reducing Animal Numbers in Radiation Countermeasure Experiments

PubMed Central

Landes, Reid D.; Lensing, Shelly Y.; Kodell, Ralph L.; Hauer-Jensen, Martin

2014-01-01

The dose of a substance that causes death in P% of a population is called an LDP, where LD stands for lethal dose. In radiation research, a common LDP of interest is the radiation dose that kills 50% of the population by a specified time, i.e., lethal dose 50 or LD50. When comparing LD50 between two populations, relative potency is the parameter of interest. In radiation research, this is commonly known as the dose reduction factor (DRF). Unfortunately, statistical inference on dose reduction factor is seldom reported. We illustrate how to calculate confidence intervals for dose reduction factor, which may then be used for statistical inference. Further, most dose reduction factor experiments use hundreds, rather than tens of animals. Through better dosing strategies and the use of a recently available sample size formula, we also show how animal numbers may be reduced while maintaining high statistical power. The illustrations center on realistic examples comparing LD50 values between a radiation countermeasure group and a radiation-only control. We also provide easy-to-use spreadsheets for sample size calculations and confidence interval calculations, as well as SAS® and R code for the latter. PMID:24164553

Adjusted Wald Confidence Interval for a Difference of Binomial Proportions Based on Paired Data

ERIC Educational Resources Information Center

Bonett, Douglas G.; Price, Robert M.

2012-01-01

Adjusted Wald intervals for binomial proportions in one-sample and two-sample designs have been shown to perform about as well as the best available methods. The adjusted Wald intervals are easy to compute and have been incorporated into introductory statistics courses. An adjusted Wald interval for paired binomial proportions is proposed here and…

Estimation and confidence intervals for empirical mixing distributions

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1995-01-01

Questions regarding collections of parameter estimates can frequently be expressed in terms of an empirical mixing distribution (EMD). This report discusses empirical Bayes estimation of an EMD, with emphasis on the construction of interval estimates. Estimation of the EMD is accomplished by substitution of estimates of prior parameters in the posterior mean of the EMD. This procedure is examined in a parametric model (the normal-normal mixture) and in a semi-parametric model. In both cases, the empirical Bayes bootstrap of Laird and Louis (1987, Journal of the American Statistical Association 82, 739-757) is used to assess the variability of the estimated EMD arising from the estimation of prior parameters. The proposed methods are applied to a meta-analysis of population trend estimates for groups of birds.

Confidence Intervals for the Probability of Superiority Effect Size Measure and the Area under a Receiver Operating Characteristic Curve

ERIC Educational Resources Information Center

Ruscio, John; Mullen, Tara

2012-01-01

It is good scientific practice to the report an appropriate estimate of effect size and a confidence interval (CI) to indicate the precision with which a population effect was estimated. For comparisons of 2 independent groups, a probability-based effect size estimator (A) that is equal to the area under a receiver operating characteristic curve…

The Precision of Effect Size Estimation From Published Psychological Research: Surveying Confidence Intervals.

PubMed

Brand, Andrew; Bradley, Michael T

2016-02-01

Confidence interval ( CI) widths were calculated for reported Cohen's d standardized effect sizes and examined in two automated surveys of published psychological literature. The first survey reviewed 1,902 articles from Psychological Science. The second survey reviewed a total of 5,169 articles from across the following four APA journals: Journal of Abnormal Psychology, Journal of Applied Psychology, Journal of Experimental Psychology: Human Perception and Performance, and Developmental Psychology. The median CI width for d was greater than 1 in both surveys. Hence, CI widths were, as Cohen (1994) speculated, embarrassingly large. Additional exploratory analyses revealed that CI widths varied across psychological research areas and that CI widths were not discernably decreasing over time. The theoretical implications of these findings are discussed along with ways of reducing the CI widths and thus improving precision of effect size estimation.

Confidence bounds for normal and lognormal distribution coefficients of variation

Treesearch

Steve Verrill

2003-01-01

This paper compares the so-called exact approach for obtaining confidence intervals on normal distribution coefficients of variation to approximate methods. Approximate approaches were found to perform less well than the exact approach for large coefficients of variation and small sample sizes. Web-based computer programs are described for calculating confidence...

Standard Errors and Confidence Intervals of Norm Statistics for Educational and Psychological Tests.

PubMed

Oosterhuis, Hannah E M; van der Ark, L Andries; Sijtsma, Klaas

2016-11-14

Norm statistics allow for the interpretation of scores on psychological and educational tests, by relating the test score of an individual test taker to the test scores of individuals belonging to the same gender, age, or education groups, et cetera. Given the uncertainty due to sampling error, one would expect researchers to report standard errors for norm statistics. In practice, standard errors are seldom reported; they are either unavailable or derived under strong distributional assumptions that may not be realistic for test scores. We derived standard errors for four norm statistics (standard deviation, percentile ranks, stanine boundaries and Z-scores) under the mild assumption that the test scores are multinomially distributed. A simulation study showed that the standard errors were unbiased and that corresponding Wald-based confidence intervals had good coverage. Finally, we discuss the possibilities for applying the standard errors in practical test use in education and psychology. The procedure is provided via the R function check.norms, which is available in the mokken package.

Factorial-based response-surface modeling with confidence intervals for optimizing thermal-optical transmission analysis of atmospheric black carbon.

PubMed

Conny, J M; Norris, G A; Gould, T R

2009-03-09

Thermal-optical transmission (TOT) analysis measures black carbon (BC) in atmospheric aerosol on a fibrous filter. The method pyrolyzes organic carbon (OC) and employs laser light absorption to distinguish BC from the pyrolyzed OC; however, the instrument does not necessarily separate the two physically. In addition, a comprehensive temperature protocol for the analysis based on the Beer-Lambert Law remains elusive. Here, empirical response-surface modeling was used to show how the temperature protocol in TOT analysis can be modified to distinguish pyrolyzed OC from BC based on the Beer-Lambert Law. We determined the apparent specific absorption cross sections for pyrolyzed OC (sigma(Char)) and BC (sigma(BC)), which accounted for individual absorption enhancement effects within the filter. Response-surface models of these cross sections were derived from a three-factor central-composite factorial experimental design: temperature and duration of the high-temperature step in the helium phase, and the heating increase in the helium-oxygen phase. The response surface for sigma(BC), which varied with instrument conditions, revealed a ridge indicating the correct conditions for OC pyrolysis in helium. The intersection of the sigma(BC) and sigma(Char) surfaces indicated the conditions where the cross sections were equivalent, satisfying an important assumption upon which the method relies. 95% confidence interval surfaces defined a confidence region for a range of pyrolysis conditions. Analyses of wintertime samples from Seattle, WA revealed a temperature between 830 degrees C and 850 degrees C as most suitable for the helium high-temperature step lasting 150s. However, a temperature as low as 750 degrees C could not be rejected statistically.

A spreadsheet template compatible with Microsoft Excel and iWork Numbers that returns the simultaneous confidence intervals for all pairwise differences between multiple sample means.

PubMed

Brown, Angus M

2010-04-01

The objective of the method described in this paper is to develop a spreadsheet template for the purpose of comparing multiple sample means. An initial analysis of variance (ANOVA) test on the data returns F--the test statistic. If F is larger than the critical F value drawn from the F distribution at the appropriate degrees of freedom, convention dictates rejection of the null hypothesis and allows subsequent multiple comparison testing to determine where the inequalities between the sample means lie. A variety of multiple comparison methods are described that return the 95% confidence intervals for differences between means using an inclusive pairwise comparison of the sample means. 2009 Elsevier Ireland Ltd. All rights reserved.

Contraceptive confidence and timing of first birth in Moldova: an event history analysis of retrospective data.

PubMed

Lyons-Amos, Mark; Padmadas, Sabu S; Durrant, Gabriele B

2014-08-11

To test the contraceptive confidence hypothesis in a modern context. The hypothesis is that women using effective or modern contraceptive methods have increased contraceptive confidence and hence a shorter interval between marriage and first birth than users of ineffective or traditional methods. We extend the hypothesis to incorporate the role of abortion, arguing that it acts as a substitute for contraception in the study context. Moldova, a country in South-East Europe. Moldova exhibits high use of traditional contraceptive methods and abortion compared with other European countries. Data are from a secondary analysis of the 2005 Moldovan Demographic and Health Survey, a nationally representative sample survey. 5377 unmarried women were selected. The outcome measure was the interval between marriage and first birth. This was modelled using a piecewise-constant hazard regression, with abortion and contraceptive method types as primary variables along with relevant sociodemographic controls. Women with high contraceptive confidence (modern method users) have a higher cumulative hazard of first birth 36 months following marriage (0.88 (0.87 to 0.89)) compared with women with low contraceptive confidence (traditional method users, cumulative hazard: 0.85 (0.84 to 0.85)). This is consistent with the contraceptive confidence hypothesis. There is a higher cumulative hazard of first birth among women with low (0.80 (0.79 to 0.80)) and moderate abortion propensities (0.76 (0.75 to 0.77)) than women with no abortion propensity (0.73 (0.72 to 0.74)) 24 months after marriage. Effective contraceptive use tends to increase contraceptive confidence and is associated with a shorter interval between marriage and first birth. Increased use of abortion also tends to increase contraceptive confidence and shorten birth duration, although this effect is non-linear-women with a very high use of abortion tend to have lengthy intervals between marriage and first birth. Published by

Interval sampling methods and measurement error: a computer simulation.

PubMed

Wirth, Oliver; Slaven, James; Taylor, Matthew A

2014-01-01

A simulation study was conducted to provide a more thorough account of measurement error associated with interval sampling methods. A computer program simulated the application of momentary time sampling, partial-interval recording, and whole-interval recording methods on target events randomly distributed across an observation period. The simulation yielded measures of error for multiple combinations of observation period, interval duration, event duration, and cumulative event duration. The simulations were conducted up to 100 times to yield measures of error variability. Although the present simulation confirmed some previously reported characteristics of interval sampling methods, it also revealed many new findings that pertain to each method's inherent strengths and weaknesses. The analysis and resulting error tables can help guide the selection of the most appropriate sampling method for observation-based behavioral assessments. © Society for the Experimental Analysis of Behavior.

Reliable prediction intervals with regression neural networks.

PubMed

Papadopoulos, Harris; Haralambous, Haris

2011-10-01

This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the data are independent and identically distributed (i.i.d.). We evaluate the proposed method on four benchmark datasets and on the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC measurements collected over a period of 11 years. Our experimental results show that the prediction intervals produced by our method are both well calibrated and tight enough to be useful in practice. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sex-specific reference intervals of hematologic and biochemical analytes in Sprague-Dawley rats using the nonparametric rank percentile method.

PubMed

He, Qili; Su, Guoming; Liu, Keliang; Zhang, Fangcheng; Jiang, Yong; Gao, Jun; Liu, Lida; Jiang, Zhongren; Jin, Minwu; Xie, Huiping

2017-01-01

Hematologic and biochemical analytes of Sprague-Dawley rats are commonly used to determine effects that were induced by treatment and to evaluate organ dysfunction in toxicological safety assessments, but reference intervals have not been well established for these analytes. Reference intervals as presently defined for these analytes in Sprague-Dawley rats have not used internationally recommended statistical method nor stratified by sex. Thus, we aimed to establish sex-specific reference intervals for hematologic and biochemical parameters in Sprague-Dawley rats according to Clinical and Laboratory Standards Institute C28-A3 and American Society for Veterinary Clinical Pathology guideline. Hematology and biochemistry blood samples were collected from 500 healthy Sprague-Dawley rats (250 males and 250 females) in the control groups. We measured 24 hematologic analytes with the Sysmex XT-2100i analyzer, 9 biochemical analytes with the Olympus AU400 analyzer. We then determined statistically relevant sex partitions and calculated reference intervals, including corresponding 90% confidence intervals, using nonparametric rank percentile method. We observed that most hematologic and biochemical analytes of Sprague-Dawley rats were significantly influenced by sex. Males had higher hemoglobin, hematocrit, red blood cell count, red cell distribution width, mean corpuscular volume, mean corpuscular hemoglobin, white blood cell count, neutrophils, lymphocytes, monocytes, percentage of neutrophils, percentage of monocytes, alanine aminotransferase, aspartate aminotransferase, and triglycerides compared to females. Females had higher mean corpuscular hemoglobin concentration, plateletcrit, platelet count, eosinophils, percentage of lymphocytes, percentage of eosinophils, creatinine, glucose, total cholesterol and urea compared to males. Sex partition was required for most hematologic and biochemical analytes in Sprague-Dawley rats. We established sex-specific reference

Indirect methods for reference interval determination - review and recommendations.

PubMed

Jones, Graham R D; Haeckel, Rainer; Loh, Tze Ping; Sikaris, Ken; Streichert, Thomas; Katayev, Alex; Barth, Julian H; Ozarda, Yesim

2018-04-19

Reference intervals are a vital part of the information supplied by clinical laboratories to support interpretation of numerical pathology results such as are produced in clinical chemistry and hematology laboratories. The traditional method for establishing reference intervals, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. An alternative approach is to perform analysis of results generated as part of routine pathology testing and using appropriate statistical techniques to determine reference intervals. This is known as the indirect approach. This paper from a working group of the International Federation of Clinical Chemistry (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) aims to summarize current thinking on indirect approaches to reference intervals. The indirect approach has some major potential advantages compared with direct methods. The processes are faster, cheaper and do not involve patient inconvenience, discomfort or the risks associated with generating new patient health information. Indirect methods also use the same preanalytical and analytical techniques used for patient management and can provide very large numbers for assessment. Limitations to the indirect methods include possible effects of diseased subpopulations on the derived interval. The IFCC C-RIDL aims to encourage the use of indirect methods to establish and verify reference intervals, to promote publication of such intervals with clear explanation of the process used and also to support the development of improved statistical techniques for these studies.

Estimation of parameters of dose volume models and their confidence limits

NASA Astrophysics Data System (ADS)

van Luijk, P.; Delvigne, T. C.; Schilstra, C.; Schippers, J. M.

2003-07-01

Predictions of the normal-tissue complication probability (NTCP) for the ranking of treatment plans are based on fits of dose-volume models to clinical and/or experimental data. In the literature several different fit methods are used. In this work frequently used methods and techniques to fit NTCP models to dose response data for establishing dose-volume effects, are discussed. The techniques are tested for their usability with dose-volume data and NTCP models. Different methods to estimate the confidence intervals of the model parameters are part of this study. From a critical-volume (CV) model with biologically realistic parameters a primary dataset was generated, serving as the reference for this study and describable by the NTCP model. The CV model was fitted to this dataset. From the resulting parameters and the CV model, 1000 secondary datasets were generated by Monte Carlo simulation. All secondary datasets were fitted to obtain 1000 parameter sets of the CV model. Thus the 'real' spread in fit results due to statistical spreading in the data is obtained and has been compared with estimates of the confidence intervals obtained by different methods applied to the primary dataset. The confidence limits of the parameters of one dataset were estimated using the methods, employing the covariance matrix, the jackknife method and directly from the likelihood landscape. These results were compared with the spread of the parameters, obtained from the secondary parameter sets. For the estimation of confidence intervals on NTCP predictions, three methods were tested. Firstly, propagation of errors using the covariance matrix was used. Secondly, the meaning of the width of a bundle of curves that resulted from parameters that were within the one standard deviation region in the likelihood space was investigated. Thirdly, many parameter sets and their likelihood were used to create a likelihood-weighted probability distribution of the NTCP. It is concluded that for the

Confidence intervals for effect sizes: compliance and clinical significance in the Journal of Consulting and clinical Psychology.

PubMed

Odgaard, Eric C; Fowler, Robert L

2010-06-01

In 2005, the Journal of Consulting and Clinical Psychology (JCCP) became the first American Psychological Association (APA) journal to require statistical measures of clinical significance, plus effect sizes (ESs) and associated confidence intervals (CIs), for primary outcomes (La Greca, 2005). As this represents the single largest editorial effort to improve statistical reporting practices in any APA journal in at least a decade, in this article we investigate the efficacy of that change. All intervention studies published in JCCP in 2003, 2004, 2007, and 2008 were reviewed. Each article was coded for method of clinical significance, type of ES, and type of associated CI, broken down by statistical test (F, t, chi-square, r/R(2), and multivariate modeling). By 2008, clinical significance compliance was 75% (up from 31%), with 94% of studies reporting some measure of ES (reporting improved for individual statistical tests ranging from eta(2) = .05 to .17, with reasonable CIs). Reporting of CIs for ESs also improved, although only to 40%. Also, the vast majority of reported CIs used approximations, which become progressively less accurate for smaller sample sizes and larger ESs (cf. Algina & Kessleman, 2003). Changes are near asymptote for ESs and clinical significance, but CIs lag behind. As CIs for ESs are required for primary outcomes, we show how to compute CIs for the vast majority of ESs reported in JCCP, with an example of how to use CIs for ESs as a method to assess clinical significance.

Evaluating the Impact of Guessing and Its Interactions With Other Test Characteristics on Confidence Interval Procedures for Coefficient Alpha

PubMed Central

Paek, Insu

2015-01-01

The effect of guessing on the point estimate of coefficient alpha has been studied in the literature, but the impact of guessing and its interactions with other test characteristics on the interval estimators for coefficient alpha has not been fully investigated. This study examined the impact of guessing and its interactions with other test characteristics on four confidence interval (CI) procedures for coefficient alpha in terms of coverage rate (CR), length, and the degree of asymmetry of CI estimates. In addition, interval estimates of coefficient alpha when data follow the essentially tau-equivalent condition were investigated as a supplement to the case of dichotomous data with examinee guessing. For dichotomous data with guessing, the results did not reveal salient negative effects of guessing and its interactions with other test characteristics (sample size, test length, coefficient alpha levels) on CR and the degree of asymmetry, but the effect of guessing was salient as a main effect and an interaction effect with sample size on the length of the CI estimates, making longer CI estimates as guessing increases, especially when combined with a small sample size. Other important effects (e.g., CI procedures on CR) are also discussed. PMID:29795863

Concept analysis: confidence/self-confidence.

PubMed

Perry, Patricia

2011-01-01

Confidence and self-confidence are crucial practice elements in nursing education and practice. Nurse educators should have an understanding of the concept of confidence in order to assist in the accomplishment of nursing students and their learning of technical and nontechnical skills. With the aim of facilitating trusted care of patients in the healthcare setting, nursing professionals must exhibit confidence, and, as such, clarification and analysis of its meaning is necessary. The purpose of this analysis is to provide clarity to the meaning of the concept confidence/self-confidence, while gaining a more comprehensive understanding of its attributes, antecedents, and consequences. Walker and Avant's eight-step method of concept analysis was utilized for the framework of the analysis process with model, contrary, borderline, and related cases presented along with attributes, antecedents, consequences, and empirical referents identified. Understanding both the individualized development of confidence among prelicensure nursing students and the role of the nurse educator in the development of confident nursing practice, nurse educators can assist students in the development of confidence and competency. Future research surrounding the nature and development of confidence/self-confidence in the prelicensure nursing student experiencing human patient simulation sessions would assist to help educators further promote the development of confidence. © 2011 Wiley Periodicals, Inc.

Line segment confidence region-based string matching method for map conflation

NASA Astrophysics Data System (ADS)

Huh, Yong; Yang, Sungchul; Ga, Chillo; Yu, Kiyun; Shi, Wenzhong

2013-04-01

In this paper, a method to detect corresponding point pairs between polygon object pairs with a string matching method based on a confidence region model of a line segment is proposed. The optimal point edit sequence to convert the contour of a target object into that of a reference object was found by the string matching method which minimizes its total error cost, and the corresponding point pairs were derived from the edit sequence. Because a significant amount of apparent positional discrepancies between corresponding objects are caused by spatial uncertainty and their confidence region models of line segments are therefore used in the above matching process, the proposed method obtained a high F-measure for finding matching pairs. We applied this method for built-up area polygon objects in a cadastral map and a topographical map. Regardless of their different mapping and representation rules and spatial uncertainties, the proposed method with a confidence level at 0.95 showed a matching result with an F-measure of 0.894.

Interval MULTIMOORA method with target values of attributes based on interval distance and preference degree: biomaterials selection

NASA Astrophysics Data System (ADS)

Hafezalkotob, Arian; Hafezalkotob, Ashkan

2017-06-01

A target-based MADM method covers beneficial and non-beneficial attributes besides target values for some attributes. Such techniques are considered as the comprehensive forms of MADM approaches. Target-based MADM methods can also be used in traditional decision-making problems in which beneficial and non-beneficial attributes only exist. In many practical selection problems, some attributes have given target values. The values of decision matrix and target-based attributes can be provided as intervals in some of such problems. Some target-based decision-making methods have recently been developed; however, a research gap exists in the area of MADM techniques with target-based attributes under uncertainty of information. We extend the MULTIMOORA method for solving practical material selection problems in which material properties and their target values are given as interval numbers. We employ various concepts of interval computations to reduce degeneration of uncertain data. In this regard, we use interval arithmetic and introduce innovative formula for interval distance of interval numbers to create interval target-based normalization technique. Furthermore, we use a pairwise preference matrix based on the concept of degree of preference of interval numbers to calculate the maximum, minimum, and ranking of these numbers. Two decision-making problems regarding biomaterials selection of hip and knee prostheses are discussed. Preference degree-based ranking lists for subordinate parts of the extended MULTIMOORA method are generated by calculating the relative degrees of preference for the arranged assessment values of the biomaterials. The resultant rankings for the problem are compared with the outcomes of other target-based models in the literature.

Serum prolactin revisited: parametric reference intervals and cross platform evaluation of polyethylene glycol precipitation-based methods for discrimination between hyperprolactinemia and macroprolactinemia.

PubMed

Overgaard, Martin; Pedersen, Susanne Møller

2017-10-26

Hyperprolactinemia diagnosis and treatment is often compromised by the presence of biologically inactive and clinically irrelevant higher-molecular-weight complexes of prolactin, macroprolactin. The objective of this study was to evaluate the performance of two macroprolactin screening regimes across commonly used automated immunoassay platforms. Parametric total and monomeric gender-specific reference intervals were determined for six immunoassay methods using female (n=96) and male sera (n=127) from healthy donors. The reference intervals were validated using 27 hyperprolactinemic and macroprolactinemic sera, whose presence of monomeric and macroforms of prolactin were determined using gel filtration chromatography (GFC). Normative data for six prolactin assays included the range of values (2.5th-97.5th percentiles). Validation sera (hyperprolactinemic and macroprolactinemic; n=27) showed higher discordant classification [mean=2.8; 95% confidence interval (CI) 1.2-4.4] for the monomer reference interval method compared to the post-polyethylene glycol (PEG) recovery cutoff method (mean=1.8; 95% CI 0.8-2.8). The two monomer/macroprolactin discrimination methods did not differ significantly (p=0.089). Among macroprolactinemic sera evaluated by both discrimination methods, the Cobas and Architect/Kryptor prolactin assays showed the lowest and the highest number of misclassifications, respectively. Current automated immunoassays for prolactin testing require macroprolactin screening methods based on PEG precipitation in order to discriminate truly from falsely elevated serum prolactin. While the recovery cutoff and monomeric reference interval macroprolactin screening methods demonstrate similar discriminative ability, the latter method also provides the clinician with an easy interpretable monomeric prolactin concentration along with a monomeric reference interval.

Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects.

PubMed

Biesanz, Jeremy C; Falk, Carl F; Savalei, Victoria

2010-08-06

Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses ( Baron & Kenny, 1986 ; Sobel, 1982 ) have in recent years been supplemented by computationally intensive methods such as bootstrapping, the distribution of the product methods, and hierarchical Bayesian Markov chain Monte Carlo (MCMC) methods. These different approaches for assessing mediation are illustrated using data from Dunn, Biesanz, Human, and Finn (2007). However, little is known about how these methods perform relative to each other, particularly in more challenging situations, such as with data that are incomplete and/or nonnormal. This article presents an extensive Monte Carlo simulation evaluating a host of approaches for assessing mediation. We examine Type I error rates, power, and coverage. We study normal and nonnormal data as well as complete and incomplete data. In addition, we adapt a method, recently proposed in statistical literature, that does not rely on confidence intervals (CIs) to test the null hypothesis of no indirect effect. The results suggest that the new inferential method-the partial posterior p value-slightly outperforms existing ones in terms of maintaining Type I error rates while maximizing power, especially with incomplete data. Among confidence interval approaches, the bias-corrected accelerated (BC a ) bootstrapping approach often has inflated Type I error rates and inconsistent coverage and is not recommended; In contrast, the bootstrapped percentile confidence interval and the hierarchical Bayesian MCMC method perform best overall, maintaining Type I error rates, exhibiting reasonable power, and producing stable and accurate coverage rates.

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.

PubMed

Aitchison, Laurence; Bang, Dan; Bahrami, Bahador; Latham, Peter E

2015-10-01

Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people's confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality.

Determination of confidence limits for experiments with low numbers of counts. [Poisson-distributed photon counts from astrophysical sources

NASA Technical Reports Server (NTRS)

Kraft, Ralph P.; Burrows, David N.; Nousek, John A.

1991-01-01

Two different methods, classical and Bayesian, for determining confidence intervals involving Poisson-distributed data are compared. Particular consideration is given to cases where the number of counts observed is small and is comparable to the mean number of background counts. Reasons for preferring the Bayesian over the classical method are given. Tables of confidence limits calculated by the Bayesian method are provided for quick reference.

Robotic fish tracking method based on suboptimal interval Kalman filter

NASA Astrophysics Data System (ADS)

Tong, Xiaohong; Tang, Chao

2017-11-01

Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

Using Confidence Interval-Based Estimation of Relevance to Select Social-Cognitive Determinants for Behavior Change Interventions.

PubMed

Crutzen, Rik; Peters, Gjalt-Jorn Ygram; Noijen, Judith

2017-01-01

When developing an intervention aimed at behavior change, one of the crucial steps in the development process is to select the most relevant social-cognitive determinants. These determinants can be seen as the buttons one needs to push to establish behavior change. Insight into these determinants is needed to select behavior change methods (i.e., general behavior change techniques that are applied in an intervention) in the development process. Therefore, a study on determinants is often conducted as formative research in the intervention development process. Ideally, all relevant determinants identified in such a study are addressed by an intervention. However, when developing a behavior change intervention, there are limits in terms of, for example, resources available for intervention development and the amount of content that participants of an intervention can be exposed to. Hence, it is important to select those determinants that are most relevant to the target behavior as these determinants should be addressed in an intervention. The aim of the current paper is to introduce a novel approach to select the most relevant social-cognitive determinants and use them in intervention development. This approach is based on visualization of confidence intervals for the means and correlation coefficients for all determinants simultaneously. This visualization facilitates comparison, which is necessary when making selections. By means of a case study on the determinants of using a high dose of 3,4-methylenedioxymethamphetamine (commonly known as ecstasy), we illustrate this approach. We provide a freely available tool to facilitate the analyses needed in this approach.

The association between short interpregnancy interval and preterm birth in Louisiana: a comparison of methods.

PubMed

Howard, Elizabeth J; Harville, Emily; Kissinger, Patricia; Xiong, Xu

2013-07-01

There is growing interest in the application of propensity scores (PS) in epidemiologic studies, especially within the field of reproductive epidemiology. This retrospective cohort study assesses the impact of a short interpregnancy interval (IPI) on preterm birth and compares the results of the conventional logistic regression analysis with analyses utilizing a PS. The study included 96,378 singleton infants from Louisiana birth certificate data (1995-2007). Five regression models designed for methods comparison are presented. Ten percent (10.17 %) of all births were preterm; 26.83 % of births were from a short IPI. The PS-adjusted model produced a more conservative estimate of the exposure variable compared to the conventional logistic regression method (β-coefficient: 0.21 vs. 0.43), as well as a smaller standard error (0.024 vs. 0.028), odds ratio and 95 % confidence intervals [1.15 (1.09, 1.20) vs. 1.23 (1.17, 1.30)]. The inclusion of more covariate and interaction terms in the PS did not change the estimates of the exposure variable. This analysis indicates that PS-adjusted regression may be appropriate for validation of conventional methods in a large dataset with a fairly common outcome. PS's may be beneficial in producing more precise estimates, especially for models with many confounders and effect modifiers and where conventional adjustment with logistic regression is unsatisfactory. Short intervals between pregnancies are associated with preterm birth in this population, according to either technique. Birth spacing is an issue that women have some control over. Educational interventions, including birth control, should be applied during prenatal visits and following delivery.

Four applications of permutation methods to testing a single-mediator model.

PubMed

Taylor, Aaron B; MacKinnon, David P

2012-09-01

Four applications of permutation tests to the single-mediator model are described and evaluated in this study. Permutation tests work by rearranging data in many possible ways in order to estimate the sampling distribution for the test statistic. The four applications to mediation evaluated here are the permutation test of ab, the permutation joint significance test, and the noniterative and iterative permutation confidence intervals for ab. A Monte Carlo simulation study was used to compare these four tests with the four best available tests for mediation found in previous research: the joint significance test, the distribution of the product test, and the percentile and bias-corrected bootstrap tests. We compared the different methods on Type I error, power, and confidence interval coverage. The noniterative permutation confidence interval for ab was the best performer among the new methods. It successfully controlled Type I error, had power nearly as good as the most powerful existing methods, and had better coverage than any existing method. The iterative permutation confidence interval for ab had lower power than do some existing methods, but it performed better than any other method in terms of coverage. The permutation confidence interval methods are recommended when estimating a confidence interval is a primary concern. SPSS and SAS macros that estimate these confidence intervals are provided.

Prediction Interval Development for Wind-Tunnel Balance Check-Loading

NASA Technical Reports Server (NTRS)

Landman, Drew; Toro, Kenneth G.; Commo, Sean A.; Lynn, Keith C.

2014-01-01

Results from the Facility Analysis Verification and Operational Reliability project revealed a critical gap in capability in ground-based aeronautics research applications. Without a standardized process for check-loading the wind-tunnel balance or the model system, the quality of the aerodynamic force data collected varied significantly between facilities. A prediction interval is required in order to confirm a check-loading. The prediction interval provides an expected upper and lower bound on balance load prediction at a given confidence level. A method has been developed which accounts for sources of variability due to calibration and check-load application. The prediction interval method of calculation and a case study demonstrating its use is provided. Validation of the methods is demonstrated for the case study based on the probability of capture of confirmation points.

Performance analysis of complex repairable industrial systems using PSO and fuzzy confidence interval based methodology.

PubMed

Garg, Harish

2013-03-01

The main objective of the present paper is to propose a methodology for analyzing the behavior of the complex repairable industrial systems. In real-life situations, it is difficult to find the most optimal design policies for MTBF (mean time between failures), MTTR (mean time to repair) and related costs by utilizing available resources and uncertain data. For this, the availability-cost optimization model has been constructed for determining the optimal design parameters for improving the system design efficiency. The uncertainties in the data related to each component of the system are estimated with the help of fuzzy and statistical methodology in the form of the triangular fuzzy numbers. Using these data, the various reliability parameters, which affects the system performance, are obtained in the form of the fuzzy membership function by the proposed confidence interval based fuzzy Lambda-Tau (CIBFLT) methodology. The computed results by CIBFLT are compared with the existing fuzzy Lambda-Tau methodology. Sensitivity analysis on the system MTBF has also been addressed. The methodology has been illustrated through a case study of washing unit, the main part of the paper industry. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Abstract: Inference and Interval Estimation for Indirect Effects With Latent Variable Models.

PubMed

Falk, Carl F; Biesanz, Jeremy C

2011-11-30

Models specifying indirect effects (or mediation) and structural equation modeling are both popular in the social sciences. Yet relatively little research has compared methods that test for indirect effects among latent variables and provided precise estimates of the effectiveness of different methods. This simulation study provides an extensive comparison of methods for constructing confidence intervals and for making inferences about indirect effects with latent variables. We compared the percentile (PC) bootstrap, bias-corrected (BC) bootstrap, bias-corrected accelerated (BC a ) bootstrap, likelihood-based confidence intervals (Neale & Miller, 1997), partial posterior predictive (Biesanz, Falk, and Savalei, 2010), and joint significance tests based on Wald tests or likelihood ratio tests. All models included three reflective latent variables representing the independent, dependent, and mediating variables. The design included the following fully crossed conditions: (a) sample size: 100, 200, and 500; (b) number of indicators per latent variable: 3 versus 5; (c) reliability per set of indicators: .7 versus .9; (d) and 16 different path combinations for the indirect effect (α = 0, .14, .39, or .59; and β = 0, .14, .39, or .59). Simulations were performed using a WestGrid cluster of 1680 3.06GHz Intel Xeon processors running R and OpenMx. Results based on 1,000 replications per cell and 2,000 resamples per bootstrap method indicated that the BC and BC a bootstrap methods have inflated Type I error rates. Likelihood-based confidence intervals and the PC bootstrap emerged as methods that adequately control Type I error and have good coverage rates.

Five-band microwave radiometer system for noninvasive brain temperature measurement in newborn babies: Phantom experiment and confidence interval

NASA Astrophysics Data System (ADS)

Sugiura, T.; Hirata, H.; Hand, J. W.; van Leeuwen, J. M. J.; Mizushina, S.

2011-10-01

Clinical trials of hypothermic brain treatment for newborn babies are currently hindered by the difficulty in measuring deep brain temperatures. As one of the possible methods for noninvasive and continuous temperature monitoring that is completely passive and inherently safe is passive microwave radiometry (MWR). We have developed a five-band microwave radiometer system with a single dual-polarized, rectangular waveguide antenna operating within the 1-4 GHz range and a method for retrieving the temperature profile from five radiometric brightness temperatures. This paper addresses (1) the temperature calibration for five microwave receivers, (2) the measurement experiment using a phantom model that mimics the temperature profile in a newborn baby, and (3) the feasibility for noninvasive monitoring of deep brain temperatures. Temperature resolutions were 0.103, 0.129, 0.138, 0.105 and 0.111 K for 1.2, 1.65, 2.3, 3.0 and 3.6 GHz receivers, respectively. The precision of temperature estimation (2σ confidence interval) was about 0.7°C at a 5-cm depth from the phantom surface. Accuracy, which is the difference between the estimated temperature using this system and the measured temperature by a thermocouple at a depth of 5 cm, was about 2°C. The current result is not satisfactory for clinical application because the clinical requirement for accuracy must be better than 1°C for both precision and accuracy at a depth of 5 cm. Since a couple of possible causes for this inaccuracy have been identified, we believe that the system can take a step closer to the clinical application of MWR for hypothermic rescue treatment.

A Simple Method for Deriving the Confidence Regions for the Penalized Cox's Model via the Minimand Perturbation.

PubMed

Lin, Chen-Yen; Halabi, Susan

2017-01-01

We propose a minimand perturbation method to derive the confidence regions for the regularized estimators for the Cox's proportional hazards model. Although the regularized estimation procedure produces a more stable point estimate, it remains challenging to provide an interval estimator or an analytic variance estimator for the associated point estimate. Based on the sandwich formula, the current variance estimator provides a simple approximation, but its finite sample performance is not entirely satisfactory. Besides, the sandwich formula can only provide variance estimates for the non-zero coefficients. In this article, we present a generic description for the perturbation method and then introduce a computation algorithm using the adaptive least absolute shrinkage and selection operator (LASSO) penalty. Through simulation studies, we demonstrate that our method can better approximate the limiting distribution of the adaptive LASSO estimator and produces more accurate inference compared with the sandwich formula. The simulation results also indicate the possibility of extending the applications to the adaptive elastic-net penalty. We further demonstrate our method using data from a phase III clinical trial in prostate cancer.

The prognostic value of the QT interval and QT interval dispersion in all-cause and cardiac mortality and morbidity in a population of Danish citizens.

PubMed

Elming, H; Holm, E; Jun, L; Torp-Pedersen, C; Køber, L; Kircshoff, M; Malik, M; Camm, J

1998-09-01

To evaluate the prognostic value of the QT interval and QT interval dispersion in total and in cardiovascular mortality, as well as in cardiac morbidity, in a general population. The QT interval was measured in all leads from a standard 12-lead ECG in a random sample of 1658 women and 1797 men aged 30-60 years. QT interval dispersion was calculated from the maximal difference between QT intervals in any two leads. All cause mortality over 13 years, and cardiovascular mortality as well as cardiac morbidity over 11 years, were the main outcome parameters. Subjects with a prolonged QT interval (430 ms or more) or prolonged QT interval dispersion (80 ms or more) were at higher risk of cardiovascular death and cardiac morbidity than subjects whose QT interval was less than 360 ms, or whose QT interval dispersion was less than 30 ms. Cardiovascular death relative risk ratios, adjusted for age, gender, myocardial infarct, angina pectoris, diabetes mellitus, arterial hypertension, smoking habits, serum cholesterol level, and heart rate were 2.9 for the QT interval (95% confidence interval 1.1-7.8) and 4.4 for QT interval dispersion (95% confidence interval 1.0-19-1). Fatal and non-fatal cardiac morbidity relative risk ratios were similar, at 2.7 (95% confidence interval 1.4-5.5) for the QT interval and 2.2 (95% confidence interval 1.1-4.0) for QT interval dispersion. Prolongation of the QT interval and QT interval dispersion independently affected the prognosis of cardiovascular mortality and cardiac fatal and non-fatal morbidity in a general population over 11 years.

Small sample mediation testing: misplaced confidence in bootstrapped confidence intervals.

PubMed

Koopman, Joel; Howe, Michael; Hollenbeck, John R; Sin, Hock-Peng

2015-01-01

Bootstrapping is an analytical tool commonly used in psychology to test the statistical significance of the indirect effect in mediation models. Bootstrapping proponents have particularly advocated for its use for samples of 20-80 cases. This advocacy has been heeded, especially in the Journal of Applied Psychology, as researchers are increasingly utilizing bootstrapping to test mediation with samples in this range. We discuss reasons to be concerned with this escalation, and in a simulation study focused specifically on this range of sample sizes, we demonstrate not only that bootstrapping has insufficient statistical power to provide a rigorous hypothesis test in most conditions but also that bootstrapping has a tendency to exhibit an inflated Type I error rate. We then extend our simulations to investigate an alternative empirical resampling method as well as a Bayesian approach and demonstrate that they exhibit comparable statistical power to bootstrapping in small samples without the associated inflated Type I error. Implications for researchers testing mediation hypotheses in small samples are presented. For researchers wishing to use these methods in their own research, we have provided R syntax in the online supplemental materials. (c) 2015 APA, all rights reserved.

Razonamiento de Estudiantes Universitarios sobre Variabilidad e Intervalos de Confianza en un Contexto Inferencial Informal = University Students' Reasoning on Variability and Confidence Intervals in Inferential Informal Context

ERIC Educational Resources Information Center

Inzunsa Cazares, Santiago

2016-01-01

This article presents the results of a qualitative research with a group of 15 university students of social sciences on informal inferential reasoning developed in a computer environment on concepts involved in the confidence intervals. The results indicate that students developed a correct reasoning about sampling variability and visualized…

A Simple Method for Deriving the Confidence Regions for the Penalized Cox’s Model via the Minimand Perturbation†

PubMed Central

Lin, Chen-Yen; Halabi, Susan

2017-01-01

We propose a minimand perturbation method to derive the confidence regions for the regularized estimators for the Cox’s proportional hazards model. Although the regularized estimation procedure produces a more stable point estimate, it remains challenging to provide an interval estimator or an analytic variance estimator for the associated point estimate. Based on the sandwich formula, the current variance estimator provides a simple approximation, but its finite sample performance is not entirely satisfactory. Besides, the sandwich formula can only provide variance estimates for the non-zero coefficients. In this article, we present a generic description for the perturbation method and then introduce a computation algorithm using the adaptive least absolute shrinkage and selection operator (LASSO) penalty. Through simulation studies, we demonstrate that our method can better approximate the limiting distribution of the adaptive LASSO estimator and produces more accurate inference compared with the sandwich formula. The simulation results also indicate the possibility of extending the applications to the adaptive elastic-net penalty. We further demonstrate our method using data from a phase III clinical trial in prostate cancer. PMID:29326496

An approach for sample size determination of average bioequivalence based on interval estimation.

PubMed

Chiang, Chieh; Hsiao, Chin-Fu

2017-03-30

In 1992, the US Food and Drug Administration declared that two drugs demonstrate average bioequivalence (ABE) if the log-transformed mean difference of pharmacokinetic responses lies in (-0.223, 0.223). The most widely used approach for assessing ABE is the two one-sided tests procedure. More specifically, ABE is concluded when a 100(1 - 2α) % confidence interval for mean difference falls within (-0.223, 0.223). As known, bioequivalent studies are usually conducted by crossover design. However, in the case that the half-life of a drug is long, a parallel design for the bioequivalent study may be preferred. In this study, a two-sided interval estimation - such as Satterthwaite's, Cochran-Cox's, or Howe's approximations - is used for assessing parallel ABE. We show that the asymptotic joint distribution of the lower and upper confidence limits is bivariate normal, and thus the sample size can be calculated based on the asymptotic power so that the confidence interval falls within (-0.223, 0.223). Simulation studies also show that the proposed method achieves sufficient empirical power. A real example is provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Solving the interval type-2 fuzzy polynomial equation using the ranking method

NASA Astrophysics Data System (ADS)

Rahman, Nurhakimah Ab.; Abdullah, Lazim

2014-07-01

Polynomial equations with trapezoidal and triangular fuzzy numbers have attracted some interest among researchers in mathematics, engineering and social sciences. There are some methods that have been developed in order to solve these equations. In this study we are interested in introducing the interval type-2 fuzzy polynomial equation and solving it using the ranking method of fuzzy numbers. The ranking method concept was firstly proposed to find real roots of fuzzy polynomial equation. Therefore, the ranking method is applied to find real roots of the interval type-2 fuzzy polynomial equation. We transform the interval type-2 fuzzy polynomial equation to a system of crisp interval type-2 fuzzy polynomial equation. This transformation is performed using the ranking method of fuzzy numbers based on three parameters, namely value, ambiguity and fuzziness. Finally, we illustrate our approach by numerical example.

Exploring new graduate nurse confidence in interprofessional collaboration: a mixed methods study.

PubMed

Pfaff, Kathryn A; Baxter, Pamela E; Jack, Susan M; Ploeg, Jenny

2014-08-01

Confidence is required for effective engagement in interprofessional collaboration. New graduate nurses often lack confidence in interprofessional interactions, and this may compromise the delivery of safe and effective healthcare. The overall objective of this study was to explore new graduate nurse confidence in interprofessional collaboration. An explanatory sequential mixed methods design was used. New graduate nurses from Ontario, Canada (N=514) completed a cross-sectional descriptive survey in 2012. The survey measured perceived confidence in interprofessional collaboration, and it included items that were proposed to have a relationship with new graduate nurse confidence in interprofessional collaboration. Follow-up qualitative telephone interviews were conducted with 16 new graduate nurses. The quantitative findings suggested that several factors have a positive relationship with new graduate nurse confidence in interprofessional collaboration: availability and accessibility of manager, availability and accessibility of educator, number of different disciplines worked with daily, number of team strategies, and satisfaction with team. The qualitative phase supported the quantitative findings and also provided new information about factors that facilitated and challenged new graduate nurse confidence when engaging in interprofessional collaboration. The facilitators were: experience, knowledge, respect, supportive relationships, and opportunities to collaborate. Challenges included: lack of experience, lack of knowledge, communication challenges, and balancing practice expectations. The overall findings relate to team and organizational support, and new graduate nurse development. Interventions that provide support for interprofessional collaboration at the team and organizational levels, and develop new graduate nurse knowledge and experiences regarding collaborative practice, are essential for enhancing new graduate nurse confidence in interprofessional

Confidence Intervals for Squared Semipartial Correlation Coefficients: The Effect of Nonnormality

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2010-01-01

The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…

Age-dependent biochemical quantities: an approach for calculating reference intervals.

PubMed

Bjerner, J

2007-01-01

A parametric method is often preferred when calculating reference intervals for biochemical quantities, as non-parametric methods are less efficient and require more observations/study subjects. Parametric methods are complicated, however, because of three commonly encountered features. First, biochemical quantities seldom display a Gaussian distribution, and there must either be a transformation procedure to obtain such a distribution or a more complex distribution has to be used. Second, biochemical quantities are often dependent on a continuous covariate, exemplified by rising serum concentrations of MUC1 (episialin, CA15.3) with increasing age. Third, outliers often exert substantial influence on parametric estimations and therefore need to be excluded before calculations are made. The International Federation of Clinical Chemistry (IFCC) currently recommends that confidence intervals be calculated for the reference centiles obtained. However, common statistical packages allowing for the adjustment of a continuous covariate do not make this calculation. In the method described in the current study, Tukey's fence is used to eliminate outliers and two-stage transformations (modulus-exponential-normal) in order to render Gaussian distributions. Fractional polynomials are employed to model functions for mean and standard deviations dependent on a covariate, and the model is selected by maximum likelihood. Confidence intervals are calculated for the fitted centiles by combining parameter estimation and sampling uncertainties. Finally, the elimination of outliers was made dependent on covariates by reiteration. Though a good knowledge of statistical theory is needed when performing the analysis, the current method is rewarding because the results are of practical use in patient care.

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading

Method of high precision interval measurement in pulse laser ranging system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Lv, Xin-yuan; Mao, Jin-jin; Liu, Wei; Yang, Dong

2013-09-01

Laser ranging is suitable for laser system, for it has the advantage of high measuring precision, fast measuring speed,no cooperative targets and strong resistance to electromagnetic interference,the measuremen of laser ranging is the key paremeters affecting the performance of the whole system.The precision of the pulsed laser ranging system was decided by the precision of the time interval measurement, the principle structure of laser ranging system was introduced, and a method of high precision time interval measurement in pulse laser ranging system was established in this paper.Based on the analysis of the factors which affected the precision of range measure,the pulse rising edges discriminator was adopted to produce timing mark for the start-stop time discrimination,and the TDC-GP2 high precision interval measurement system based on TMS320F2812 DSP was designed to improve the measurement precision.Experimental results indicate that the time interval measurement method in this paper can obtain higher range accuracy. Compared with the traditional time interval measurement system,the method simplifies the system design and reduce the influence of bad weather conditions,furthermore,it satisfies the requirements of low costs and miniaturization.

Confidence estimation for quantitative photoacoustic imaging

NASA Astrophysics Data System (ADS)

Gröhl, Janek; Kirchner, Thomas; Maier-Hein, Lena

2018-02-01

Quantification of photoacoustic (PA) images is one of the major challenges currently being addressed in PA research. Tissue properties can be quantified by correcting the recorded PA signal with an estimation of the corresponding fluence. Fluence estimation itself, however, is an ill-posed inverse problem which usually needs simplifying assumptions to be solved with state-of-the-art methods. These simplifications, as well as noise and artifacts in PA images reduce the accuracy of quantitative PA imaging (PAI). This reduction in accuracy is often localized to image regions where the assumptions do not hold true. This impedes the reconstruction of functional parameters when averaging over entire regions of interest (ROI). Averaging over a subset of voxels with a high accuracy would lead to an improved estimation of such parameters. To achieve this, we propose a novel approach to the local estimation of confidence in quantitative reconstructions of PA images. It makes use of conditional probability densities to estimate confidence intervals alongside the actual quantification. It encapsulates an estimation of the errors introduced by fluence estimation as well as signal noise. We validate the approach using Monte Carlo generated data in combination with a recently introduced machine learning-based approach to quantitative PAI. Our experiments show at least a two-fold improvement in quantification accuracy when evaluating on voxels with high confidence instead of thresholding signal intensity.

A dimension-wise analysis method for the structural-acoustic system with interval parameters

NASA Astrophysics Data System (ADS)

Xu, Menghui; Du, Jianke; Wang, Chong; Li, Yunlong

2017-04-01

The interval structural-acoustic analysis is mainly accomplished by interval and subinterval perturbation methods. Potential limitations for these intrusive methods include overestimation or interval translation effect for the former and prohibitive computational cost for the latter. In this paper, a dimension-wise analysis method is thus proposed to overcome these potential limitations. In this method, a sectional curve of the system response surface along each input dimensionality is firstly extracted, the minimal and maximal points of which are identified based on its Legendre polynomial approximation. And two input vectors, i.e. the minimal and maximal input vectors, are dimension-wisely assembled by the minimal and maximal points of all sectional curves. Finally, the lower and upper bounds of system response are computed by deterministic finite element analysis at the two input vectors. Two numerical examples are studied to demonstrate the effectiveness of the proposed method and show that, compared to the interval and subinterval perturbation method, a better accuracy is achieved without much compromise on efficiency by the proposed method, especially for nonlinear problems with large interval parameters.

The effect of covariate mean differences on the standard error and confidence interval for the comparison of treatment means.

PubMed

Liu, Xiaofeng Steven

2011-05-01

The use of covariates is commonly believed to reduce the unexplained error variance and the standard error for the comparison of treatment means, but the reduction in the standard error is neither guaranteed nor uniform over different sample sizes. The covariate mean differences between the treatment conditions can inflate the standard error of the covariate-adjusted mean difference and can actually produce a larger standard error for the adjusted mean difference than that for the unadjusted mean difference. When the covariate observations are conceived of as randomly varying from one study to another, the covariate mean differences can be related to a Hotelling's T(2) . Using this Hotelling's T(2) statistic, one can always find a minimum sample size to achieve a high probability of reducing the standard error and confidence interval width for the adjusted mean difference. ©2010 The British Psychological Society.

Annoyance from transportation noise: relationships with exposure metrics DNL and DENL and their confidence intervals.

PubMed Central

Miedema, H M; Oudshoorn, C G

2001-01-01

We present a model of the distribution of noise annoyance with the mean varying as a function of the noise exposure. Day-night level (DNL) and day-evening-night level (DENL) were used as noise descriptors. Because the entire annoyance distribution has been modeled, any annoyance measure that summarizes this distribution can be calculated from the model. We fitted the model to data from noise annoyance studies for aircraft, road traffic, and railways separately. Polynomial approximations of relationships implied by the model for the combinations of the following exposure and annoyance measures are presented: DNL or DENL, and percentage "highly annoyed" (cutoff at 72 on a scale of 0-100), percentage "annoyed" (cutoff at 50 on a scale of 0-100), or percentage (at least) "a little annoyed" (cutoff at 28 on a scale of 0-100). These approximations are very good, and they are easier to use for practical calculations than the model itself, because the model involves a normal distribution. Our results are based on the same data set that was used earlier to establish relationships between DNL and percentage highly annoyed. In this paper we provide better estimates of the confidence intervals due to the improved model of the relationship between annoyance and noise exposure. Moreover, relationships using descriptors other than DNL and percentage highly annoyed, which are presented here, have not been established earlier on the basis of a large dataset. PMID:11335190

Exact nonparametric confidence bands for the survivor function.

PubMed

Matthews, David

2013-10-12

A method to produce exact simultaneous confidence bands for the empirical cumulative distribution function that was first described by Owen, and subsequently corrected by Jager and Wellner, is the starting point for deriving exact nonparametric confidence bands for the survivor function of any positive random variable. We invert a nonparametric likelihood test of uniformity, constructed from the Kaplan-Meier estimator of the survivor function, to obtain simultaneous lower and upper bands for the function of interest with specified global confidence level. The method involves calculating a null distribution and associated critical value for each observed sample configuration. However, Noe recursions and the Van Wijngaarden-Decker-Brent root-finding algorithm provide the necessary tools for efficient computation of these exact bounds. Various aspects of the effect of right censoring on these exact bands are investigated, using as illustrations two observational studies of survival experience among non-Hodgkin's lymphoma patients and a much larger group of subjects with advanced lung cancer enrolled in trials within the North Central Cancer Treatment Group. Monte Carlo simulations confirm the merits of the proposed method of deriving simultaneous interval estimates of the survivor function across the entire range of the observed sample. This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. It was begun while the author was visiting the Department of Statistics, University of Auckland, and completed during a subsequent sojourn at the Medical Research Council Biostatistics Unit in Cambridge. The support of both institutions, in addition to that of NSERC and the University of Waterloo, is greatly appreciated.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

PubMed Central

Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.

2011-01-01

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, as this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referred to as Statistical Tools for AMT tag Confidence (STAC). STAC additionally provides a Uniqueness Probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download as both a command line and a Windows graphical application. PMID:21692516

New Approaches to Robust Confidence Intervals for Location: A Simulation Study.

DTIC Science & Technology

1984-06-01

obtain a denominator for the test statistic. Those statistics based on location estimates derived from Hampel’s redescending influence function or v...defined an influence function for a test in terms of the behavior of its P-values when the data are sampled from a model distribution modified by point...proposal could be used for interval estimation as well as hypothesis testing, the extension is immediate. Once an influence function has been defined

Solutions of interval type-2 fuzzy polynomials using a new ranking method

NASA Astrophysics Data System (ADS)

Rahman, Nurhakimah Ab.; Abdullah, Lazim; Ghani, Ahmad Termimi Ab.; Ahmad, Noor'Ani

2015-10-01

A few years ago, a ranking method have been introduced in the fuzzy polynomial equations. Concept of the ranking method is proposed to find actual roots of fuzzy polynomials (if exists). Fuzzy polynomials are transformed to system of crisp polynomials, performed by using ranking method based on three parameters namely, Value, Ambiguity and Fuzziness. However, it was found that solutions based on these three parameters are quite inefficient to produce answers. Therefore in this study a new ranking method have been developed with the aim to overcome the inherent weakness. The new ranking method which have four parameters are then applied in the interval type-2 fuzzy polynomials, covering the interval type-2 of fuzzy polynomial equation, dual fuzzy polynomial equations and system of fuzzy polynomials. The efficiency of the new ranking method then numerically considered in the triangular fuzzy numbers and the trapezoidal fuzzy numbers. Finally, the approximate solutions produced from the numerical examples indicate that the new ranking method successfully produced actual roots for the interval type-2 fuzzy polynomials.

A contracting-interval program for the Danilewski method. Ph.D. Thesis - Va. Univ.

NASA Technical Reports Server (NTRS)

Harris, J. D.

1971-01-01

The concept of contracting-interval programs is applied to finding the eigenvalues of a matrix. The development is a three-step process in which (1) a program is developed for the reduction of a matrix to Hessenberg form, (2) a program is developed for the reduction of a Hessenberg matrix to colleague form, and (3) the characteristic polynomial with interval coefficients is readily obtained from the interval of colleague matrices. This interval polynomial is then factored into quadratic factors so that the eigenvalues may be obtained. To develop a contracting-interval program for factoring this polynomial with interval coefficients it is necessary to have an iteration method which converges even in the presence of controlled rounding errors. A theorem is stated giving sufficient conditions for the convergence of Newton's method when both the function and its Jacobian cannot be evaluated exactly but errors can be made proportional to the square of the norm of the difference between the previous two iterates. This theorem is applied to prove the convergence of the generalization of the Newton-Bairstow method that is used to obtain quadratic factors of the characteristic polynomial.

Vaccination Confidence and Parental Refusal/Delay of Early Childhood Vaccines

PubMed Central

Gilkey, Melissa B.; McRee, Annie-Laurie; Magnus, Brooke E.; Reiter, Paul L.; Dempsey, Amanda F.; Brewer, Noel T.

2016-01-01

Objective To support efforts to address parental hesitancy towards early childhood vaccination, we sought to validate the Vaccination Confidence Scale using data from a large, population-based sample of U.S. parents. Methods We used weighted data from 9,354 parents who completed the 2011 National Immunization Survey. Parents reported on the immunization history of a 19- to 35-month-old child in their households. Healthcare providers then verified children’s vaccination status for vaccines including measles, mumps, and rubella (MMR), varicella, and seasonal flu. We used separate multivariable logistic regression models to assess associations between parents’ mean scores on the 8-item Vaccination Confidence Scale and vaccine refusal, vaccine delay, and vaccination status. Results A substantial minority of parents reported a history of vaccine refusal (15%) or delay (27%). Vaccination confidence was negatively associated with refusal of any vaccine (odds ratio [OR] = 0.58, 95% confidence interval [CI], 0.54–0.63) as well as refusal of MMR, varicella, and flu vaccines specifically. Negative associations between vaccination confidence and measures of vaccine delay were more moderate, including delay of any vaccine (OR = 0.81, 95% CI, 0.76–0.86). Vaccination confidence was positively associated with having received vaccines, including MMR (OR = 1.53, 95% CI, 1.40–1.68), varicella (OR = 1.54, 95% CI, 1.42–1.66), and flu vaccines (OR = 1.32, 95% CI, 1.23–1.42). Conclusions Vaccination confidence was consistently associated with early childhood vaccination behavior across multiple vaccine types. Our findings support expanding the application of the Vaccination Confidence Scale to measure vaccination beliefs among parents of young children. PMID:27391098

Exponential operations and aggregation operators of interval neutrosophic sets and their decision making methods.

PubMed

Ye, Jun

2016-01-01

An interval neutrosophic set (INS) is a subclass of a neutrosophic set and a generalization of an interval-valued intuitionistic fuzzy set, and then the characteristics of INS are independently described by the interval numbers of its truth-membership, indeterminacy-membership, and falsity-membership degrees. However, the exponential parameters (weights) of all the existing exponential operational laws of INSs and the corresponding exponential aggregation operators are crisp values in interval neutrosophic decision making problems. As a supplement, this paper firstly introduces new exponential operational laws of INSs, where the bases are crisp values or interval numbers and the exponents are interval neutrosophic numbers (INNs), which are basic elements in INSs. Then, we propose an interval neutrosophic weighted exponential aggregation (INWEA) operator and a dual interval neutrosophic weighted exponential aggregation (DINWEA) operator based on these exponential operational laws and introduce comparative methods based on cosine measure functions for INNs and dual INNs. Further, we develop decision-making methods based on the INWEA and DINWEA operators. Finally, a practical example on the selecting problem of global suppliers is provided to illustrate the applicability and rationality of the proposed methods.

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

Estimation of postmortem interval through albumin in CSF by simple dye binding method.

PubMed

Parmar, Ankita K; Menon, Shobhana K

2015-12-01

Estimation of postmortem interval is a very important question in some medicolegal investigations. For the precise estimation of postmortem interval, there is a need of a method which can give accurate estimation. Bromocresol green (BCG) is a simple dye binding method and widely used in routine practice. Application of this method in forensic practice may bring revolutionary changes. In this study, cerebrospinal fluid was aspirated from cisternal puncture from 100 autopsies. A study was carried out on concentration of albumin with respect to postmortem interval. After death, albumin present in CSF undergoes changes, after 72 h of death, concentration of albumin has become 0.012 mM, and this decrease was linear from 2 h to 72 h. An important relationship was found between albumin concentration and postmortem interval with an error of ± 1-4h. The study concludes that CSF albumin can be a useful and significant parameter in estimation of postmortem interval. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

Advanced Computational Methods for Optimization of Non-Periodic Inspection Intervals for Aging Infrastructure

DTIC Science & Technology

2017-01-05

AFRL-AFOSR-JP-TR-2017-0002 Advanced Computational Methods for Optimization of Non-Periodic Inspection Intervals for Aging Infrastructure Manabu...Computational Methods for Optimization of Non-Periodic Inspection Intervals for Aging Infrastructure 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA2386...UNLIMITED: PB Public Release 13. SUPPLEMENTARY NOTES 14. ABSTRACT This report for the project titled ’Advanced Computational Methods for Optimization of

A comparison of statistical methods for evaluating matching performance of a biometric identification device: a preliminary report

NASA Astrophysics Data System (ADS)

Schuckers, Michael E.; Hawley, Anne; Livingstone, Katie; Mramba, Nona

2004-08-01

Confidence intervals are an important way to assess and estimate a parameter. In the case of biometric identification devices, several approaches to confidence intervals for an error rate have been proposed. Here we evaluate six of these methods. To complete this evaluation, we simulate data from a wide variety of parameter values. This data are simulated via a correlated binary distribution. We then determine how well these methods do at what they say they do: capturing the parameter inside the confidence interval. In addition, the average widths of the various confidence intervals are recorded for each set of parameters. The complete results of this simulation are presented graphically for easy comparison. We conclude by making a recommendation regarding which method performs best.

Common pitfalls in statistical analysis: “P” values, statistical significance and confidence intervals

PubMed Central

Ranganathan, Priya; Pramesh, C. S.; Buyse, Marc

2015-01-01

In the second part of a series on pitfalls in statistical analysis, we look at various ways in which a statistically significant study result can be expressed. We debunk some of the myths regarding the ‘P’ value, explain the importance of ‘confidence intervals’ and clarify the importance of including both values in a paper PMID:25878958

[Nonparametric method of estimating survival functions containing right-censored and interval-censored data].

PubMed

Xu, Yonghong; Gao, Xiaohuan; Wang, Zhengxi

2014-04-01

Missing data represent a general problem in many scientific fields, especially in medical survival analysis. Dealing with censored data, interpolation method is one of important methods. However, most of the interpolation methods replace the censored data with the exact data, which will distort the real distribution of the censored data and reduce the probability of the real data falling into the interpolation data. In order to solve this problem, we in this paper propose a nonparametric method of estimating the survival function of right-censored and interval-censored data and compare its performance to SC (self-consistent) algorithm. Comparing to the average interpolation and the nearest neighbor interpolation method, the proposed method in this paper replaces the right-censored data with the interval-censored data, and greatly improves the probability of the real data falling into imputation interval. Then it bases on the empirical distribution theory to estimate the survival function of right-censored and interval-censored data. The results of numerical examples and a real breast cancer data set demonstrated that the proposed method had higher accuracy and better robustness for the different proportion of the censored data. This paper provides a good method to compare the clinical treatments performance with estimation of the survival data of the patients. This pro vides some help to the medical survival data analysis.

Data assimilation method based on the constraints of confidence region

NASA Astrophysics Data System (ADS)

Li, Yong; Li, Siming; Sheng, Yao; Wang, Luheng

2018-03-01

The ensemble Kalman filter (EnKF) is a distinguished data assimilation method that is widely used and studied in various fields including methodology and oceanography. However, due to the limited sample size or imprecise dynamics model, it is usually easy for the forecast error variance to be underestimated, which further leads to the phenomenon of filter divergence. Additionally, the assimilation results of the initial stage are poor if the initial condition settings differ greatly from the true initial state. To address these problems, the variance inflation procedure is usually adopted. In this paper, we propose a new method based on the constraints of a confidence region constructed by the observations, called EnCR, to estimate the inflation parameter of the forecast error variance of the EnKF method. In the new method, the state estimate is more robust to both the inaccurate forecast models and initial condition settings. The new method is compared with other adaptive data assimilation methods in the Lorenz-63 and Lorenz-96 models under various model parameter settings. The simulation results show that the new method performs better than the competing methods.

Resampling methods in Microsoft Excel® for estimating reference intervals

PubMed Central

Theodorsson, Elvar

2015-01-01

Computer- intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel® in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles.
The purpose of this paper is to introduce the reader to resampling estimation techniques in general and in using Microsoft Excel® 2010 for the purpose of estimating reference intervals in particular.
Parametric methods are preferable to resampling methods when the distributions of observations in the reference samples is Gaussian or can transformed to that distribution even when the number of reference samples is less than 120. Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference individuals and corresponding samples are in the order of 40. At least 500-1000 random samples with replacement should be taken from the results of measurement of the reference samples. PMID:26527366

Resampling methods in Microsoft Excel® for estimating reference intervals.

PubMed

Theodorsson, Elvar

2015-01-01

Computer-intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel® in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles. 
The purpose of this paper is to introduce the reader to resampling estimation techniques in general and in using Microsoft Excel® 2010 for the purpose of estimating reference intervals in particular.
 Parametric methods are preferable to resampling methods when the distributions of observations in the reference samples is Gaussian or can transformed to that distribution even when the number of reference samples is less than 120. Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference individuals and corresponding samples are in the order of 40. At least 500-1000 random samples with replacement should be taken from the results of measurement of the reference samples.

Implementing the measurement interval midpoint method for change estimation

Treesearch

James A. Westfall; Thomas Frieswyk; Douglas M. Griffith

2009-01-01

The adoption of nationally consistent estimation procedures for the Forest Inventory and Analysis (FIA) program mandates changes in the methods used to develop resource trend information. Particularly, it is prescribed that changes in tree status occur at the midpoint of the measurement interval to minimize potential bias. The individual-tree characteristics requiring...

Overconfidence in Interval Estimates: What Does Expertise Buy You?

ERIC Educational Resources Information Center

McKenzie, Craig R. M.; Liersch, Michael J.; Yaniv, Ilan

2008-01-01

People's 90% subjective confidence intervals typically contain the true value about 50% of the time, indicating extreme overconfidence. Previous results have been mixed regarding whether experts are as overconfident as novices. Experiment 1 examined interval estimates from information technology (IT) professionals and UC San Diego (UCSD) students…

Comparison of patients' confidence in office, ambulatory, and home blood pressure measurements as methods of assessing for hypertension.

PubMed

Viera, Anthony J; Tuttle, Laura A; Voora, Raven; Olsson, Emily

2015-12-01

Uncertainty exists when relying on office (clinic) blood pressure (BP) measurements to diagnose hypertension. Home BP monitoring and ambulatory BP monitoring (ABPM) provide measurements that are more strongly associated with cardiovascular disease. The degree to which patients exhibit uncertainty about office BP measurements is unknown, as is whether they would have less uncertainty about other BP measurement methods. We therefore assessed people's confidence in methods of BP measurement, comparing perceptions about office BP monitoring, home BP monitoring, and ABPM techniques. We surveyed adults who were 30 years or older (n=193), all whom had undergone office BP measurements, two sessions of 24-h ABPM, and two 5-day periods of home BP monitoring. Respondents were asked to indicate their level of confidence on a 1 to 9 scale that BP measurements represented their 'usual' BP. Respondents had least confidence that assessments of BP made by office measurements (median 6) represented usual BP and greater confidence that assessments made by home BP monitoring (median 7, P<0.0001 vs. office) and ABPM (median 8, P<0.0001 vs. office) did so. Confidence levels did not vary significantly by BP levels, age, sex, race, or education level. The finding that patients do not have a great deal of confidence in office BP measurements, but have a higher degree of confidence in home BP and ambulatory BP assessment methods may be helpful in guiding strategies to diagnose hypertension and improve antihypertensive medication adherence.

A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies

PubMed Central

2014-01-01

Background The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. Methods The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. Results The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the Mann-Whitney interval without transformation and also the interval of the binormal AUC are very liberal. For small sample sizes the Wald interval with continuity has a comparable coverage probability as the LT interval and higher power. For large sample sizes the results of the LT interval and of the Wald interval without continuity correction are comparable. Conclusions If individual patient data is not available, but only the estimated AUC and the total sample size, the modified Wald intervals can be recommended as confidence intervals for the AUC. For small sample sizes the continuity correction should be used. PMID:24552686

The Interpretation of Scholars' Interpretations of Confidence Intervals: Criticism, Replication, and Extension of Hoekstra et al. (2014)

PubMed Central

García-Pérez, Miguel A.; Alcalá-Quintana, Rocío

2016-01-01

Hoekstra et al. (Psychonomic Bulletin & Review, 2014, 21:1157–1164) surveyed the interpretation of confidence intervals (CIs) by first-year students, master students, and researchers with six items expressing misinterpretations of CIs. They asked respondents to answer all items, computed the number of items endorsed, and concluded that misinterpretation of CIs is robust across groups. Their design may have produced this outcome artifactually for reasons that we describe. This paper discusses first the two interpretations of CIs and, hence, why misinterpretation cannot be inferred from endorsement of some of the items. Next, a re-analysis of Hoekstra et al.'s data reveals some puzzling differences between first-year and master students that demand further investigation. For that purpose, we designed a replication study with an extended questionnaire including two additional items that express correct interpretations of CIs (to compare endorsement of correct vs. nominally incorrect interpretations) and we asked master students to indicate which items they would have omitted had they had the option (to distinguish deliberate from uninformed endorsement caused by the forced-response format). Results showed that incognizant first-year students endorsed correct and nominally incorrect items identically, revealing that the two item types are not differentially attractive superficially; in contrast, master students were distinctively more prone to endorsing correct items when their uninformed responses were removed, although they admitted to nescience more often that might have been expected. Implications for teaching practices are discussed. PMID:27458424

Study of FibroTest and hyaluronic acid biological variation in healthy volunteers and comparison of serum hyaluronic acid biological variation between chronic liver diseases of different etiology and fibrotic stage using confidence intervals.

PubMed

Istaces, Nicolas; Gulbis, Béatrice

2015-07-01

Personalized ranges of liver fibrosis serum biomarkers such as FibroTest or hyaluronic acid could be used for early detection of fibrotic changes in patients with progressive chronic liver disease. Our aim was to generate reliable biological variation estimates for these two biomarkers with confidence intervals for within-subject biological variation and reference change value. Nine fasting healthy volunteers and 66 chronic liver disease patients were included. Biological variation estimates were calculated for FibroTest in healthy volunteers, and for hyaluronic acid in healthy volunteers and chronic liver disease patients stratified by etiology and liver fibrosis stage. In healthy volunteers, within-subject biological coefficient of variation (with 95% confidence intervals) and index of individuality were 20% (16%-28%) and 0.6 for FibroTest and 34% (27%-47%) and 0.79 for hyaluronic acid, respectively. Overall hyaluronic acid within-subject biological coefficient of variation was similar among non-alcoholic fatty liver disease and chronic hepatitis C with 41% (34%-52%) and 45% (39%-55%), respectively, in contrast to chronic hepatitis B with 170% (140%-215%). Hyaluronic acid within-subject biological coefficients of variation were similar between F0-F1, F2 and F3 liver fibrosis stages in non-alcoholic fatty liver disease with 34% (25%-49%), 41% (31%-59%) and 34% (23%-62%), respectively, and in chronic hepatitis C with 34% (27%-47%), 33% (26%-45%) and 38% (27%-65%), respectively. However, corresponding hyaluronic acid indexes of individuality were lower in the higher fibrosis stages. Non-overlapping confidence intervals of biological variation estimates allowed us to detect significant differences regarding hyaluronic acid biological variation between chronic liver disease subgroups. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Comparison of Dissolution Similarity Assessment Methods for Products with Large Variations: f2 Statistics and Model-Independent Multivariate Confidence Region Procedure for Dissolution Profiles of Multiple Oral Products.

PubMed

Yoshida, Hiroyuki; Shibata, Hiroko; Izutsu, Ken-Ichi; Goda, Yukihiro

2017-01-01

The current Japanese Ministry of Health Labour and Welfare (MHLW)'s Guideline for Bioequivalence Studies of Generic Products uses averaged dissolution rates for the assessment of dissolution similarity between test and reference formulations. This study clarifies how the application of model-independent multivariate confidence region procedure (Method B), described in the European Medical Agency and U.S. Food and Drug Administration guidelines, affects similarity outcomes obtained empirically from dissolution profiles with large variations in individual dissolution rates. Sixty-one datasets of dissolution profiles for immediate release, oral generic, and corresponding innovator products that showed large variation in individual dissolution rates in generic products were assessed on their similarity by using the f 2 statistics defined in the MHLW guidelines (MHLW f 2 method) and two different Method B procedures, including a bootstrap method applied with f 2 statistics (BS method) and a multivariate analysis method using the Mahalanobis distance (MV method). The MHLW f 2 and BS methods provided similar dissolution similarities between reference and generic products. Although a small difference in the similarity assessment may be due to the decrease in the lower confidence interval for expected f 2 values derived from the large variation in individual dissolution rates, the MV method provided results different from those obtained through MHLW f 2 and BS methods. Analysis of actual dissolution data for products with large individual variations would provide valuable information towards an enhanced understanding of these methods and their possible incorporation in the MHLW guidelines.

Alternative methods to evaluate trial level surrogacy.

PubMed

Abrahantes, Josè Cortiñas; Shkedy, Ziv; Molenberghs, Geert

2008-01-01

The evaluation and validation of surrogate endpoints have been extensively studied in the last decade. Prentice [1] and Freedman, Graubard and Schatzkin [2] laid the foundations for the evaluation of surrogate endpoints in randomized clinical trials. Later, Buyse et al. [5] proposed a meta-analytic methodology, producing different methods for different settings, which was further studied by Alonso and Molenberghs [9], in their unifying approach based on information theory. In this article, we focus our attention on the trial-level surrogacy and propose alternative procedures to evaluate such surrogacy measure, which do not pre-specify the type of association. A promising correction based on cross-validation is investigated. As well as the construction of confidence intervals for this measure. In order to avoid making assumption about the type of relationship between the treatment effects and its distribution, a collection of alternative methods, based on regression trees, bagging, random forests, and support vector machines, combined with bootstrap-based confidence interval and, should one wish, in conjunction with a cross-validation based correction, will be proposed and applied. We apply the various strategies to data from three clinical studies: in opthalmology, in advanced colorectal cancer, and in schizophrenia. The results obtained for the three case studies are compared; they indicate that using random forest or bagging models produces larger estimated values for the surrogacy measure, which are in general stabler and the confidence interval narrower than linear regression and support vector regression. For the advanced colorectal cancer studies, we even found the trial-level surrogacy is considerably different from what has been reported. In general the alternative methods are more computationally demanding, and specially the calculation of the confidence intervals, require more computational time that the delta-method counterpart. First, more flexible

Effects of High-Intensity Interval Training on Aerobic Capacity in Cardiac Patients: A Systematic Review with Meta-Analysis

PubMed Central

Xie, Bin; Yan, Xianfeng

2017-01-01

Purpose. The aim of this study was to compare the effects of high-intensity interval training (INTERVAL) and moderate-intensity continuous training (CONTINUOUS) on aerobic capacity in cardiac patients. Methods. A meta-analysis identified by searching the PubMed, Cochrane Library, EMBASE, and Web of Science databases from inception through December 2016 compared the effects of INTERVAL and CONTINUOUS among cardiac patients. Results. Twenty-one studies involving 736 participants with cardiac diseases were included. Compared with CONTINUOUS, INTERVAL was associated with greater improvement in peak VO2 (mean difference 1.76 mL/kg/min, 95% confidence interval 1.06 to 2.46 mL/kg/min, p < 0.001) and VO2 at AT (mean difference 0.90 mL/kg/min, 95% confidence interval 0.0 to 1.72 mL/kg/min, p = 0.03). No significant difference between the INTERVAL and CONTINUOUS groups was observed in terms of peak heart rate, peak minute ventilation, VE/VCO2 slope and respiratory exchange ratio, body mass, systolic or diastolic blood pressure, triglyceride or low- or high-density lipoprotein cholesterol level, flow-mediated dilation, or left ventricular ejection fraction. Conclusions. This study showed that INTERVAL improves aerobic capacity more effectively than does CONTINUOUS in cardiac patients. Further studies with larger samples are needed to confirm our observations. PMID:28386556

Responsibility and confidence

PubMed Central

Austin, Zubin

2013-01-01

Background: Despite the changing role of the pharmacist in patient-centred practice, pharmacists anecdotally reported little confidence in their clinical decision-making skills and do not feel responsible for their patients. Observational findings have suggested these trends within the profession, but there is a paucity of evidence to explain why. We conducted an exploratory study with an objective to identify reasons for the lack of responsibility and/or confidence in various pharmacy practice settings. Methods: Pharmacist interviews were conducted via written response, face-to-face or telephone. Seven questions were asked on the topic of responsibility and confidence as it applies to pharmacy practice and how pharmacists think these themes differ in medicine. Interview transcripts were analyzed and divided by common theme. Quotations to support these themes are presented. Results: Twenty-nine pharmacists were asked to participate, and 18 responded (62% response rate). From these interviews, 6 themes were identified as barriers to confidence and responsibility: hierarchy of the medical system, role definitions, evolution of responsibility, ownership of decisions for confidence building, quality and consequences of mentorship and personality traits upon admission. Discussion: We identified 6 potential barriers to the development of pharmacists’ self-confidence and responsibility. These findings have practical applicability for educational research, future curriculum changes, experiential learning structure and pharmacy practice. Due to bias and the limitations of this form of exploratory research and small sample size, evidence should be interpreted cautiously. Conclusion: Pharmacists feel neither responsible nor confident for their clinical decisions due to social, educational, experiential and personal reasons. Can Pharm J 2013;146:155-161. PMID:23795200

Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis.

PubMed

Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A

2015-05-01

Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.

Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods

ERIC Educational Resources Information Center

MacKinnon, David P.; Lockwood, Chondra M.; Williams, Jason

2004-01-01

The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal…

Exploration of Analysis Methods for Diagnostic Imaging Tests: Problems with ROC AUC and Confidence Scores in CT Colonography

PubMed Central

Mallett, Susan; Halligan, Steve; Collins, Gary S.; Altman, Doug G.

2014-01-01

Background Different methods of evaluating diagnostic performance when comparing diagnostic tests may lead to different results. We compared two such approaches, sensitivity and specificity with area under the Receiver Operating Characteristic Curve (ROC AUC) for the evaluation of CT colonography for the detection of polyps, either with or without computer assisted detection. Methods In a multireader multicase study of 10 readers and 107 cases we compared sensitivity and specificity, using radiological reporting of the presence or absence of polyps, to ROC AUC calculated from confidence scores concerning the presence of polyps. Both methods were assessed against a reference standard. Here we focus on five readers, selected to illustrate issues in design and analysis. We compared diagnostic measures within readers, showing that differences in results are due to statistical methods. Results Reader performance varied widely depending on whether sensitivity and specificity or ROC AUC was used. There were problems using confidence scores; in assigning scores to all cases; in use of zero scores when no polyps were identified; the bimodal non-normal distribution of scores; fitting ROC curves due to extrapolation beyond the study data; and the undue influence of a few false positive results. Variation due to use of different ROC methods exceeded differences between test results for ROC AUC. Conclusions The confidence scores recorded in our study violated many assumptions of ROC AUC methods, rendering these methods inappropriate. The problems we identified will apply to other detection studies using confidence scores. We found sensitivity and specificity were a more reliable and clinically appropriate method to compare diagnostic tests. PMID:25353643

Impact of the Birkman Method Assessment on Pharmacy Student Self-Confidence, Self-Perceptions, and Self-Awareness

PubMed Central

Grant, Amy D.; Fabel, Patricia H.; Worrall, Cathy; Brittain, Kristy; Martinez, Breanne; Lu, Z. Kevin; Davis, Robert; Doran, Georgia H.; Ziegler, Bryan

2016-01-01

Objective. To identify changes in pharmacy student self-confidence, self-perceptions, and self-awareness after completing the Birkman Method assessment and training program. Methods. Survey tools were developed to evaluate students at baseline and following the co-curricular Birkman Method program. Following IRB approval, students participating in the Birkman Method program were recruited for enrollment in this survey-based study. Results. Student self-confidence was high at baseline (mean=4 out of 5) and did not significantly change after Birkman Method testing and training. Self-perceptions regarding usual and stressed communication style and behaviors and behavioral needs under stress changed significantly after Birkman Method testing and training for these endpoints. The Birkman Method intervention resulted in a significant improvement in self-awareness, as indicated by a mean self-perception accuracy score increase of 1.6 points (95% CI: 1.3-1.9). Conclusions. A Birkman Method assessment and training program is an effective self-assessment tool for students, and may be useful for accomplishing Accreditation Council for Pharmacy Education (ACPE) 2016 Standard 4 affective domain elements, particularly self-awareness. PMID:28090097

The QT Interval and Risk of Incident Atrial Fibrillation

PubMed Central

Mandyam, Mala C.; Soliman, Elsayed Z.; Alonso, Alvaro; Dewland, Thomas A.; Heckbert, Susan R.; Vittinghoff, Eric; Cummings, Steven R.; Ellinor, Patrick T.; Chaitman, Bernard R.; Stocke, Karen; Applegate, William B.; Arking, Dan E.; Butler, Javed; Loehr, Laura R.; Magnani, Jared W.; Murphy, Rachel A.; Satterfield, Suzanne; Newman, Anne B.; Marcus, Gregory M.

2013-01-01

BACKGROUND Abnormal atrial repolarization is important in the development of atrial fibrillation (AF), but no direct measurement is available in clinical medicine. OBJECTIVE To determine whether the QT interval, a marker of ventricular repolarization, could be used to predict incident AF. METHODS We examined a prolonged QT corrected by the Framingham formula (QTFram) as a predictor of incident AF in the Atherosclerosis Risk in Communities (ARIC) study. The Cardiovascular Health Study (CHS) and Health, Aging, and Body Composition (Health ABC) study were used for validation. Secondary predictors included QT duration as a continuous variable, a short QT interval, and QT intervals corrected by other formulae. RESULTS Among 14,538 ARIC participants, a prolonged QTFram predicted a roughly two-fold increased risk of AF (hazard ratio [HR] 2.05, 95% confidence interval [CI] 1.42–2.96, p<0.001). No substantive attenuation was observed after adjustment for age, race, sex, study center, body mass index, hypertension, diabetes, coronary disease, and heart failure. The findings were validated in CHS and Health ABC and were similar across various QT correction methods. Also in ARIC, each 10-ms increase in QTFram was associated with an increased unadjusted (HR 1.14, 95%CI 1.10–1.17, p<0.001) and adjusted (HR 1.11, 95%CI 1.07–1.14, p<0.001) risk of AF. Findings regarding a short QT were inconsistent across cohorts. CONCLUSIONS A prolonged QT interval is associated with an increased risk of incident AF. PMID:23872693

Corrected Confidence Bands for Functional Data Using Principal Components

PubMed Central

Goldsmith, J.; Greven, S.; Crainiceanu, C.

2014-01-01

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN. PMID:23003003

A simple method for assessing occupational exposure via the one-way random effects model.

PubMed

Krishnamoorthy, K; Mathew, Thomas; Peng, Jie

2016-11-01

A one-way random effects model is postulated for the log-transformed shift-long personal exposure measurements, where the random effect in the model represents an effect due to the worker. Simple closed-form confidence intervals are proposed for the relevant parameters of interest using the method of variance estimates recovery (MOVER). The performance of the confidence bounds is evaluated and compared with those based on the generalized confidence interval approach. Comparison studies indicate that the proposed MOVER confidence bounds are better than the generalized confidence bounds for the overall mean exposure and an upper percentile of the exposure distribution. The proposed methods are illustrated using a few examples involving industrial hygiene data.

The integrated model of sport confidence: a canonical correlation and mediational analysis.

PubMed

Koehn, Stefan; Pearce, Alan J; Morris, Tony

2013-12-01

The main purpose of the study was to examine crucial parts of Vealey's (2001) integrated framework hypothesizing that sport confidence is a mediating variable between sources of sport confidence (including achievement, self-regulation, and social climate) and athletes' affect in competition. The sample consisted of 386 athletes, who completed the Sources of Sport Confidence Questionnaire, Trait Sport Confidence Inventory, and Dispositional Flow Scale-2. Canonical correlation analysis revealed a confidence-achievement dimension underlying flow. Bias-corrected bootstrap confidence intervals in AMOS 20.0 were used in examining mediation effects between source domains and dispositional flow. Results showed that sport confidence partially mediated the relationship between achievement and self-regulation domains and flow, whereas no significant mediation was found for social climate. On a subscale level, full mediation models emerged for achievement and flow dimensions of challenge-skills balance, clear goals, and concentration on the task at hand.

Exploration of analysis methods for diagnostic imaging tests: problems with ROC AUC and confidence scores in CT colonography.

PubMed

Mallett, Susan; Halligan, Steve; Collins, Gary S; Altman, Doug G

2014-01-01

Different methods of evaluating diagnostic performance when comparing diagnostic tests may lead to different results. We compared two such approaches, sensitivity and specificity with area under the Receiver Operating Characteristic Curve (ROC AUC) for the evaluation of CT colonography for the detection of polyps, either with or without computer assisted detection. In a multireader multicase study of 10 readers and 107 cases we compared sensitivity and specificity, using radiological reporting of the presence or absence of polyps, to ROC AUC calculated from confidence scores concerning the presence of polyps. Both methods were assessed against a reference standard. Here we focus on five readers, selected to illustrate issues in design and analysis. We compared diagnostic measures within readers, showing that differences in results are due to statistical methods. Reader performance varied widely depending on whether sensitivity and specificity or ROC AUC was used. There were problems using confidence scores; in assigning scores to all cases; in use of zero scores when no polyps were identified; the bimodal non-normal distribution of scores; fitting ROC curves due to extrapolation beyond the study data; and the undue influence of a few false positive results. Variation due to use of different ROC methods exceeded differences between test results for ROC AUC. The confidence scores recorded in our study violated many assumptions of ROC AUC methods, rendering these methods inappropriate. The problems we identified will apply to other detection studies using confidence scores. We found sensitivity and specificity were a more reliable and clinically appropriate method to compare diagnostic tests.

The Effect Of Different Corrective Feedback Methods on the Outcome and Self Confidence of Young Athletes

PubMed Central

Tzetzis, George; Votsis, Evandros; Kourtessis, Thomas

2008-01-01

This experiment investigated the effects of three corrective feedback methods, using different combinations of correction, or error cues and positive feedback for learning two badminton skills with different difficulty (forehand clear - low difficulty, backhand clear - high difficulty). Outcome and self-confidence scores were used as dependent variables. The 48 participants were randomly assigned into four groups. Group A received correction cues and positive feedback. Group B received cues on errors of execution. Group C received positive feedback, correction cues and error cues. Group D was the control group. A pre, post and a retention test was conducted. A three way analysis of variance ANOVA (4 groups X 2 task difficulty X 3 measures) with repeated measures on the last factor revealed significant interactions for each depended variable. All the corrective feedback methods groups, increased their outcome scores over time for the easy skill, but only groups A and C for the difficult skill. Groups A and B had significantly better outcome scores than group C and the control group for the easy skill on the retention test. However, for the difficult skill, group C was better than groups A, B and D. The self confidence scores of groups A and C improved over time for the easy skill but not for group B and D. Again, for the difficult skill, only group C improved over time. Finally a regression analysis depicted that the improvement in performance predicted a proportion of the improvement in self confidence for both the easy and the difficult skill. It was concluded that when young athletes are taught skills of different difficulty, different type of instruction, might be more appropriate in order to improve outcome and self confidence. A more integrated approach on teaching will assist coaches or physical education teachers to be more efficient and effective. Key pointsThe type of the skill is a critical factor in determining the effectiveness of the feedback types

Binary Interval Search: a scalable algorithm for counting interval intersections.

PubMed

Layer, Ryan M; Skadron, Kevin; Robins, Gabriel; Hall, Ira M; Quinlan, Aaron R

2013-01-01

The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery. We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals. https://github.com/arq5x/bits.

Testing 40 Predictions from the Transtheoretical Model Again, with Confidence

ERIC Educational Resources Information Center

Velicer, Wayne F.; Brick, Leslie Ann D.; Fava, Joseph L.; Prochaska, James O.

2013-01-01

Testing Theory-based Quantitative Predictions (TTQP) represents an alternative to traditional Null Hypothesis Significance Testing (NHST) procedures and is more appropriate for theory testing. The theory generates explicit effect size predictions and these effect size estimates, with related confidence intervals, are used to test the predictions.…

Reference interval computation: which method (not) to choose?

PubMed

Pavlov, Igor Y; Wilson, Andrew R; Delgado, Julio C

2012-07-11

When different methods are applied to reference interval (RI) calculation the results can sometimes be substantially different, especially for small reference groups. If there are no reliable RI data available, there is no way to confirm which method generates results closest to the true RI. We randomly drawn samples obtained from a public database for 33 markers. For each sample, RIs were calculated by bootstrapping, parametric, and Box-Cox transformed parametric methods. Results were compared to the values of the population RI. For approximately half of the 33 markers, results of all 3 methods were within 3% of the true reference value. For other markers, parametric results were either unavailable or deviated considerably from the true values. The transformed parametric method was more accurate than bootstrapping for sample size of 60, very close to bootstrapping for sample size 120, but in some cases unavailable. We recommend against using parametric calculations to determine RIs. The transformed parametric method utilizing Box-Cox transformation would be preferable way of RI calculation, if it satisfies normality test. If not, the bootstrapping is always available, and is almost as accurate and precise as the transformed parametric method. Copyright © 2012 Elsevier B.V. All rights reserved.

Binary Interval Search: a scalable algorithm for counting interval intersections

PubMed Central

Layer, Ryan M.; Skadron, Kevin; Robins, Gabriel; Hall, Ira M.; Quinlan, Aaron R.

2013-01-01

Motivation: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery. Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals. Availability: https://github.com/arq5x/bits. Contact: arq5x@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23129298

Method and system for assigning a confidence metric for automated determination of optic disc location

DOEpatents

Karnowski, Thomas P [Knoxville, TN; Tobin, Jr., Kenneth W.; Muthusamy Govindasamy, Vijaya Priya [Knoxville, TN; Chaum, Edward [Memphis, TN

2012-07-10

A method for assigning a confidence metric for automated determination of optic disc location that includes analyzing a retinal image and determining at least two sets of coordinates locating an optic disc in the retinal image. The sets of coordinates can be determined using first and second image analysis techniques that are different from one another. An accuracy parameter can be calculated and compared to a primary risk cut-off value. A high confidence level can be assigned to the retinal image if the accuracy parameter is less than the primary risk cut-off value and a low confidence level can be assigned to the retinal image if the accuracy parameter is greater than the primary risk cut-off value. The primary risk cut-off value being selected to represent an acceptable risk of misdiagnosis of a disease having retinal manifestations by the automated technique.

Comparing the Effects of Simulation-Based and Traditional Teaching Methods on the Critical Thinking Abilities and Self-Confidence of Nursing Students.

PubMed

Alamrani, Mashael Hasan; Alammar, Kamila Ahmad; Alqahtani, Sarah Saad; Salem, Olfat A

2018-06-01

Critical thinking and self-confidence are imperative to success in clinical practice. Educators should use teaching strategies that will help students enhance their critical thinking and self-confidence in complex content such as electrocardiogram interpretation. Therefore, teaching electrocardiogram interpretation to students is important for nurse educators. This study compares the effect of simulation-based and traditional teaching methods on the critical thinking and self-confidence of students during electrocardiogram interpretation sessions. Thirty undergraduate nursing students volunteered to participate in this study. The participants were divided into intervention and control groups, which were taught respectively using the simulation-based and traditional teaching programs. All of the participants were asked to complete the study instrumentpretest and posttest to measure their critical thinking and self-confidence. Improvement was observed in the control and experimental groups with respect to critical thinking and self-confidence, as evidenced by the results of the paired samples t test and the Wilcoxon signed-rank test (p < .05). However, the independent t test and Mann-Whitney U test indicate that the difference between the two groups was not significant (p > .05). This study evaluated an innovative simulation-based teaching method for nurses. No significant differences in outcomes were identified between the simulator-based and traditional teaching methods, indicating that well-implemented educational programs that use either teaching method effectively promote critical thinking and self-confidence in nursing students. Nurse educators are encouraged to design educational plans with clear objectives to improve the critical thinking and self-confidence of their students. Future research should compare the effects of several teaching sessions using each method in a larger sample.

A method for analyzing clustered interval-censored data based on Cox's model.

PubMed

Kor, Chew-Teng; Cheng, Kuang-Fu; Chen, Yi-Hau

2013-02-28

Methods for analyzing interval-censored data are well established. Unfortunately, these methods are inappropriate for the studies with correlated data. In this paper, we focus on developing a method for analyzing clustered interval-censored data. Our method is based on Cox's proportional hazard model with piecewise-constant baseline hazard function. The correlation structure of the data can be modeled by using Clayton's copula or independence model with proper adjustment in the covariance estimation. We establish estimating equations for the regression parameters and baseline hazards (and a parameter in copula) simultaneously. Simulation results confirm that the point estimators follow a multivariate normal distribution, and our proposed variance estimations are reliable. In particular, we found that the approach with independence model worked well even when the true correlation model was derived from Clayton's copula. We applied our method to a family-based cohort study of pandemic H1N1 influenza in Taiwan during 2009-2010. Using the proposed method, we investigate the impact of vaccination and family contacts on the incidence of pH1N1 influenza. Copyright © 2012 John Wiley & Sons, Ltd.

Parametric methods outperformed non-parametric methods in comparisons of discrete numerical variables.

PubMed

Fagerland, Morten W; Sandvik, Leiv; Mowinckel, Petter

2011-04-13

The number of events per individual is a widely reported variable in medical research papers. Such variables are the most common representation of the general variable type called discrete numerical. There is currently no consensus on how to compare and present such variables, and recommendations are lacking. The objective of this paper is to present recommendations for analysis and presentation of results for discrete numerical variables. Two simulation studies were used to investigate the performance of hypothesis tests and confidence interval methods for variables with outcomes {0, 1, 2}, {0, 1, 2, 3}, {0, 1, 2, 3, 4}, and {0, 1, 2, 3, 4, 5}, using the difference between the means as an effect measure. The Welch U test (the T test with adjustment for unequal variances) and its associated confidence interval performed well for almost all situations considered. The Brunner-Munzel test also performed well, except for small sample sizes (10 in each group). The ordinary T test, the Wilcoxon-Mann-Whitney test, the percentile bootstrap interval, and the bootstrap-t interval did not perform satisfactorily. The difference between the means is an appropriate effect measure for comparing two independent discrete numerical variables that has both lower and upper bounds. To analyze this problem, we encourage more frequent use of parametric hypothesis tests and confidence intervals.

Reference intervals for selected serum biochemistry analytes in cheetahs Acinonyx jubatus.

PubMed

Hudson-Lamb, Gavin C; Schoeman, Johan P; Hooijberg, Emma H; Heinrich, Sonja K; Tordiffe, Adrian S W

2016-02-26

Published haematologic and serum biochemistry reference intervals are very scarce for captive cheetahs and even more for free-ranging cheetahs. The current study was performed to establish reference intervals for selected serum biochemistry analytes in cheetahs. Baseline serum biochemistry analytes were analysed from 66 healthy Namibian cheetahs. Samples were collected from 30 captive cheetahs at the AfriCat Foundation and 36 free-ranging cheetahs from central Namibia. The effects of captivity-status, age, sex and haemolysis score on the tested serum analytes were investigated. The biochemistry analytes that were measured were sodium, potassium, magnesium, chloride, urea and creatinine. The 90% confidence interval of the reference limits was obtained using the non-parametric bootstrap method. Reference intervals were preferentially determined by the non-parametric method and were as follows: sodium (128 mmol/L - 166 mmol/L), potassium (3.9 mmol/L - 5.2 mmol/L), magnesium (0.8 mmol/L - 1.2 mmol/L), chloride (97 mmol/L - 130 mmol/L), urea (8.2 mmol/L - 25.1 mmol/L) and creatinine (88 µmol/L - 288 µmol/L). Reference intervals from the current study were compared with International Species Information System values for cheetahs and found to be narrower. Moreover, age, sex and haemolysis score had no significant effect on the serum analytes in this study. Separate reference intervals for captive and free-ranging cheetahs were also determined. Captive cheetahs had higher urea values, most likely due to dietary factors. This study is the first to establish reference intervals for serum biochemistry analytes in cheetahs according to international guidelines. These results can be used for future health and disease assessments in both captive and free-ranging cheetahs.

Microvascular anastomosis simulation using a chicken thigh model: Interval versus massed training.

PubMed

Schoeff, Stephen; Hernandez, Brian; Robinson, Derek J; Jameson, Mark J; Shonka, David C

2017-11-01

To compare the effectiveness of massed versus interval training when teaching otolaryngology residents microvascular suturing on a validated microsurgical model. Otolaryngology residents were placed into interval (n = 7) or massed (n = 7) training groups. The interval group performed three separate 30-minute practice sessions separated by at least 1 week, and the massed group performed a single 90-minute practice session. Both groups viewed a video demonstration and recorded a pretest prior to the first training session. A post-test was administered following the last practice session. At an academic medical center, 14 otolaryngology residents were assigned using stratified randomization to interval or massed training. Blinded evaluators graded performance using a validated microvascular Objective Structured Assessment of Technical Skill tool. The tool is comprised of two major components: task-specific score (TSS) and global rating scale (GRS). Participants also received pre- and poststudy surveys to compare subjective confidence in multiple aspects of microvascular skill acquisition. Overall, all residents showed increased TSS and GRS on post- versus pretest. After completion of training, the interval group had a statistically significant increase in both TSS and GRS, whereas the massed group's increase was not significant. Residents in both groups reported significantly increased levels of confidence after completion of the study. Self-directed learning using a chicken thigh artery model may benefit microsurgical skills, competence, and confidence for resident surgeons. Interval training results in significant improvement in early development of microvascular anastomosis skills, whereas massed training does not. NA. Laryngoscope, 127:2490-2494, 2017. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Neurophysiology of perceived confidence.

PubMed

Graziano, Martin; Parra, Lucas C; Sigman, Mariano

2010-01-01

In a partial report paradigm, subjects observe during a brief presentation a cluttered field and after some time - typically ranging from 100 ms to a second - are asked to report a subset of the presented elements. A vast buffer of information is transiently available to be broadcasted which, if not retrieved in time, fades rapidly without reaching consciousness. An interesting feature of this experiment is that objective performance and subjective confidence is decoupled. This converts this paradigm in an ideal vehicle to understand the brain dynamics of the construction of confidence. Here we report a high-density EEG experiment in which we infer elements of the EEG response which are indicative of subjective confidence. We find that an early response during encoding partially correlates with perceived confidence. However, the bulk of the weight of subjective confidence is determined during a late, N400-like waveform, during the retrieval stage. This shows that we can find markers of access to internal, subjective states, that are uncoupled from objective response and stimulus properties of the task, and we propose that this can be used with decoding methods of EEG to infer subjective mental states.

Five-Year Risk of Interval-Invasive Second Breast Cancer

PubMed Central

Buist, Diana S. M.; Houssami, Nehmat; Dowling, Emily C.; Halpern, Elkan F.; Gazelle, G. Scott; Lehman, Constance D.; Henderson, Louise M.; Hubbard, Rebecca A.

2015-01-01

Background: Earlier detection of second breast cancers after primary breast cancer (PBC) treatment improves survival, yet mammography is less accurate in women with prior breast cancer. The purpose of this study was to examine women presenting clinically with second breast cancers after negative surveillance mammography (interval cancers), and to estimate the five-year risk of interval-invasive second cancers for women with varying risk profiles. Methods: We evaluated a prospective cohort of 15 114 women with 47 717 surveillance mammograms diagnosed with stage 0-II unilateral PBC from 1996 through 2008 at facilities in the Breast Cancer Surveillance Consortium. We used discrete time survival models to estimate the association between odds of an interval-invasive second breast cancer and candidate predictors, including demographic, PBC, and imaging characteristics. All statistical tests were two-sided. Results: The cumulative incidence of second breast cancers after five years was 54.4 per 1000 women, with 325 surveillance-detected and 138 interval-invasive second breast cancers. The five-year risk of interval-invasive second cancer for women with referent category characteristics was 0.60%. For women with the most and least favorable profiles, the five-year risk ranged from 0.07% to 6.11%. Multivariable modeling identified grade II PBC (odds ratio [OR] = 1.95, 95% confidence interval [CI] = 1.15 to 3.31), treatment with lumpectomy without radiation (OR = 3.27, 95% CI = 1.91 to 5.62), interval PBC presentation (OR = 2.01, 95% CI 1.28 to 3.16), and heterogeneously dense breasts on mammography (OR = 1.54, 95% CI = 1.01 to 2.36) as independent predictors of interval-invasive second breast cancers. Conclusions: PBC diagnosis and treatment characteristics contribute to variation in subsequent-interval second breast cancer risk. Consideration of these factors may be useful in developing tailored post-treatment imaging surveillance plans. PMID:25904721

Corrected confidence bands for functional data using principal components.

PubMed

Goldsmith, J; Greven, S; Crainiceanu, C

2013-03-01

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN. Copyright © 2013, The International Biometric Society.

The holy grail of soil metal contamination site assessment: reducing risk and increasing confidence of decision making using infield portable X-ray Fluorescence (pXRF) technology

NASA Astrophysics Data System (ADS)

Rouillon, M.; Taylor, M. P.; Dong, C.

2016-12-01

This research assesses the advantages of integrating field portable X-ray Fluorescence (pXRF) technology for reducing the risk and increase confidence of decision making for metal-contaminated site assessments. Metal-contaminated sites are often highly heterogeneous and require a high sampling density to accurately characterize the distribution and concentration of contaminants. The current regulatory assessment approaches rely on a small number of samples processed using standard wet-chemistry methods. In New South Wales (NSW), Australia, the current notification trigger for characterizing metal-contaminated sites require the upper 95% confidence interval of the site mean to equal or exceed the relevant guidelines. The method's low `minimum' sampling requirements can misclassify sites due to the heterogeneous nature of soil contamination, leading to inaccurate decision making. To address this issue, we propose integrating infield pXRF analysis with the established sampling method to overcome sampling limitations. This approach increases the minimum sampling resolution and reduces the 95% CI of the site mean. Infield pXRF analysis at contamination hotspots enhances sample resolution efficiently and without the need to return to the site. In this study, the current and proposed pXRF site assessment methods are compared at five heterogeneous metal-contaminated sites by analysing the spatial distribution of contaminants, 95% confidence intervals of site means, and the sampling and analysis uncertainty associated with each method. Finally, an analysis of costs associated with both the current and proposed methods is presented to demonstrate the advantages of incorporating pXRF into metal-contaminated site assessments. The data shows that pXRF integrated site assessments allows for faster, cost-efficient, characterisation of metal-contaminated sites with greater confidence for decision making.

Prolonged corrected QT interval is predictive of future stroke events even in subjects without ECG-diagnosed left ventricular hypertrophy.

PubMed

Ishikawa, Joji; Ishikawa, Shizukiyo; Kario, Kazuomi

2015-03-01

We attempted to evaluate whether subjects who exhibit prolonged corrected QT (QTc) interval (≥440 ms in men and ≥460 ms in women) on ECG, with and without ECG-diagnosed left ventricular hypertrophy (ECG-LVH; Cornell product, ≥244 mV×ms), are at increased risk of stroke. Among the 10 643 subjects, there were a total of 375 stroke events during the follow-up period (128.7±28.1 months; 114 142 person-years). The subjects with prolonged QTc interval (hazard ratio, 2.13; 95% confidence interval, 1.22-3.73) had an increased risk of stroke even after adjustment for ECG-LVH (hazard ratio, 1.71; 95% confidence interval, 1.22-2.40). When we stratified the subjects into those with neither a prolonged QTc interval nor ECG-LVH, those with a prolonged QTc interval but without ECG-LVH, and those with ECG-LVH, multivariate-adjusted Cox proportional hazards analysis demonstrated that the subjects with prolonged QTc intervals but not ECG-LVH (1.2% of all subjects; incidence, 10.7%; hazard ratio, 2.70, 95% confidence interval, 1.48-4.94) and those with ECG-LVH (incidence, 7.9%; hazard ratio, 1.83; 95% confidence interval, 1.31-2.57) had an increased risk of stroke events, compared with those with neither a prolonged QTc interval nor ECG-LVH. In conclusion, prolonged QTc interval was associated with stroke risk even among patients without ECG-LVH in the general population. © 2014 American Heart Association, Inc.

Performance of toxicity probability interval based designs in contrast to the continual reassessment method

PubMed Central

Horton, Bethany Jablonski; Wages, Nolan A.; Conaway, Mark R.

2016-01-01

Toxicity probability interval designs have received increasing attention as a dose-finding method in recent years. In this study, we compared the two-stage, likelihood-based continual reassessment method (CRM), modified toxicity probability interval (mTPI), and the Bayesian optimal interval design (BOIN) in order to evaluate each method's performance in dose selection for Phase I trials. We use several summary measures to compare the performance of these methods, including percentage of correct selection (PCS) of the true maximum tolerable dose (MTD), allocation of patients to doses at and around the true MTD, and an accuracy index. This index is an efficiency measure that describes the entire distribution of MTD selection and patient allocation by taking into account the distance between the true probability of toxicity at each dose level and the target toxicity rate. The simulation study considered a broad range of toxicity curves and various sample sizes. When considering PCS, we found that CRM outperformed the two competing methods in most scenarios, followed by BOIN, then mTPI. We observed a similar trend when considering the accuracy index for dose allocation, where CRM most often outperformed both the mTPI and BOIN. These trends were more pronounced with increasing number of dose levels. PMID:27435150

Engineering Student Self-Assessment through Confidence-Based Scoring

ERIC Educational Resources Information Center

Yuen-Reed, Gigi; Reed, Kyle B.

2015-01-01

A vital aspect of an answer is the confidence that goes along with it. Misstating the level of confidence one has in the answer can have devastating outcomes. However, confidence assessment is rarely emphasized during typical engineering education. The confidence-based scoring method described in this study encourages students to both think about…

Differentiating Small (≤1 cm) Focal Liver Lesions as Metastases or Cysts by means of Computed Tomography: A Case-Study to Illustrate a Fuzzy Logic-Based Method to Assess the Impact of Diagnostic Confidence on Radiological Diagnosis

PubMed Central

Zanella, Gloria; Pullini, Serena; Como, Giuseppe; Bazzocchi, Massimo

2014-01-01

Purpose. To quantify the impact of diagnostic confidence on radiological diagnosis with a fuzzy logic-based method. Materials and Methods. Twenty-two oncologic patients with 20 cysts and 30 metastases ≤1 cm in size found at 64-row computed tomography were included. Two readers (R1/R2) expressed diagnoses as a subjective level of confidence P(d) in malignancy within the interval [0,1] rather than on a “crisp” basis (malignant/benign); confidence in benignancy was 1 − p(d). When cross-tabulating data according to the standard of reference, 2 × 2 table cells resulted from the aggregation between p(d)/1 − p(d) and final diagnosis. We then assessed (i) readers diagnostic performance on a fuzzy and crisp basis; (ii) the “divergence” δ(F, C) (%) as a measure of how confidence impacted on crisp diagnosis. Results. Diagnoses expressed with lower confidence increased fuzzy false positives compared to crisp ones (from 0 to 0.2 for R1; from 1 to 2.4 for R2). Crisp/fuzzy accuracy was 94.0%/93.6% (R1) and 94.0/91.6% (R2). δ(F, C) (%) was larger in the case of the less experienced reader (R2) (up to +7.95% for specificity). According to simulations, δ(F, C) (%) was negative/positive depending on the level of confidence in incorrect diagnoses. Conclusion. Fuzzy evaluation shows a measurable effect of uncertainty on radiological diagnoses. PMID:24587815

On the Use of Experimental Methods to Improve Confidence in Educed Impedance

NASA Technical Reports Server (NTRS)

Jones, Michael G.; Watson, Willie R.

2011-01-01

Results from impedance eduction methods developed by NASA Langley Research Center are used throughout the acoustic liner community. In spite of recent enhancements, occasional anomalies persist with these methods, generally at frequencies where the liner produces minimal attenuation. This investigation demonstrates an experimental approach to educe impedance with increased confidence over a desired frequency range, by combining results from successive tests with different cavity depths. A series of tests is conducted with three wire-mesh facesheets, for which the results should be weakly dependent on source sound pressure level and mean grazing flow speed. First, a raylometer is used to measure the DC flow resistance of each facesheet. These facesheets are then mounted onto a frame and a normal incidence tube is used to determine their respective acoustic impedance spectra. A comparison of the acoustic resistance component with the DC flow resistance for each facesheet is used to validate the measurement process. Next, each facesheet is successively mounted onto three frames with different cavity depths, and a grazing flow impedance tube is used to educe their respective acoustic impedance spectra with and without mean flow. The no-flow results are compared with those measured in the normal incidence tube to validate the impedance eduction method. Since the anti-resonance frequency varies with cavity depth, each sample provides robust results over a different frequency range. Hence, a combination of results can be used to determine the facesheet acoustic resistance. When combined with the acoustic reactance, observed to be weakly dependent on the source sound pressure level and grazing flow Mach number, the acoustic impedance can be educed with increased confidence. Representative results of these tests are discussed, and the complete database is available in electronic format upon request.

Tracing the evolution of chiropractic students’ confidence in clinical and patient communication skills during a clinical internship: a multi-methods study

PubMed Central

2012-01-01

Background Anecdotal evidence points to variations in individual students’ evolving confidence in clinical and patient communication skills during a clinical internship. A better understanding of the specific aspects of internships that contribute to increasing or decreasing confidence is needed to best support students during the clinical component of their study. Methods A multi-method approach, combining two large-scale surveys with 269 students and three in-depth individual interviews with a sub-sample of 29 students, was used to investigate the evolution of change in student confidence during a 10-month long internship. Change in levels of confidence in patient communication and clinical skills was measured and relationship to demographic factors were explored. The interviews elicited students’ accounts and reflections on what affected the evolution of their confidence during the internship. Results At the start of their internship, students were more confident in their patient communication skills than their clinical skills but prior experience was significantly related to confidence in both. Initial confidence in patient communication skills was also related to age and prior qualification but not gender whilst confidence in clinical skills was related to gender but not age or prior qualification. These influences were maintained over time. Overall, students’ levels of confidence in patient communication and clinical skills confidence increased significantly over the duration of the internship with evidence that change over time in these two aspects were inter-related. To explore how specific aspects of the internship contributed to changing levels of confidence, two extreme sub-groups of interviewees were identified, those with the least increase and those with the highest increase in professional confidence over time. A number of key factors affecting the development of confidence were identified, including among others, interactions with clinicians

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Ying; Zhou, Zhi; Botterud, Audun

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixedmore » integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.« less

Predictor sort sampling and one-sided confidence bounds on quantiles

Treesearch

Steve Verrill; Victoria L. Herian; David W. Green

2002-01-01

Predictor sort experiments attempt to make use of the correlation between a predictor that can be measured prior to the start of an experiment and the response variable that we are investigating. Properly designed and analyzed, they can reduce necessary sample sizes, increase statistical power, and reduce the lengths of confidence intervals. However, if the non- random...

Calibration with confidence: a principled method for panel assessment.

PubMed

MacKay, R S; Kenna, R; Low, R J; Parker, S

2017-02-01

Frequently, a set of objects has to be evaluated by a panel of assessors, but not every object is assessed by every assessor. A problem facing such panels is how to take into account different standards among panel members and varying levels of confidence in their scores. Here, a mathematically based algorithm is developed to calibrate the scores of such assessors, addressing both of these issues. The algorithm is based on the connectivity of the graph of assessors and objects evaluated, incorporating declared confidences as weights on its edges. If the graph is sufficiently well connected, relative standards can be inferred by comparing how assessors rate objects they assess in common, weighted by the levels of confidence of each assessment. By removing these biases, 'true' values are inferred for all the objects. Reliability estimates for the resulting values are obtained. The algorithm is tested in two case studies: one by computer simulation and another based on realistic evaluation data. The process is compared to the simple averaging procedure in widespread use, and to Fisher's additive incomplete block analysis. It is anticipated that the algorithm will prove useful in a wide variety of situations such as evaluation of the quality of research submitted to national assessment exercises; appraisal of grant proposals submitted to funding panels; ranking of job applicants; and judgement of performances on degree courses wherein candidates can choose from lists of options.

Calibration with confidence: a principled method for panel assessment

PubMed Central

MacKay, R. S.; Low, R. J.; Parker, S.

2017-01-01

Frequently, a set of objects has to be evaluated by a panel of assessors, but not every object is assessed by every assessor. A problem facing such panels is how to take into account different standards among panel members and varying levels of confidence in their scores. Here, a mathematically based algorithm is developed to calibrate the scores of such assessors, addressing both of these issues. The algorithm is based on the connectivity of the graph of assessors and objects evaluated, incorporating declared confidences as weights on its edges. If the graph is sufficiently well connected, relative standards can be inferred by comparing how assessors rate objects they assess in common, weighted by the levels of confidence of each assessment. By removing these biases, ‘true’ values are inferred for all the objects. Reliability estimates for the resulting values are obtained. The algorithm is tested in two case studies: one by computer simulation and another based on realistic evaluation data. The process is compared to the simple averaging procedure in widespread use, and to Fisher's additive incomplete block analysis. It is anticipated that the algorithm will prove useful in a wide variety of situations such as evaluation of the quality of research submitted to national assessment exercises; appraisal of grant proposals submitted to funding panels; ranking of job applicants; and judgement of performances on degree courses wherein candidates can choose from lists of options. PMID:28386432

A text zero-watermarking method based on keyword dense interval

NASA Astrophysics Data System (ADS)

Yang, Fan; Zhu, Yuesheng; Jiang, Yifeng; Qing, Yin

2017-07-01

Digital watermarking has been recognized as a useful technology for the copyright protection and authentication of digital information. However, rarely did the former methods focus on the key content of digital carrier. The idea based on the protection of key content is more targeted and can be considered in different digital information, including text, image and video. In this paper, we use text as research object and a text zero-watermarking method which uses keyword dense interval (KDI) as the key content is proposed. First, we construct zero-watermarking model by introducing the concept of KDI and giving the method of KDI extraction. Second, we design detection model which includes secondary generation of zero-watermark and the similarity computing method of keyword distribution. Besides, experiments are carried out, and the results show that the proposed method gives better performance than other available methods especially in the attacks of sentence transformation and synonyms substitution.

Using spatially explicit surveillance models to provide confidence in the eradication of an invasive ant

PubMed Central

Ward, Darren F.; Anderson, Dean P.; Barron, Mandy C.

2016-01-01

Effective detection plays an important role in the surveillance and management of invasive species. Invasive ants are very difficult to eradicate and are prone to imperfect detection because of their small size and cryptic nature. Here we demonstrate the use of spatially explicit surveillance models to estimate the probability that Argentine ants (Linepithema humile) have been eradicated from an offshore island site, given their absence across four surveys and three surveillance methods, conducted since ant control was applied. The probability of eradication increased sharply as each survey was conducted. Using all surveys and surveillance methods combined, the overall median probability of eradication of Argentine ants was 0.96. There was a high level of confidence in this result, with a high Credible Interval Value of 0.87. Our results demonstrate the value of spatially explicit surveillance models for the likelihood of eradication of Argentine ants. We argue that such models are vital to give confidence in eradication programs, especially from highly valued conservation areas such as offshore islands. PMID:27721491

An interval precise integration method for transient unbalance response analysis of rotor system with uncertainty

NASA Astrophysics Data System (ADS)

Fu, Chao; Ren, Xingmin; Yang, Yongfeng; Xia, Yebao; Deng, Wangqun

2018-07-01

A non-intrusive interval precise integration method (IPIM) is proposed in this paper to analyze the transient unbalance response of uncertain rotor systems. The transfer matrix method (TMM) is used to derive the deterministic equations of motion of a hollow-shaft overhung rotor. The uncertain transient dynamic problem is solved by combing the Chebyshev approximation theory with the modified precise integration method (PIM). Transient response bounds are calculated by interval arithmetic of the expansion coefficients. Theoretical error analysis of the proposed method is provided briefly, and its accuracy is further validated by comparing with the scanning method in simulations. Numerical results show that the IPIM can keep good accuracy in vibration prediction of the start-up transient process. Furthermore, the proposed method can also provide theoretical guidance to other transient dynamic mechanical systems with uncertainties.

Stapled versus handsewn methods for colorectal anastomosis surgery.

PubMed

Lustosa, S A; Matos, D; Atallah, A N; Castro, A A

2001-01-01

anastomosis. The Risk Difference method (random effects model) and NNT for dichotomous outcomes measures and weighted mean difference for continuous outcomes measures, with the corresponding 95% confidence interval, were presented in this review. Statistical heterogeneity was evaluated by using funnel plot and chi-square testing. Of the 1233 patients enrolled ( in 9 trials), 622 were treated with stapled, and 611 with manual, suture. The following main results were obtained: a) Mortality: result based on 901 patients; Risk Difference - 0.6% Confidence Interval -2.8% to +1.6%. b) Overall Dehiscence: result based on 1233 patients; Risk Difference 0.2%, 95% Confidence Interval -5.0% to +5.3%. c) Clinical Anastomotic Dehiscence : result based on 1233 patients; Risk Difference -1.4%, 95% Confidence Interval -5.2 to +2.3%. d) Radiological Anastomotic Dehiscence : result based on 825 patients; Risk Difference 1.2%, 95% Confidence Interval -4.8% to +7.3%. e) Stricture: result based on 1042 patients; Risk Difference 4.6%, 95% Confidence Interval 1.2% to 8.1%. Number needed to treat 17, 95% confidence interval 12 to 31. f) Anastomotic Hemorrhage: result based on 662 patients; Risk Difference 2.7%, 95% Confidence Interval - 0.1% to +5.5%. g) Reoperation: result based on 544 patients; Risk Difference 3.9%, 95% Confidence Interval 0.3% to 7.4%. h) Wound Infection: result based on 567 patients; Risk Difference 1.0%, 95% Confidence Interval -2.2% to +4.3%. i) Anastomosis duration: result based on one study (159 patients); Weighted Mean Difference -7.6 minutes, 95% Confidence Interval -12.9 to -2.2 minutes. j) Hospital Stay: result based on one study (159 patients), Weighted Mean Difference 2.0 days, 95% Confidence Interval -3.27 to +7.2 days. The evidence found was insufficient to demonstrate any superiority of stapled over handsewn techniques in colorectal anastomosis, regardless of the level of anastomosis.

Interval estimation and optimal design for the within-subject coefficient of variation for continuous and binary variables

PubMed Central

Shoukri, Mohamed M; Elkum, Nasser; Walter, Stephen D

2006-01-01

Background In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. Methods We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. Results For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on pre-specified width of confidence interval are novel contribution to the literature for the binary variable. Conclusion Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary. PMID:16686943

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability.

PubMed

Beda, Alessandro; Simpson, David M; Faes, Luca

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications

The influence of interpregnancy interval on infant mortality.

PubMed

McKinney, David; House, Melissa; Chen, Aimin; Muglia, Louis; DeFranco, Emily

2017-03-01

-<6 months (adjusted relative risk, 1.32; 95% confidence interval, 1.17-1.49) followed by interpregnancy intervals of 6-<12 months (adjusted relative risk, 1.16; 95% confidence interval, 1.04-1.30). Analysis stratified by maternal race revealed similar findings. Attributable risk calculation showed that 24.2% of infant mortalities following intervals of 0-<6 months and 14.1% with intervals of 6-<12 months are attributable to the short interpregnancy interval. By avoiding short interpregnancy intervals of ≤12 months we estimate that in the state of Ohio 31 infant mortalities (20 white and 8 black) per year could have been prevented and the infant mortality rate could have been reduced from 7.2-7.0 during this time frame. An interpregnancy interval of 12-60 months (1-5 years) between birth and conception of next pregnancy is associated with lowest risk of infant mortality. Public health initiatives and provider counseling to optimize birth spacing has the potential to significantly reduce infant mortality for both white and black mothers. Copyright © 2017 Elsevier Inc. All rights reserved.

Vaccination Confidence and Parental Refusal/Delay of Early Childhood Vaccines.

PubMed

Gilkey, Melissa B; McRee, Annie-Laurie; Magnus, Brooke E; Reiter, Paul L; Dempsey, Amanda F; Brewer, Noel T

2016-01-01

To support efforts to address parental hesitancy towards early childhood vaccination, we sought to validate the Vaccination Confidence Scale using data from a large, population-based sample of U.S. parents. We used weighted data from 9,354 parents who completed the 2011 National Immunization Survey. Parents reported on the immunization history of a 19- to 35-month-old child in their households. Healthcare providers then verified children's vaccination status for vaccines including measles, mumps, and rubella (MMR), varicella, and seasonal flu. We used separate multivariable logistic regression models to assess associations between parents' mean scores on the 8-item Vaccination Confidence Scale and vaccine refusal, vaccine delay, and vaccination status. A substantial minority of parents reported a history of vaccine refusal (15%) or delay (27%). Vaccination confidence was negatively associated with refusal of any vaccine (odds ratio [OR] = 0.58, 95% confidence interval [CI], 0.54-0.63) as well as refusal of MMR, varicella, and flu vaccines specifically. Negative associations between vaccination confidence and measures of vaccine delay were more moderate, including delay of any vaccine (OR = 0.81, 95% CI, 0.76-0.86). Vaccination confidence was positively associated with having received vaccines, including MMR (OR = 1.53, 95% CI, 1.40-1.68), varicella (OR = 1.54, 95% CI, 1.42-1.66), and flu vaccines (OR = 1.32, 95% CI, 1.23-1.42). Vaccination confidence was consistently associated with early childhood vaccination behavior across multiple vaccine types. Our findings support expanding the application of the Vaccination Confidence Scale to measure vaccination beliefs among parents of young children.

A Very Simple Method to Calculate the (Positive) Largest Lyapunov Exponent Using Interval Extensions

NASA Astrophysics Data System (ADS)

Mendes, Eduardo M. A. M.; Nepomuceno, Erivelton G.

2016-12-01

In this letter, a very simple method to calculate the positive Largest Lyapunov Exponent (LLE) based on the concept of interval extensions and using the original equations of motion is presented. The exponent is estimated from the slope of the line derived from the lower bound error when considering two interval extensions of the original system. It is shown that the algorithm is robust, fast and easy to implement and can be considered as alternative to other algorithms available in the literature. The method has been successfully tested in five well-known systems: Logistic, Hénon, Lorenz and Rössler equations and the Mackey-Glass system.

Design of time interval generator based on hybrid counting method

NASA Astrophysics Data System (ADS)

Yao, Yuan; Wang, Zhaoqi; Lu, Houbing; Chen, Lian; Jin, Ge

2016-10-01

Time Interval Generators (TIGs) are frequently used for the characterizations or timing operations of instruments in particle physics experiments. Though some "off-the-shelf" TIGs can be employed, the necessity of a custom test system or control system makes the TIGs, being implemented in a programmable device desirable. Nowadays, the feasibility of using Field Programmable Gate Arrays (FPGAs) to implement particle physics instrumentation has been validated in the design of Time-to-Digital Converters (TDCs) for precise time measurement. The FPGA-TDC technique is based on the architectures of Tapped Delay Line (TDL), whose delay cells are down to few tens of picosecond. In this case, FPGA-based TIGs with high delay step are preferable allowing the implementation of customized particle physics instrumentations and other utilities on the same FPGA device. A hybrid counting method for designing TIGs with both high resolution and wide range is presented in this paper. The combination of two different counting methods realizing an integratable TIG is described in detail. A specially designed multiplexer for tap selection is emphatically introduced. The special structure of the multiplexer is devised for minimizing the different additional delays caused by the unpredictable routings from different taps to the output. A Kintex-7 FPGA is used for the hybrid counting-based implementation of a TIG, providing a resolution up to 11 ps and an interval range up to 8 s.

A science confidence gap: Education, trust in scientific methods, and trust in scientific institutions in the United States, 2014.

PubMed

Achterberg, Peter; de Koster, Willem; van der Waal, Jeroen

2017-08-01

Following up on suggestions that attitudes toward science are multi-dimensional, we analyze nationally representative survey data collected in the United States in 2014 ( N = 2006), and demonstrate the existence of a science confidence gap: some people place great trust in scientific methods and principles, but simultaneously distrust scientific institutions. This science confidence gap is strongly associated with level of education: it is larger among the less educated than among the more educated. We investigate explanations for these educational differences. Whereas hypotheses deduced from reflexive-modernization theory do not pass the test, those derived from theorizing on the role of anomie are corroborated. The less educated are more anomic (they have more modernity-induced cultural discontents), which not only underlies their distrust in scientific institutions, but also fuels their trust in scientific methods and principles. This explains why this science confidence gap is most pronounced among the less educated.

An automated and objective method for age partitioning of reference intervals based on continuous centile curves.

PubMed

Yang, Qian; Lew, Hwee Yeong; Peh, Raymond Hock Huat; Metz, Michael Patrick; Loh, Tze Ping

2016-10-01

Reference intervals are the most commonly used decision support tool when interpreting quantitative laboratory results. They may require partitioning to better describe subpopulations that display significantly different reference values. Partitioning by age is particularly important for the paediatric population since there are marked physiological changes associated with growth and maturation. However, most partitioning methods are either technically complex or require prior knowledge of the underlying physiology/biological variation of the population. There is growing interest in the use of continuous centile curves, which provides seamless laboratory reference values as a child grows, as an alternative to rigidly described fixed reference intervals. However, the mathematical functions that describe these curves can be complex and may not be easily implemented in laboratory information systems. Hence, the use of fixed reference intervals is expected to continue for a foreseeable time. We developed a method that objectively proposes optimised age partitions and reference intervals for quantitative laboratory data (http://research.sph.nus.edu.sg/pp/ppResult.aspx), based on the sum of gradient that best describes the underlying distribution of the continuous centile curves. It is hoped that this method may improve the selection of age intervals for partitioning, which is receiving increasing attention in paediatric laboratory medicine. Copyright © 2016 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.

What Are Confidence Judgments Made of? Students' Explanations for Their Confidence Ratings and What that Means for Calibration

ERIC Educational Resources Information Center

Dinsmore, Daniel L.; Parkinson, Meghan M.

2013-01-01

Although calibration has been widely studied, questions remain about how best to capture confidence ratings, how to calculate continuous variable calibration indices, and on what exactly students base their reported confidence ratings. Undergraduates in a research methods class completed a prior knowledge assessment, two sets of readings and…

An interval model updating strategy using interval response surface models

NASA Astrophysics Data System (ADS)

Fang, Sheng-En; Zhang, Qiu-Hu; Ren, Wei-Xin

2015-08-01

Stochastic model updating provides an effective way of handling uncertainties existing in real-world structures. In general, probabilistic theories, fuzzy mathematics or interval analyses are involved in the solution of inverse problems. However in practice, probability distributions or membership functions of structural parameters are often unavailable due to insufficient information of a structure. At this moment an interval model updating procedure shows its superiority in the aspect of problem simplification since only the upper and lower bounds of parameters and responses are sought. To this end, this study develops a new concept of interval response surface models for the purpose of efficiently implementing the interval model updating procedure. The frequent interval overestimation due to the use of interval arithmetic can be maximally avoided leading to accurate estimation of parameter intervals. Meanwhile, the establishment of an interval inverse problem is highly simplified, accompanied by a saving of computational costs. By this means a relatively simple and cost-efficient interval updating process can be achieved. Lastly, the feasibility and reliability of the developed method have been verified against a numerical mass-spring system and also against a set of experimentally tested steel plates.

The method of micro-motion cycle feature extraction based on confidence coefficient evaluation criteria

NASA Astrophysics Data System (ADS)

Tang, Chuanzi; Ren, Hongmei; Bo, Li; Jing, Huang

2017-11-01

In radar target recognition, the micro motion characteristics of target is one of the characteristics that researchers pay attention to at home and abroad, in which the characteristics of target precession cycle is one of the important characteristics of target movement characteristics. Periodic feature extraction methods have been studied for years, the complex shape of the target and the scattering center stack lead to random fluctuations of the RCS. These random fluctuations also exist certain periodicity, which has a great influence on the target recognition result. In order to solve the problem, this paper proposes a extraction method of micro-motion cycle feature based on confidence coefficient evaluation criteria.

The Confidence-Accuracy Relationship for Eyewitness Identification Decisions: Effects of Exposure Duration, Retention Interval, and Divided Attention

ERIC Educational Resources Information Center

Palmer, Matthew A.; Brewer, Neil; Weber, Nathan; Nagesh, Ambika

2013-01-01

Prior research points to a meaningful confidence-accuracy (CA) relationship for positive identification decisions. However, there are theoretical grounds for expecting that different aspects of the CA relationship (calibration, resolution, and over/underconfidence) might be undermined in some circumstances. This research investigated whether the…

Trends in P Value, Confidence Interval, and Power Analysis Reporting in Health Professions Education Research Reports: A Systematic Appraisal.

PubMed

Abbott, Eduardo F; Serrano, Valentina P; Rethlefsen, Melissa L; Pandian, T K; Naik, Nimesh D; West, Colin P; Pankratz, V Shane; Cook, David A

2018-02-01

To characterize reporting of P values, confidence intervals (CIs), and statistical power in health professions education research (HPER) through manual and computerized analysis of published research reports. The authors searched PubMed, Embase, and CINAHL in May 2016, for comparative research studies. For manual analysis of abstracts and main texts, they randomly sampled 250 HPER reports published in 1985, 1995, 2005, and 2015, and 100 biomedical research reports published in 1985 and 2015. Automated computerized analysis of abstracts included all HPER reports published 1970-2015. In the 2015 HPER sample, P values were reported in 69/100 abstracts and 94 main texts. CIs were reported in 6 abstracts and 22 main texts. Most P values (≥77%) were ≤.05. Across all years, 60/164 two-group HPER studies had ≥80% power to detect a between-group difference of 0.5 standard deviations. From 1985 to 2015, the proportion of HPER abstracts reporting a CI did not change significantly (odds ratio [OR] 2.87; 95% CI 1.04, 7.88) whereas that of main texts reporting a CI increased (OR 1.96; 95% CI 1.39, 2.78). Comparison with biomedical studies revealed similar reporting of P values, but more frequent use of CIs in biomedicine. Automated analysis of 56,440 HPER abstracts found 14,867 (26.3%) reporting a P value, 3,024 (5.4%) reporting a CI, and increased reporting of P values and CIs from 1970 to 2015. P values are ubiquitous in HPER, CIs are rarely reported, and most studies are underpowered. Most reported P values would be considered statistically significant.

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability

PubMed Central

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications

An innovative method for offshore wind farm site selection based on the interval number with probability distribution

NASA Astrophysics Data System (ADS)

Wu, Yunna; Chen, Kaifeng; Xu, Hu; Xu, Chuanbo; Zhang, Haobo; Yang, Meng

2017-12-01

There is insufficient research relating to offshore wind farm site selection in China. The current methods for site selection have some defects. First, information loss is caused by two aspects: the implicit assumption that the probability distribution on the interval number is uniform; and ignoring the value of decision makers' (DMs') common opinion on the criteria information evaluation. Secondly, the difference in DMs' utility function has failed to receive attention. An innovative method is proposed in this article to solve these drawbacks. First, a new form of interval number and its weighted operator are proposed to reflect the uncertainty and reduce information loss. Secondly, a new stochastic dominance degree is proposed to quantify the interval number with a probability distribution. Thirdly, a two-stage method integrating the weighted operator with stochastic dominance degree is proposed to evaluate the alternatives. Finally, a case from China proves the effectiveness of this method.

Adding Confidence to Knowledge

ERIC Educational Resources Information Center

Goodson, Ludwika Aniela; Slater, Don; Zubovic, Yvonne

2015-01-01

A "knowledge survey" and a formative evaluation process led to major changes in an instructor's course and teaching methods over a 5-year period. Design of the survey incorporated several innovations, including: a) using "confidence survey" rather than "knowledge survey" as the title; b) completing an instructional…

Counting Better? An Examination of the Impact of Quantitative Method Teaching on Statistical Anxiety and Confidence

ERIC Educational Resources Information Center

Chamberlain, John Martyn; Hillier, John; Signoretta, Paola

2015-01-01

This article reports the results of research concerned with students' statistical anxiety and confidence to both complete and learn to complete statistical tasks. Data were collected at the beginning and end of a quantitative method statistics module. Students recognised the value of numeracy skills but felt they were not necessarily relevant for…

Bootstrap confidence levels for phylogenetic trees.

PubMed

Efron, B; Halloran, E; Holmes, S

1996-07-09

Evolutionary trees are often estimated from DNA or RNA sequence data. How much confidence should we have in the estimated trees? In 1985, Felsenstein [Felsenstein, J. (1985) Evolution 39, 783-791] suggested the use of the bootstrap to answer this question. Felsenstein's method, which in concept is a straightforward application of the bootstrap, is widely used, but has been criticized as biased in the genetics literature. This paper concerns the use of the bootstrap in the tree problem. We show that Felsenstein's method is not biased, but that it can be corrected to better agree with standard ideas of confidence levels and hypothesis testing. These corrections can be made by using the more elaborate bootstrap method presented here, at the expense of considerably more computation.

Communication confidence in persons with aphasia.

PubMed

Babbitt, Edna M; Cherney, Leora R

2010-01-01

Communication confidence is a construct that has not been explored in the aphasia literature. Recently, national and international organizations have endorsed broader assessment methods that address quality of life and include participation, activity, and impairment domains as well as psychosocial areas. Individuals with aphasia encounter difficulties in all these areas on a daily basis in living with a communication disorder. Improvements are often reflected in narratives that are not typically included in standard assessments. This article illustrates how a new instrument measuring communication confidence might fit into a broad assessment framework and discusses the interaction of communication confidence, autonomy, and self-determination for individuals living with aphasia.

Iron Metabolism Genes, Low-Level Lead Exposure, and QT Interval

PubMed Central

Park, Sung Kyun; Hu, Howard; Wright, Robert O.; Schwartz, Joel; Cheng, Yawen; Sparrow, David; Vokonas, Pantel S.; Weisskopf, Marc G.

2009-01-01

Background Cumulative exposure to lead has been shown to be associated with depression of electrocardiographic conduction, such as QT interval (time from start of the Q wave to end of the T wave). Because iron can enhance the oxidative effects of lead, we examined whether polymorphisms in iron metabolism genes [hemochromatosis (HFE), transferrin (TF) C2, and heme oxygenase-1 (HMOX-1)] increase susceptibility to the effects of lead on QT interval in 613 community-dwelling older men. Methods We used standard 12-lead electrocardiograms, K-shell X-ray fluorescence, and graphite furnace atomic absorption spectrometry to measure QT interval, bone lead, and blood lead levels, respectively. Results A one-interquartile-range increase in tibia lead level (13 μg/g) was associated with a 11.35-msec [95% confidence interval (CI), 4.05–18.65 msec] and a 6.81-msec (95% CI, 1.67–11.95 msec) increase in the heart-rate–corrected QT interval among persons carrying long HMOX-1 alleles and at least one copy of an HFE variant, respectively, but had no effect in persons with short and middle HMOX-1 alleles and the wild-type HFE genotype. The lengthening of the heart-rate–corrected QT interval with higher tibia lead and blood lead became more pronounced as the total number (0 vs. 1 vs. ≥2) of gene variants increased (tibia, p-trend = 0.01; blood, p-trend = 0.04). This synergy seems to be driven by a joint effect between HFE variant and HMOX-1 L alleles. Conclusion We found evidence that gene variants related to iron metabolism increase the impacts of low-level lead exposure on the prolonged QT interval. This is the first such report, so these results should be interpreted cautiously and need to be independently verified. PMID:19165391

A modified hybrid uncertain analysis method for dynamic response field of the LSOAAC with random and interval parameters

NASA Astrophysics Data System (ADS)

Zi, Bin; Zhou, Bin

2016-07-01

For the prediction of dynamic response field of the luffing system of an automobile crane (LSOAAC) with random and interval parameters, a hybrid uncertain model is introduced. In the hybrid uncertain model, the parameters with certain probability distribution are modeled as random variables, whereas, the parameters with lower and upper bounds are modeled as interval variables instead of given precise values. Based on the hybrid uncertain model, the hybrid uncertain dynamic response equilibrium equation, in which different random and interval parameters are simultaneously included in input and output terms, is constructed. Then a modified hybrid uncertain analysis method (MHUAM) is proposed. In the MHUAM, based on random interval perturbation method, the first-order Taylor series expansion and the first-order Neumann series, the dynamic response expression of the LSOAAC is developed. Moreover, the mathematical characteristics of extrema of bounds of dynamic response are determined by random interval moment method and monotonic analysis technique. Compared with the hybrid Monte Carlo method (HMCM) and interval perturbation method (IPM), numerical results show the feasibility and efficiency of the MHUAM for solving the hybrid LSOAAC problems. The effects of different uncertain models and parameters on the LSOAAC response field are also investigated deeply, and numerical results indicate that the impact made by the randomness in the thrust of the luffing cylinder F is larger than that made by the gravity of the weight in suspension Q . In addition, the impact made by the uncertainty in the displacement between the lower end of the lifting arm and the luffing cylinder a is larger than that made by the length of the lifting arm L .

Confidence Wagering during Mathematics and Science Testing

ERIC Educational Resources Information Center

Jack, Brady Michael; Liu, Chia-Ju; Chiu, Hoan-Lin; Shymansky, James A.

2009-01-01

This proposal presents the results of a case study involving five 8th grade Taiwanese classes, two mathematics and three science classes. These classes used a new method of testing called confidence wagering. This paper advocates the position that confidence wagering can predict the accuracy of a student's test answer selection during…

Method of confidence domains in the analysis of noise-induced extinction for tritrophic population system

NASA Astrophysics Data System (ADS)

Bashkirtseva, Irina; Ryashko, Lev; Ryazanova, Tatyana

2017-09-01

A problem of the analysis of the noise-induced extinction in multidimensional population systems is considered. For the investigation of conditions of the extinction caused by random disturbances, a new approach based on the stochastic sensitivity function technique and confidence domains is suggested, and applied to tritrophic population model of interacting prey, predator and top predator. This approach allows us to analyze constructively the probabilistic mechanisms of the transition to the noise-induced extinction from both equilibrium and oscillatory regimes of coexistence. In this analysis, a method of principal directions for the reducing of the dimension of confidence domains is suggested. In the dispersion of random states, the principal subspace is defined by the ratio of eigenvalues of the stochastic sensitivity matrix. A detailed analysis of two scenarios of the noise-induced extinction in dependence on parameters of considered tritrophic system is carried out.

Confidence regions of planar cardiac vectors

NASA Technical Reports Server (NTRS)

Dubin, S.; Herr, A.; Hunt, P.

1980-01-01

A method for plotting the confidence regions of vectorial data obtained in electrocardiology is presented. The 90%, 95% and 99% confidence regions of cardiac vectors represented in a plane are obtained in the form of an ellipse centered at coordinates corresponding to the means of a sample selected at random from a bivariate normal distribution. An example of such a plot for the frontal plane QRS mean electrical axis for 80 horses is also presented.

One-way ANOVA based on interval information

NASA Astrophysics Data System (ADS)

Hesamian, Gholamreza

2016-08-01

This paper deals with extending the one-way analysis of variance (ANOVA) to the case where the observed data are represented by closed intervals rather than real numbers. In this approach, first a notion of interval random variable is introduced. Especially, a normal distribution with interval parameters is introduced to investigate hypotheses about the equality of interval means or test the homogeneity of interval variances assumption. Moreover, the least significant difference (LSD method) for investigating multiple comparison of interval means is developed when the null hypothesis about the equality of means is rejected. Then, at a given interval significance level, an index is applied to compare the interval test statistic and the related interval critical value as a criterion to accept or reject the null interval hypothesis of interest. Finally, the method of decision-making leads to some degrees to accept or reject the interval hypotheses. An applied example will be used to show the performance of this method.

Validity and applicability of a new recording method for hypertension.

PubMed

Mas-Heredia, Minerva; Molés-Moliner, Eloisa; González-de Paz, Luis; Kostov, Belchin; Ortiz-Molina, Jacinto; Mauri-Vázquez, Vanesa; Menacho-Pascual, Ignacio; Cararach-Salami, Daniel; Sierra-Benito, Cristina; Sisó-Almirall, Antoni

2014-09-01

Blood pressure measurement methods and conditions are determinants of hypertension diagnosis. A recent British guideline recommends systematic 24-h ambulatory blood pressure monitoring. However, these devices are not available at all health centers and they can only be used by 1 patient per day. The aim of this study was to test a new blood pressure recording method to see if it gave the same diagnostic results as 24-h blood pressure monitoring. One-hour blood pressure monitoring under routine clinical practice conditions was compared with standard method of day time recording by analyzing the coefficient of correlation and Bland-Altman plots. The Kappa index was used to calculate degree of agreement. Method sensitivity and specificity were also analyzed. Of the 102 participants, 89 (87.3%) obtained the same diagnosis regardless of method, with high between-method agreement (κ= 0.81; 95% confidence interval, 0.71-0.91). We observed robust correlations between diastolic (r=0.85) and systolic blood pressure (r=0.76) readings. Sensitivity and specificity for the new method for diagnosing white coat hypertension were 85.2% (95% confidence interval 67.5%-94.1%) and 92% (95% confidence interval, 83.6%-96.3%), respectively. One-hour blood pressure monitoring is a valid and reliable method for diagnosing hypertension and for classifying hypertension subpopulations, especially in white coat hypertension and refractory hypertension. This also leads to a more productive use of monitoring instruments. Copyright © 2013 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

Power, effects, confidence, and significance: an investigation of statistical practices in nursing research.

PubMed

Gaskin, Cadeyrn J; Happell, Brenda

2014-05-01

To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported. Statistical review. Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified. The median power to detect small, medium, and large effect sizes was .40 (interquartile range [IQR]=.24-.71), .98 (IQR=.85-1.00), and 1.00 (IQR=1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR=.26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers. The use, reporting, and interpretation of inferential statistics in nursing research need substantial

Statistical evaluation of time-dependent metabolite concentrations: estimation of post-mortem intervals based on in situ 1H-MRS of the brain.

PubMed

Scheurer, Eva; Ith, Michael; Dietrich, Daniel; Kreis, Roland; Hüsler, Jürg; Dirnhofer, Richard; Boesch, Chris

2005-05-01

Knowledge of the time interval from death (post-mortem interval, PMI) has an enormous legal, criminological and psychological impact. Aiming to find an objective method for the determination of PMIs in forensic medicine, 1H-MR spectroscopy (1H-MRS) was used in a sheep head model to follow changes in brain metabolite concentrations after death. Following the characterization of newly observed metabolites (Ith et al., Magn. Reson. Med. 2002; 5: 915-920), the full set of acquired spectra was analyzed statistically to provide a quantitative estimation of PMIs with their respective confidence limits. In a first step, analytical mathematical functions are proposed to describe the time courses of 10 metabolites in the decomposing brain up to 3 weeks post-mortem. Subsequently, the inverted functions are used to predict PMIs based on the measured metabolite concentrations. Individual PMIs calculated from five different metabolites are then pooled, being weighted by their inverse variances. The predicted PMIs from all individual examinations in the sheep model are compared with known true times. In addition, four human cases with forensically estimated PMIs are compared with predictions based on single in situ MRS measurements. Interpretation of the individual sheep examinations gave a good correlation up to 250 h post-mortem, demonstrating that the predicted PMIs are consistent with the data used to generate the model. Comparison of the estimated PMIs with the forensically determined PMIs in the four human cases shows an adequate correlation. Current PMI estimations based on forensic methods typically suffer from uncertainties in the order of days to weeks without mathematically defined confidence information. In turn, a single 1H-MRS measurement of brain tissue in situ results in PMIs with defined and favorable confidence intervals in the range of hours, thus offering a quantitative and objective method for the determination of PMIs. Copyright 2004 John Wiley & Sons, Ltd.

An Assessment of Pharmacy Student Confidence in Learning.

ERIC Educational Resources Information Center

Popovich, Nicholas G.; Rogers, Wallace J.

1987-01-01

A study to determine student knowledge and confidence in that knowledge when answering multiple-choice examination questions in a nonprescription drug course is described. An alternate approach to methods of confidence testing was investigated. The knowledge and experience survey is appended. (Author/MLW)

Contrasting effects of a mixed-methods high-intensity interval training intervention in girl football players.

PubMed

Wright, Matthew D; Hurst, Christopher; Taylor, Jonathan M

2016-10-01

Little is known about the responses of girl athletes to training interventions throughout maturation. This study evaluated group and individual responses to an 8-week, mixed-methods, high-intensity interval training (HIIT) programme in girl football players. Thirty-seven players (age 13.4 ± 1.5 years) were tested for 20-m speed, repeated-sprint ability, change-of-direction speed and level 1 yo-yo intermittent recovery (YYIR). Players were subcategorised into before-, at- and after-PHV (peak height velocity) based on maturity offset. Very likely moderate (25%; ±90% confidence limits = 9.2) improvements occurred in YYIR, but data were unclear in players before-PHV with moderate individual differences in response. Decrements in repeated-sprint ability were most likely very large (6.5%; ±3.2) before-PHV, and likely moderate (1.7%; ±1.0) at-PHV. Data were unclear after-PHV. A very likely moderate (2.7%; ±1.0) decrement occurred in change-of-direction speed at-PHV while there was a very likely increase (-2.4%; ±1.3) in after-PHV players. Possibly small (-1.1%; ±1.4) improvements in 20-m speed occurred before-PHV but the effect was otherwise unclear with moderate to large individual differences. These data reflect specific responses to training interventions in girls of different biological maturity, while highlighting individual responses to HIIT interventions. This can assist practitioners in providing effective training prescription.

Bayesian Methods and Confidence Intervals for Automatic Target Recognition of SAR Canonical Shapes

DTIC Science & Technology

2014-03-27

and DirectX [22]. The CUDA platform was developed by the NVIDIA Corporation to allow programmers access to the computational capabilities of the...were used for the intense repetitive computations. Developing CUDA software requires writing code for specialized compilers provided by NVIDIA and

Identifying the Effects of Unjustified Confidence versus Overconfidence: Lessons Learned from Two Analytic Methods

PubMed Central

Parker, Andrew M.; Stone, Eric R.

2013-01-01

One of the most common findings in behavioral decision research is that people have unrealistic beliefs about how much they know. However, demonstrating that misplaced confidence exists does not necessarily mean that there are costs to it. This paper contrasts two approaches toward answering whether misplaced confidence is good or bad, which we have labeled the overconfidence and unjustified confidence approach. We first consider conceptual and analytic issues distinguishing these approaches. Then, we provide findings from a set of simulations designed to determine when the approaches produce different conclusions across a range of possible confidence-knowledge-outcome relationships. Finally, we illustrate the main findings from the simulations with three empirical examples drawn from our own data. We conclude that the unjustified confidence approach is typically the preferred approach, both because it is appropriate for testing a larger set of psychological mechanisms as well as for methodological reasons. PMID:25309037

Increasing Product Confidence-Shifting Paradigms.

PubMed

Phillips, Marla; Kashyap, Vishal; Cheung, Mee-Shew

2015-01-01

Leaders in the pharmaceutical, medical device, and food industries expressed a unilateral concern over product confidence throughout the total product lifecycle, an unsettling fact for these leaders to manage given that their products affect the lives of millions of people each year. Fueled by the heparin incident of intentional adulteration in 2008, initial efforts for increasing product confidence were focused on improving the confidence of incoming materials, with a belief that supplier performance must be the root cause. As in the heparin case, concern over supplier performance extended deep into the supply chain to include suppliers of the suppliers-which is often a blind spot for pharmaceutical, device, and food manufacturers. Resolved to address the perceived lack of supplier performance, these U.S. Food and Drug Administration (FDA)-regulated industries began to adopt the supplier relationship management strategy, developed by the automotive industry, that emphasizes "management" of suppliers for the betterment of the manufacturers. Current product and supplier management strategies, however, have not led to a significant improvement in product confidence. As a result of the enduring concern by industry leaders over the lack of product confidence, Xavier University launched the Integrity of Supply Initiative in 2012 with a team of industry leaders and FDA officials. Through a methodical research approach, data generated by the pharmaceutical, medical device, and food manufacturers surprisingly pointed to themselves as a source of the lack of product confidence, and revealed that manufacturers either unknowingly increase the potential for error or can control/prevent many aspects of product confidence failure. It is only through this paradigm shift that manufacturers can work collaboratively with their suppliers as equal partners, instead of viewing their suppliers as "lesser" entities needing to be controlled. The basis of this shift provides manufacturers

A multi-professional educational intervention to improve and sustain respondents' confidence to deliver palliative care: A mixed-methods study.

PubMed

Reed, Elizabeth; Todd, Jennifer; Lawton, Sally; Grant, Robert; Sadler, Clair; Berg, Jane; Lucas, Caroline; Watson, Max

2018-02-01

Education has been highlighted as fundamental in equipping healthcare professionals with essential knowledge and skills to provide good end-of-life care. Multiprofessional educational programmes have a positive influence on knowledge, attitude and confidence but few have sought to understand the longer term impact on care delivery. The European Certificate in Essential Palliative Care is an 8-week home-study-based programme for healthcare professionals and is currently run in nine centres. Successful candidates have undertaken the course from their own countries around the world. This article describes the evaluation of the European Certificate in Essential Palliative Care which has been evolving over 15 years. To evaluate the impact an educational intervention has on participants' confidence in palliative care, to determine whether this is sustained over time and explore participants' perception of the influence of the course on confidence. A mixed-method longitudinal approach. A survey using a self-efficacy scale was emailed to 342 candidates who received an educational intervention and semi-structured interviews to a sub-sample of 15 candidates at baseline, 3 and 6 months. At 3 months, candidates had almost 20 times higher odds of being above any given level of confidence than at baseline which was sustained at 6 months. Qualitative analysis identified examples of increased competence and confidence improving palliative care delivery. Findings suggest that the European Certificate in Essential Palliative Care improves confidence in palliative care and that this is sustained over time with evidence of confidence in symptom control, communication and a holistic approach in clinical practice.

A Model For Teaching Advanced Neuroscience Methods: A Student-Run Seminar to Increase Practical Understanding and Confidence

PubMed Central

Harrison, Theresa M.; Ching, Christopher R. K.; Andrews, Anne M.

2016-01-01

Neuroscience doctoral students must master specific laboratory techniques and approaches to complete their thesis work (hands-on learning). Due to the highly interdisciplinary nature of the field, learning about a diverse range of methodologies through literature surveys and coursework is also necessary for student success (hands-off learning). Traditional neuroscience coursework stresses what is known about the nervous system with relatively little emphasis on the details of the methods used to obtain this knowledge. Furthermore, hands-off learning is made difficult by a lack of detail in methods sections of primary articles, subfield-specific jargon and vague experimental rationales. We designed a student-taught course to enable first-year neuroscience doctoral students to overcome difficulties in hands-off learning by introducing a new approach to reading and presenting primary research articles that focuses on methodology. In our literature-based course students were encouraged to present a method with which they had no previous experience. To facilitate weekly discussions, “experts” were invited to class sessions. Experts were advanced graduate students who had hands-on experience with the method being covered and served as discussion co-leaders. Self-evaluation worksheets were administered on the first and last days of the 10-week course and used to assess students’ confidence in discussing research and methods outside of their primary research expertise. These evaluations revealed that the course significantly increased the students’ confidence in reading, presenting and discussing a wide range of advanced neuroscience methods. PMID:27980464

How to regress and predict in a Bland-Altman plot? Review and contribution based on tolerance intervals and correlated-errors-in-variables models.

PubMed

Francq, Bernard G; Govaerts, Bernadette

2016-06-30

Two main methodologies for assessing equivalence in method-comparison studies are presented separately in the literature. The first one is the well-known and widely applied Bland-Altman approach with its agreement intervals, where two methods are considered interchangeable if their differences are not clinically significant. The second approach is based on errors-in-variables regression in a classical (X,Y) plot and focuses on confidence intervals, whereby two methods are considered equivalent when providing similar measures notwithstanding the random measurement errors. This paper reconciles these two methodologies and shows their similarities and differences using both real data and simulations. A new consistent correlated-errors-in-variables regression is introduced as the errors are shown to be correlated in the Bland-Altman plot. Indeed, the coverage probabilities collapse and the biases soar when this correlation is ignored. Novel tolerance intervals are compared with agreement intervals with or without replicated data, and novel predictive intervals are introduced to predict a single measure in an (X,Y) plot or in a Bland-Atman plot with excellent coverage probabilities. We conclude that the (correlated)-errors-in-variables regressions should not be avoided in method comparison studies, although the Bland-Altman approach is usually applied to avert their complexity. We argue that tolerance or predictive intervals are better alternatives than agreement intervals, and we provide guidelines for practitioners regarding method comparison studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Tracing the evolution of chiropractic students' confidence in clinical and patient communication skills during a clinical internship: a multi-methods study.

PubMed

Hecimovich, Mark; Volet, Simone

2012-06-19

Anecdotal evidence points to variations in individual students' evolving confidence in clinical and patient communication skills during a clinical internship. A better understanding of the specific aspects of internships that contribute to increasing or decreasing confidence is needed to best support students during the clinical component of their study. A multi-method approach, combining two large-scale surveys with 269 students and three in-depth individual interviews with a sub-sample of 29 students, was used to investigate the evolution of change in student confidence during a 10-month long internship. Change in levels of confidence in patient communication and clinical skills was measured and relationship to demographic factors were explored. The interviews elicited students' accounts and reflections on what affected the evolution of their confidence during the internship. At the start of their internship, students were more confident in their patient communication skills than their clinical skills but prior experience was significantly related to confidence in both. Initial confidence in patient communication skills was also related to age and prior qualification but not gender whilst confidence in clinical skills was related to gender but not age or prior qualification. These influences were maintained over time. Overall, students' levels of confidence in patient communication and clinical skills confidence increased significantly over the duration of the internship with evidence that change over time in these two aspects were inter-related. To explore how specific aspects of the internship contributed to changing levels of confidence, two extreme sub-groups of interviewees were identified, those with the least increase and those with the highest increase in professional confidence over time. A number of key factors affecting the development of confidence were identified, including among others, interactions with clinicians and patients, personal agency and

The establishment of tocopherol reference intervals for Hungarian adult population using a validated HPLC method.

PubMed

Veres, Gábor; Szpisjak, László; Bajtai, Attila; Siska, Andrea; Klivényi, Péter; Ilisz, István; Földesi, Imre; Vécsei, László; Zádori, Dénes

2017-09-01

Evidence suggests that decreased α-tocopherol (the most biologically active substance in the vitamin E group) level can cause neurological symptoms, most likely ataxia. The aim of the current study was to first provide reference intervals for serum tocopherols in the adult Hungarian population with appropriate sample size, recruiting healthy control subjects and neurological patients suffering from conditions without symptoms of ataxia, myopathy or cognitive deficiency. A validated HPLC method applying a diode array detector and rac-tocol as internal standard was utilized for that purpose. Furthermore, serum cholesterol levels were determined as well for data normalization. The calculated 2.5-97.5% reference intervals for α-, β/γ- and δ-tocopherols were 24.62-54.67, 0.81-3.69 and 0.29-1.07 μm, respectively, whereas the tocopherol/cholesterol ratios were 5.11-11.27, 0.14-0.72 and 0.06-0.22 μmol/mmol, respectively. The establishment of these reference intervals may improve the diagnostic accuracy of tocopherol measurements in certain neurological conditions with decreased tocopherol levels. Moreover, the current study draws special attention to the possible pitfalls in the complex process of the determination of reference intervals as well, including the selection of study population, the application of internal standard and method validation and the calculation of tocopherol/cholesterol ratios. Copyright © 2017 John Wiley & Sons, Ltd.

Simultaneous confidence sets for several effective doses.

PubMed

Tompsett, Daniel M; Biedermann, Stefanie; Liu, Wei

2018-04-03

Construction of simultaneous confidence sets for several effective doses currently relies on inverting the Scheffé type simultaneous confidence band, which is known to be conservative. We develop novel methodology to make the simultaneous coverage closer to its nominal level, for both two-sided and one-sided simultaneous confidence sets. Our approach is shown to be considerably less conservative than the current method, and is illustrated with an example on modeling the effect of smoking status and serum triglyceride level on the probability of the recurrence of a myocardial infarction. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Face distinctiveness and delayed testing: differential effects on performance and confidence.

PubMed

Metzger, Mitchell M

2006-04-01

The author investigated the effect of delayed testing on participants' memory for distinctive and typical faces. Participants viewed distinctive and typical faces and were then tested for recognition immediately or after a delay of 3, 6, or 12 weeks. Consistent with prior research, analysis of measure of sensitivity (d') showed that participants performed better on distinctive rather than typical faces, and memory performance declined with longer retention intervals between study and testing. Furthermore, the superior performance on distinctive faces had vanished by the 12-week test. Contrary to d' data, however, an analysis of confidence scores indicated that participants were still significantly more confident on trials depicting distinctive faces, even with a 12-week delay between study and recognition testing.

Experimental congruence of interval scale production from paired comparisons and ranking for image evaluation

NASA Astrophysics Data System (ADS)

Handley, John C.; Babcock, Jason S.; Pelz, Jeff B.

2003-12-01

Image evaluation tasks are often conducted using paired comparisons or ranking. To elicit interval scales, both methods rely on Thurstone's Law of Comparative Judgment in which objects closer in psychological space are more often confused in preference comparisons by a putative discriminal random process. It is often debated whether paired comparisons and ranking yield the same interval scales. An experiment was conducted to assess scale production using paired comparisons and ranking. For this experiment a Pioneer Plasma Display and Apple Cinema Display were used for stimulus presentation. Observers performed rank order and paired comparisons tasks on both displays. For each of five scenes, six images were created by manipulating attributes such as lightness, chroma, and hue using six different settings. The intention was to simulate the variability from a set of digital cameras or scanners. Nineteen subjects, (5 females, 14 males) ranging from 19-51 years of age participated in this experiment. Using a paired comparison model and a ranking model, scales were estimated for each display and image combination yielding ten scale pairs, ostensibly measuring the same psychological scale. The Bradley-Terry model was used for the paired comparisons data and the Bradley-Terry-Mallows model was used for the ranking data. Each model was fit using maximum likelihood estimation and assessed using likelihood ratio tests. Approximate 95% confidence intervals were also constructed using likelihood ratios. Model fits for paired comparisons were satisfactory for all scales except those from two image/display pairs; the ranking model fit uniformly well on all data sets. Arguing from overlapping confidence intervals, we conclude that paired comparisons and ranking produce no conflicting decisions regarding ultimate ordering of treatment preferences, but paired comparisons yield greater precision at the expense of lack-of-fit.

Potential confounding in the association between short birth intervals and increased neonatal, infant, and child mortality

PubMed Central

Perin, Jamie; Walker, Neff

2015-01-01

Background Recent steep declines in child mortality have been attributed in part to increased use of contraceptives and the resulting change in fertility behaviour, including an increase in the time between births. Previous observational studies have documented strong associations between short birth spacing and an increase in the risk of neonatal, infant, and under-five mortality, compared to births with longer preceding birth intervals. In this analysis, we compare two methods to estimate the association between short birth intervals and mortality risk to better inform modelling efforts linking family planning and mortality in children. Objectives Our goal was to estimate the mortality risk for neonates, infants, and young children by preceding birth space using household survey data, controlling for mother-level factors and to compare the results to those from previous analyses with survey data. Design We assessed the potential for confounding when estimating the relative mortality risk by preceding birth interval and estimated mortality risk by birth interval in four categories: less than 18 months, 18–23 months, 24–35 months, and 36 months or longer. We estimated the relative risks among women who were 35 and older at the time of the survey with two methods: in a Cox proportional hazards regression adjusting for potential confounders and also by stratifying Cox regression by mother, to control for all factors that remain constant over a woman's childbearing years. We estimated the overall effects for birth spacing in a meta-analysis with random survey effects. Results We identified several factors known for their associations with neonatal, infant, and child mortality that are also associated with preceding birth interval. When estimating the effect of birth spacing on mortality, we found that regression adjustment for these factors does not substantially change the risk ratio for short birth intervals compared to an unadjusted mortality ratio. For birth

Potential confounding in the association between short birth intervals and increased neonatal, infant, and child mortality.

PubMed

Perin, Jamie; Walker, Neff

2015-01-01

Recent steep declines in child mortality have been attributed in part to increased use of contraceptives and the resulting change in fertility behaviour, including an increase in the time between births. Previous observational studies have documented strong associations between short birth spacing and an increase in the risk of neonatal, infant, and under-five mortality, compared to births with longer preceding birth intervals. In this analysis, we compare two methods to estimate the association between short birth intervals and mortality risk to better inform modelling efforts linking family planning and mortality in children. Our goal was to estimate the mortality risk for neonates, infants, and young children by preceding birth space using household survey data, controlling for mother-level factors and to compare the results to those from previous analyses with survey data. We assessed the potential for confounding when estimating the relative mortality risk by preceding birth interval and estimated mortality risk by birth interval in four categories: less than 18 months, 18-23 months, 24-35 months, and 36 months or longer. We estimated the relative risks among women who were 35 and older at the time of the survey with two methods: in a Cox proportional hazards regression adjusting for potential confounders and also by stratifying Cox regression by mother, to control for all factors that remain constant over a woman's childbearing years. We estimated the overall effects for birth spacing in a meta-analysis with random survey effects. We identified several factors known for their associations with neonatal, infant, and child mortality that are also associated with preceding birth interval. When estimating the effect of birth spacing on mortality, we found that regression adjustment for these factors does not substantially change the risk ratio for short birth intervals compared to an unadjusted mortality ratio. For birth intervals less than 18 months, standard

Confidence-Based Feature Acquisition

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; desJardins, Marie; MacGlashan, James

2010-01-01

Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).

Interpregnancy interval and risk of autistic disorder.

PubMed

Gunnes, Nina; Surén, Pål; Bresnahan, Michaeline; Hornig, Mady; Lie, Kari Kveim; Lipkin, W Ian; Magnus, Per; Nilsen, Roy Miodini; Reichborn-Kjennerud, Ted; Schjølberg, Synnve; Susser, Ezra Saul; Øyen, Anne-Siri; Stoltenberg, Camilla

2013-11-01

A recent California study reported increased risk of autistic disorder in children conceived within a year after the birth of a sibling. We assessed the association between interpregnancy interval and risk of autistic disorder using nationwide registry data on pairs of singleton full siblings born in Norway. We defined interpregnancy interval as the time from birth of the first-born child to conception of the second-born child in a sibship. The outcome of interest was autistic disorder in the second-born child. Analyses were restricted to sibships in which the second-born child was born in 1990-2004. Odds ratios (ORs) were estimated by fitting ordinary logistic models and logistic generalized additive models. The study sample included 223,476 singleton full-sibling pairs. In sibships with interpregnancy intervals <9 months, 0.25% of the second-born children had autistic disorder, compared with 0.13% in the reference category (≥ 36 months). For interpregnancy intervals shorter than 9 months, the adjusted OR of autistic disorder in the second-born child was 2.18 (95% confidence interval 1.42-3.26). The risk of autistic disorder in the second-born child was also increased for interpregnancy intervals of 9-11 months in the adjusted analysis (OR = 1.71 [95% CI = 1.07-2.64]). Consistent with a previous report from California, interpregnancy intervals shorter than 1 year were associated with increased risk of autistic disorder in the second-born child. A possible explanation is depletion of micronutrients in mothers with closely spaced pregnancies.

Increasing Confidence and Ability in Implementing Kangaroo Mother Care Method Among Young Mothers.

PubMed

Kenanga Purbasary, Eleni; Rustina, Yeni; Budiarti, Tri

Mothers giving birth to low birth weight babies (LBWBs) have low confidence in caring for their babies because they are often still young and may lack the knowledge, experience, and ability to care for the baby. This research aims to determine the effect of education about kangaroo mother care (KMC) on the confidence and ability of young mothers to implement KMC. The research methodology used was a controlled-random experimental approach with pre- and post-test equivalent groups of 13 mothers and their LBWBs in the intervention group and 13 mothers and their LBWBs in the control group. Data were collected via an instrument measuring young mothers' confidence, the validity and reliability of which have been tested with a resulting r value of .941, and an observation sheet on KMC implementation. After conducting the education, the confidence score of young mothers and their ability to perform KMC increased meaningfully. The score of confidence of young mothers before education was 37 (p = .1555: and the ability score for KMC Implementation before education was 9 (p = .1555). The median score of confidence of young mothers after education in the intervention group was 87 and in the control group was 50 (p = .001, 95% CI 60.36-75.56), and ability median score for KMC implementation after education in the intervention group was 16 and in the control group was 12 (p = .001, 95% CI 1.50-1.88). KMC education should be conducted gradually, and it is necessary to involve the family, in order for KMC implementation to continue at home. A family visit can be done for LBWBs to evaluate the ability of the young mothers to implement KMC.

Measurement of tag confidence in user generated contents retrieval

NASA Astrophysics Data System (ADS)

Lee, Sihyoung; Min, Hyun-Seok; Lee, Young Bok; Ro, Yong Man

2009-01-01

As online image sharing services are becoming popular, the importance of correctly annotated tags is being emphasized for precise search and retrieval. Tags created by user along with user-generated contents (UGC) are often ambiguous due to the fact that some tags are highly subjective and visually unrelated to the image. They cause unwanted results to users when image search engines rely on tags. In this paper, we propose a method of measuring tag confidence so that one can differentiate confidence tags from noisy tags. The proposed tag confidence is measured from visual semantics of the image. To verify the usefulness of the proposed method, experiments were performed with UGC database from social network sites. Experimental results showed that the image retrieval performance with confidence tags was increased.

Developing Confidence Limits For Reliability Of Software

NASA Technical Reports Server (NTRS)

Hayhurst, Kelly J.

1991-01-01

Technique developed for estimating reliability of software by use of Moranda geometric de-eutrophication model. Pivotal method enables straightforward construction of exact bounds with associated degree of statistical confidence about reliability of software. Confidence limits thus derived provide precise means of assessing quality of software. Limits take into account number of bugs found while testing and effects of sampling variation associated with random order of discovering bugs.

Modified Dempster-Shafer approach using an expected utility interval decision rule

NASA Astrophysics Data System (ADS)

Cheaito, Ali; Lecours, Michael; Bosse, Eloi

1999-03-01

The combination operation of the conventional Dempster- Shafer algorithm has a tendency to increase exponentially the number of propositions involved in bodies of evidence by creating new ones. The aim of this paper is to explore a 'modified Dempster-Shafer' approach of fusing identity declarations emanating form different sources which include a number of radars, IFF and ESM systems in order to limit the explosion of the number of propositions. We use a non-ad hoc decision rule based on the expected utility interval to select the most probable object in a comprehensive Platform Data Base containing all the possible identity values that a potential target may take. We study the effect of the redistribution of the confidence levels of the eliminated propositions which otherwise overload the real-time data fusion system; these eliminated confidence levels can in particular be assigned to ignorance, or uniformly added to the remaining propositions and to ignorance. A scenario has been selected to demonstrate the performance of our modified Dempster-Shafer method of evidential reasoning.

A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies.

PubMed

Kottas, Martina; Kuss, Oliver; Zapf, Antonia

2014-02-19

The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the Mann-Whitney interval without transformation and also the interval of the binormal AUC are very liberal. For small sample sizes the Wald interval with continuity has a comparable coverage probability as the LT interval and higher power. For large sample sizes the results of the LT interval and of the Wald interval without continuity correction are comparable. If individual patient data is not available, but only the estimated AUC and the total sample size, the modified Wald intervals can be recommended as confidence intervals for the AUC. For small sample sizes the continuity correction should be used.

Generalized Bootstrap Method for Assessment of Uncertainty in Semivariogram Inference

USGS Publications Warehouse

Olea, R.A.; Pardo-Iguzquiza, E.

2011-01-01

The semivariogram and its related function, the covariance, play a central role in classical geostatistics for modeling the average continuity of spatially correlated attributes. Whereas all methods are formulated in terms of the true semivariogram, in practice what can be used are estimated semivariograms and models based on samples. A generalized form of the bootstrap method to properly model spatially correlated data is used to advance knowledge about the reliability of empirical semivariograms and semivariogram models based on a single sample. Among several methods available to generate spatially correlated resamples, we selected a method based on the LU decomposition and used several examples to illustrate the approach. The first one is a synthetic, isotropic, exhaustive sample following a normal distribution, the second example is also a synthetic but following a non-Gaussian random field, and a third empirical sample consists of actual raingauge measurements. Results show wider confidence intervals than those found previously by others with inadequate application of the bootstrap. Also, even for the Gaussian example, distributions for estimated semivariogram values and model parameters are positively skewed. In this sense, bootstrap percentile confidence intervals, which are not centered around the empirical semivariogram and do not require distributional assumptions for its construction, provide an achieved coverage similar to the nominal coverage. The latter cannot be achieved by symmetrical confidence intervals based on the standard error, regardless if the standard error is estimated from a parametric equation or from bootstrap. ?? 2010 International Association for Mathematical Geosciences.

Four Methods for Analyzing Partial Interval Recording Data, with Application to Single-Case Research.

PubMed

Pustejovsky, James E; Swan, Daniel M

2015-01-01

Partial interval recording (PIR) is a procedure for collecting measurements during direct observation of behavior. It is used in several areas of educational and psychological research, particularly in connection with single-case research. Measurements collected using partial interval recording suffer from construct invalidity because they are not readily interpretable in terms of the underlying characteristics of the behavior. Using an alternating renewal process model for the behavior under observation, we demonstrate that ignoring the construct invalidity of PIR data can produce misleading inferences, such as inferring that an intervention reduces the prevalence of an undesirable behavior when in fact it has the opposite effect. We then propose four different methods for analyzing PIR summary measurements, each of which can be used to draw inferences about interpretable behavioral parameters. We demonstrate the methods by applying them to data from two single-case studies of problem behavior.

Importance of the Time Interval between Bowel Preparation and Colonoscopy in Determining the Quality of Bowel Preparation for Full-Dose Polyethylene Glycol Preparation

PubMed Central

Kim, Tae Kyung; Kim, Hyung Wook; Kim, Su Jin; Ha, Jong Kun; Jang, Hyung Ha; Hong, Young Mi; Park, Su Bum; Choi, Cheol Woong; Kang, Dae Hwan

2014-01-01

Background/Aims The quality of bowel preparation (QBP) is the important factor in performing a successful colonoscopy. Several factors influencing QBP have been reported; however, some factors, such as the optimal preparation-to-colonoscopy time interval, remain controversial. This study aimed to determine the factors influencing QBP and the optimal time interval for full-dose polyethylene glycol (PEG) preparation. Methods A total of 165 patients who underwent colonoscopy from June 2012 to August 2012 were prospectively evaluated. The QBP was assessed using the Ottawa Bowel Preparation Scale (Ottawa) score according to several factors influencing the QBP were analyzed. Results Colonoscopies with a time interval of 5 to 6 hours had the best Ottawa score in all parts of the colon. Patients with time intervals of 6 hours or less had the better QBP than those with time intervals of more than 6 hours (p=0.046). In the multivariate analysis, the time interval (odds ratio, 1.897; 95% confidence interval, 1.006 to 3.577; p=0.048) was the only significant contributor to a satisfactory bowel preparation. Conclusions The optimal time was 5 to 6 hours for the full-dose PEG method, and the time interval was the only significant contributor to a satisfactory bowel preparation. PMID:25368750

A method for meta-analysis of epidemiological studies.

PubMed

Einarson, T R; Leeder, J S; Koren, G

1988-10-01

This article presents a stepwise approach for conducting a meta-analysis of epidemiological studies based on proposed guidelines. This systematic method is recommended for practitioners evaluating epidemiological studies in the literature to arrive at an overall quantitative estimate of the impact of a treatment. Bendectin is used as an illustrative example. Meta-analysts should establish a priori the purpose of the analysis and a complete protocol. This protocol should be adhered to, and all steps performed should be recorded in detail. To aid in developing such a protocol, we present methods the researcher can use to perform each of 22 steps in six major areas. The illustrative meta-analysis confirmed previous traditional narrative literature reviews that Bendectin is not related to teratogenic outcomes in humans. The overall summary odds ratio was 1.01 (chi 2 = 0.05, p = 0.815) with a 95 percent confidence interval of 0.66-1.55. When the studies were separated according to study type, the summary odds ratio for cohort studies was 0.95 with a 95 percent confidence interval of 0.62-1.45. For case-control studies, the summary odds ratio was 1.27 with a 95 percent confidence interval of 0.83-1.94. The corresponding chi-square values were not statistically significant at the p = 0.05 level.

Parents' obesity-related behavior and confidence to support behavioral change in their obese child: data from the STAR study.

PubMed

Arsenault, Lisa N; Xu, Kathleen; Taveras, Elsie M; Hacker, Karen A

2014-01-01

Successful childhood obesity interventions frequently focus on behavioral modification and involve parents or family members. Parental confidence in supporting behavior change may be an element of successful family-based prevention efforts. We aimed to determine whether parents' own obesity-related behaviors were related to their confidence in supporting their child's achievement of obesity-related behavioral goals. Cross-sectional analyses of data collected at baseline of a randomized control trial testing a treatment intervention for obese children (n = 787) in primary care settings (n = 14). Five obesity-related behaviors (physical activity, screen time, sugar-sweetened beverage, sleep duration, fast food) were self-reported by parents for themselves and their child. Behaviors were dichotomized on the basis of achievement of behavioral goals. Five confidence questions asked how confident the parent was in helping their child achieve each goal. Logistic regression modeling high confidence was conducted with goal achievement and demographics as independent variables. Parents achieving physical activity or sleep duration goals were significantly more likely to be highly confident in supporting their child's achievement of those goals (physical activity, odds ratio 1.76; 95% confidence interval 1.19-2.60; sleep, odds ratio 1.74; 95% confidence interval 1.09-2.79) independent of sociodemographic variables and child's current behavior. Parental achievements of TV watching and fast food goals were also associated with confidence, but significance was attenuated after child's behavior was included in models. Parents' own obesity-related behaviors are factors that may affect their confidence to support their child's behavior change. Providers seeking to prevent childhood obesity should address parent/family behaviors as part of their obesity prevention strategies. Copyright © 2014 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.

Statistics of return intervals between long heartbeat intervals and their usability for online prediction of disorders

NASA Astrophysics Data System (ADS)

Bogachev, Mikhail I.; Kireenkov, Igor S.; Nifontov, Eugene M.; Bunde, Armin

2009-06-01

We study the statistics of return intervals between large heartbeat intervals (above a certain threshold Q) in 24 h records obtained from healthy subjects. We find that both the linear and the nonlinear long-term memory inherent in the heartbeat intervals lead to power-laws in the probability density function PQ(r) of the return intervals. As a consequence, the probability WQ(t; Δt) that at least one large heartbeat interval will occur within the next Δt heartbeat intervals, with an increasing elapsed number of intervals t after the last large heartbeat interval, follows a power-law. Based on these results, we suggest a method of obtaining a priori information about the occurrence of the next large heartbeat interval, and thus to predict it. We show explicitly that the proposed method, which exploits long-term memory, is superior to the conventional precursory pattern recognition technique, which focuses solely on short-term memory. We believe that our results can be straightforwardly extended to obtain more reliable predictions in other physiological signals like blood pressure, as well as in other complex records exhibiting multifractal behaviour, e.g. turbulent flow, precipitation, river flows and network traffic.

Optimal Measurement Interval for Emergency Department Crowding Estimation Tools.

PubMed

Wang, Hao; Ojha, Rohit P; Robinson, Richard D; Jackson, Bradford E; Shaikh, Sajid A; Cowden, Chad D; Shyamanand, Rath; Leuck, JoAnna; Schrader, Chet D; Zenarosa, Nestor R

2017-11-01

Emergency department (ED) crowding is a barrier to timely care. Several crowding estimation tools have been developed to facilitate early identification of and intervention for crowding. Nevertheless, the ideal frequency is unclear for measuring ED crowding by using these tools. Short intervals may be resource intensive, whereas long ones may not be suitable for early identification. Therefore, we aim to assess whether outcomes vary by measurement interval for 4 crowding estimation tools. Our eligible population included all patients between July 1, 2015, and June 30, 2016, who were admitted to the JPS Health Network ED, which serves an urban population. We generated 1-, 2-, 3-, and 4-hour ED crowding scores for each patient, using 4 crowding estimation tools (National Emergency Department Overcrowding Scale [NEDOCS], Severely Overcrowded, Overcrowded, and Not Overcrowded Estimation Tool [SONET], Emergency Department Work Index [EDWIN], and ED Occupancy Rate). Our outcomes of interest included ED length of stay (minutes) and left without being seen or eloped within 4 hours. We used accelerated failure time models to estimate interval-specific time ratios and corresponding 95% confidence limits for length of stay, in which the 1-hour interval was the reference. In addition, we used binomial regression with a log link to estimate risk ratios (RRs) and corresponding confidence limit for left without being seen. Our study population comprised 117,442 patients. The time ratios for length of stay were similar across intervals for each crowding estimation tool (time ratio=1.37 to 1.30 for NEDOCS, 1.44 to 1.37 for SONET, 1.32 to 1.27 for EDWIN, and 1.28 to 1.23 for ED Occupancy Rate). The RRs of left without being seen differences were also similar across intervals for each tool (RR=2.92 to 2.56 for NEDOCS, 3.61 to 3.36 for SONET, 2.65 to 2.40 for EDWIN, and 2.44 to 2.14 for ED Occupancy Rate). Our findings suggest limited variation in length of stay or left without being

Linkage disequilibrium interval mapping of quantitative trait loci.

PubMed

Boitard, Simon; Abdallah, Jihad; de Rochambeau, Hubert; Cierco-Ayrolles, Christine; Mangin, Brigitte

2006-03-16

For many years gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of gene location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods. Under a three-locus Wright-Fisher model, we derived approximate expressions for the expected haplotype frequencies in a population. We considered haplotypes comprising one trait locus and two flanking markers. Using these theoretical expressions, we built a likelihood-maximization method, called HAPim, for estimating the location of a quantitative trait locus. For each postulated position, the method only requires information from the two flanking markers. Over a wide range of simulation scenarios it was found to be more accurate than a two-marker composite likelihood method. It also performed as well as identity by descent methods, whilst being valuable in a wider range of populations. Our method makes efficient use of marker information, and can be valuable for fine mapping purposes. Its performance is increased if multiallelic markers are available. Several improvements can be developed to account for more complex evolution scenarios or provide robust confidence intervals for the location estimates.

Analyzing big data with the hybrid interval regression methods.

PubMed

Huang, Chia-Hui; Yang, Keng-Chieh; Kao, Han-Ying

2014-01-01

Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.

Analyzing Big Data with the Hybrid Interval Regression Methods

PubMed Central

Kao, Han-Ying

2014-01-01

Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes. PMID:25143968

Identifying the bad guy in a lineup using confidence judgments under deadline pressure.

PubMed

Brewer, Neil; Weber, Nathan; Wootton, David; Lindsay, D Stephen

2012-10-01

Eyewitness-identification tests often culminate in witnesses not picking the culprit or identifying innocent suspects. We tested a radical alternative to the traditional lineup procedure used in such tests. Rather than making a positive identification, witnesses made confidence judgments under a short deadline about whether each lineup member was the culprit. We compared this deadline procedure with the traditional sequential-lineup procedure in three experiments with retention intervals ranging from 5 min to 1 week. A classification algorithm that identified confidence criteria that optimally discriminated accurate from inaccurate decisions revealed that decision accuracy was 24% to 66% higher under the deadline procedure than under the traditional procedure. Confidence profiles across lineup stimuli were more informative than were identification decisions about the likelihood that an individual witness recognized the culprit or correctly recognized that the culprit was not present. Large differences between the maximum and the next-highest confidence value signaled very high accuracy. Future support for this procedure across varied conditions would highlight a viable alternative to the problematic lineup procedures that have traditionally been used by law enforcement.

Hypothesis Testing, "p" Values, Confidence Intervals, Measures of Effect Size, and Bayesian Methods in Light of Modern Robust Techniques

ERIC Educational Resources Information Center

Wilcox, Rand R.; Serang, Sarfaraz

2017-01-01

The article provides perspectives on p values, null hypothesis testing, and alternative techniques in light of modern robust statistical methods. Null hypothesis testing and "p" values can provide useful information provided they are interpreted in a sound manner, which includes taking into account insights and advances that have…

A novel implementation of homodyne time interval analysis method for primary vibration calibration

NASA Astrophysics Data System (ADS)

Sun, Qiao; Zhou, Ling; Cai, Chenguang; Hu, Hongbo

2011-12-01

In this paper, the shortcomings and their causes of the conventional homodyne time interval analysis (TIA) method is described with respect to its software algorithm and hardware implementation, based on which a simplified TIA method is proposed with the help of virtual instrument technology. Equipped with an ordinary Michelson interferometer and dual channel synchronous data acquisition card, the primary vibration calibration system using the simplified method can perform measurements of complex sensitivity of accelerometers accurately, meeting the uncertainty requirements laid down in pertaining ISO standard. The validity and accuracy of the simplified TIA method is verified by simulation and comparison experiments with its performance analyzed. This simplified method is recommended to apply in national metrology institute of developing countries and industrial primary vibration calibration labs for its simplified algorithm and low requirements on hardware.

Multi-reader ROC studies with split-plot designs: a comparison of statistical methods.

PubMed

Obuchowski, Nancy A; Gallas, Brandon D; Hillis, Stephen L

2012-12-01

Multireader imaging trials often use a factorial design, in which study patients undergo testing with all imaging modalities and readers interpret the results of all tests for all patients. A drawback of this design is the large number of interpretations required of each reader. Split-plot designs have been proposed as an alternative, in which one or a subset of readers interprets all images of a sample of patients, while other readers interpret the images of other samples of patients. In this paper, the authors compare three methods of analysis for the split-plot design. Three statistical methods are presented: the Obuchowski-Rockette method modified for the split-plot design, a newly proposed marginal-mean analysis-of-variance approach, and an extension of the three-sample U-statistic method. A simulation study using the Roe-Metz model was performed to compare the type I error rate, power, and confidence interval coverage of the three test statistics. The type I error rates for all three methods are close to the nominal level but tend to be slightly conservative. The statistical power is nearly identical for the three methods. The coverage of 95% confidence intervals falls close to the nominal coverage for small and large sample sizes. The split-plot multireader, multicase study design can be statistically efficient compared to the factorial design, reducing the number of interpretations required per reader. Three methods of analysis, shown to have nominal type I error rates, similar power, and nominal confidence interval coverage, are available for this study design. Copyright © 2012 AUR. All rights reserved.

Effect of insertion method and postinsertion time interval prior to force application on the removal torque of orthodontic miniscrews.

PubMed

Sharifi, Maryam; Ghassemi, Amirreza; Bayani, Shahin

2015-01-01

Success of orthodontic miniscrews in providing stable anchorage is dependent on their stability. The purpose of this study was to assess the effect of insertion method and postinsertion time interval on the removal torque of miniscrews as an indicator of their stability. Seventy-two miniscrews (Jeil Medical) were inserted into the femoral bones of three male German Shepherd dogs and assigned to nine groups of eight miniscrews. Three insertion methods, including hand-driven, motor-driven with 5.0-Ncm insertion torque, and motor-driven with 20.0-Ncm insertion torque, were tested. Three time intervals of 0, 2, and 6 weeks between miniscrew insertion and removal were tested as well. Removal torque values were measured in newton centimeters by a removal torque tester (IMADA). Data were analyzed by one-way analysis of variance (ANOVA) followed by the Bonferroni post hoc test at a .05 level of significance. A miniscrew survival rate of 93% was observed in this study. The highest mean value of removal torque among the three postinsertion intervals (2.4 ± 0.59 Ncm) was obtained immediately after miniscrew insertion with a statistically significant difference from the other two time intervals (P < .001). Insertion were observed in this regard (P = .46). The stability of miniscrews was not affected by the insertion method. However, of the postinsertion time intervals, the highest removal torque values were obtained immediately after insertion.

Validation of the Vaccination Confidence Scale: A brief measure to identify parents at risk for refusing adolescent vaccines

PubMed Central

Reiter, Paul L.; Magnus, Brooke E.; McRee, Annie-Laurie; Dempsey, Amanda F.; Brewer, Noel T.

2015-01-01

Objective To support efforts to address vaccine hesitancy, we sought to validate a brief measure of vaccination confidence using a large, nationally representative sample of parents. Methods We analyzed weighted data from 9,018 parents who completed the 2010 National Immunization Survey-Teen, an annual, population-based telephone survey. Parents reported on the immunization history of a 13- to 17-year-old child in their households for vaccines including tetanus, diphtheria, and acellular pertussis (Tdap), meningococcal, and human papillomavirus (HPV) vaccines. For each vaccine, separate logistic regression models assessed associations between parents’ mean scores on the 8-item Vaccination Confidence Scale and vaccine refusal, vaccine delay, and vaccination status. We repeated analyses for the scale’s 4-item short form. Results One quarter of parents (24%) reported refusal of any vaccine, with refusal of specific vaccines ranging from 21% for HPV to 2% for Tdap. Using the full 8-item scale, vaccination confidence was negatively associated with measures of vaccine refusal and positively associated with measures of vaccination status. For example, refusal of any vaccine was more common among parents whose scale scores were medium (odds ratio [OR] = 2.08, 95% confidence interval [CI], 1.75–2.47) or low (OR = 4.61, 95% CI, 3.51–6.05) versus high. For the 4-item short form, scores were also consistently associated with vaccine refusal and vaccination status. Vaccination confidence was inconsistently associated with vaccine delay. Conclusions The Vaccination Confidence Scale shows promise as a tool for identifying parents at risk for refusing adolescent vaccines. The scale’s short form appears to offer comparable performance. PMID:26300368

Normal probability plots with confidence.

PubMed

Chantarangsi, Wanpen; Liu, Wei; Bretz, Frank; Kiatsupaibul, Seksan; Hayter, Anthony J; Wan, Fang

2015-01-01

Normal probability plots are widely used as a statistical tool for assessing whether an observed simple random sample is drawn from a normally distributed population. The users, however, have to judge subjectively, if no objective rule is provided, whether the plotted points fall close to a straight line. In this paper, we focus on how a normal probability plot can be augmented by intervals for all the points so that, if the population distribution is normal, then all the points should fall into the corresponding intervals simultaneously with probability 1-α. These simultaneous 1-α probability intervals provide therefore an objective mean to judge whether the plotted points fall close to the straight line: the plotted points fall close to the straight line if and only if all the points fall into the corresponding intervals. The powers of several normal probability plot based (graphical) tests and the most popular nongraphical Anderson-Darling and Shapiro-Wilk tests are compared by simulation. Based on this comparison, recommendations are given in Section 3 on which graphical tests should be used in what circumstances. An example is provided to illustrate the methods. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Re-evaluation of link between interpregnancy interval and adverse birth outcomes: retrospective cohort study matching two intervals per mother

PubMed Central

Pereira, Gavin; Jacoby, Peter; de Klerk, Nicholas; Stanley, Fiona J

2014-01-01

Objective To re-evaluate the causal effect of interpregnancy interval on adverse birth outcomes, on the basis that previous studies relying on between mother comparisons may have inadequately adjusted for confounding by maternal risk factors. Design Retrospective cohort study using conditional logistic regression (matching two intervals per mother so each mother acts as her own control) to model the incidence of adverse birth outcomes as a function of interpregnancy interval; additional unconditional logistic regression with adjustment for confounders enabled comparison with the unmatched design of previous studies. Setting Perth, Western Australia, 1980-2010. Participants 40 441 mothers who each delivered three liveborn singleton neonates. Main outcome measures Preterm birth (<37 weeks), small for gestational age birth (<10th centile of birth weight by sex and gestational age), and low birth weight (<2500 g). Results Within mother analysis of interpregnancy intervals indicated a much weaker effect of short intervals on the odds of preterm birth and low birth weight compared with estimates generated using a traditional between mother analysis. The traditional unmatched design estimated an adjusted odds ratio for an interpregnancy interval of 0-5 months (relative to the reference category of 18-23 months) of 1.41 (95% confidence interval 1.31 to 1.51) for preterm birth, 1.26 (1.15 to 1.37) for low birth weight, and 0.98 (0.92 to 1.06) for small for gestational age birth. In comparison, the matched design showed a much weaker effect of short interpregnancy interval on preterm birth (odds ratio 1.07, 0.86 to 1.34) and low birth weight (1.03, 0.79 to 1.34), and the effect for small for gestational age birth remained small (1.08, 0.87 to 1.34). Both the unmatched and matched models estimated a high odds of small for gestational age birth and low birth weight for long interpregnancy intervals (longer than 59 months), but the estimated effect of long interpregnancy

ESTABLISHMENT OF A FIBRINOGEN REFERENCE INTERVAL IN ORNATE BOX TURTLES (TERRAPENE ORNATA ORNATA).

PubMed

Parkinson, Lily; Olea-Popelka, Francisco; Klaphake, Eric; Dadone, Liza; Johnston, Matthew

2016-09-01

This study sought to establish a reference interval for fibrinogen in healthy ornate box turtles ( Terrapene ornata ornata). A total of 48 turtles were enrolled, with 42 turtles deemed to be noninflammatory and thus fitting the inclusion criteria and utilized to estimate a fibrinogen reference interval. Turtles were excluded based upon physical examination and blood work abnormalities. A Shapiro-Wilk normality test indicated that the noninflammatory turtle fibrinogen values were normally distributed (Gaussian distribution) with an average of 108 mg/dl and a 95% confidence interval of the mean of 97.9-117 mg/dl. Those turtles excluded from the reference interval because of abnormalities affecting their health had significantly different fibrinogen values (P = 0.313). A reference interval for healthy ornate box turtles was calculated. Further investigation into the utility of fibrinogen measurement for clinical usage in ornate box turtles is warranted.

Exact intervals and tests for median when one sample value possibly an outliner

NASA Technical Reports Server (NTRS)

Keller, G. J.; Walsh, J. E.

1973-01-01

Available are independent observations (continuous data) that are believed to be a random sample. Desired are distribution-free confidence intervals and significance tests for the population median. However, there is the possibility that either the smallest or the largest observation is an outlier. Then, use of a procedure for rejection of an outlying observation might seem appropriate. Such a procedure would consider that two alternative situations are possible and would select one of them. Either (1) the n observations are truly a random sample, or (2) an outlier exists and its removal leaves a random sample of size n-1. For either situation, confidence intervals and tests are desired for the median of the population yielding the random sample. Unfortunately, satisfactory rejection procedures of a distribution-free nature do not seem to be available. Moreover, all rejection procedures impose undesirable conditional effects on the observations, and also, can select the wrong one of the two above situations. It is found that two-sided intervals and tests based on two symmetrically located order statistics (not the largest and smallest) of the n observations have this property.

Effect Sizes and their Intervals: The Two-Level Repeated Measures Case

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2005-01-01

Probability coverage for eight different confidence intervals (CIs) of measures of effect size (ES) in a two-level repeated measures design was investigated. The CIs and measures of ES differed with regard to whether they used least squares or robust estimates of central tendency and variability, whether the end critical points of the interval…

Magnetic Resonance Fingerprinting with short relaxation intervals.

PubMed

Amthor, Thomas; Doneva, Mariya; Koken, Peter; Sommer, Karsten; Meineke, Jakob; Börnert, Peter

2017-09-01

The aim of this study was to investigate a technique for improving the performance of Magnetic Resonance Fingerprinting (MRF) in repetitive sampling schemes, in particular for 3D MRF acquisition, by shortening relaxation intervals between MRF pulse train repetitions. A calculation method for MRF dictionaries adapted to short relaxation intervals and non-relaxed initial spin states is presented, based on the concept of stationary fingerprints. The method is applicable to many different k-space sampling schemes in 2D and 3D. For accuracy analysis, T 1 and T 2 values of a phantom are determined by single-slice Cartesian MRF for different relaxation intervals and are compared with quantitative reference measurements. The relevance of slice profile effects is also investigated in this case. To further illustrate the capabilities of the method, an application to in-vivo spiral 3D MRF measurements is demonstrated. The proposed computation method enables accurate parameter estimation even for the shortest relaxation intervals, as investigated for different sampling patterns in 2D and 3D. In 2D Cartesian measurements, we achieved a scan acceleration of more than a factor of two, while maintaining acceptable accuracy: The largest T 1 values of a sample set deviated from their reference values by 0.3% (longest relaxation interval) and 2.4% (shortest relaxation interval). The largest T 2 values showed systematic deviations of up to 10% for all relaxation intervals, which is discussed. The influence of slice profile effects for multislice acquisition is shown to become increasingly relevant for short relaxation intervals. In 3D spiral measurements, a scan time reduction of 36% was achieved, maintaining the quality of in-vivo T1 and T2 maps. Reducing the relaxation interval between MRF sequence repetitions using stationary fingerprint dictionaries is a feasible method to improve the scan efficiency of MRF sequences. The method enables fast implementations of 3D spatially

We will be champions: Leaders' confidence in 'us' inspires team members' team confidence and performance.

PubMed

Fransen, K; Steffens, N K; Haslam, S A; Vanbeselaere, N; Vande Broek, G; Boen, F

2016-12-01

The present research examines the impact of leaders' confidence in their team on the team confidence and performance of their teammates. In an experiment involving newly assembled soccer teams, we manipulated the team confidence expressed by the team leader (high vs neutral vs low) and assessed team members' responses and performance as they unfolded during a competition (i.e., in a first baseline session and a second test session). Our findings pointed to team confidence contagion such that when the leader had expressed high (rather than neutral or low) team confidence, team members perceived their team to be more efficacious and were more confident in the team's ability to win. Moreover, leaders' team confidence affected individual and team performance such that teams led by a highly confident leader performed better than those led by a less confident leader. Finally, the results supported a hypothesized mediational model in showing that the effect of leaders' confidence on team members' team confidence and performance was mediated by the leader's perceived identity leadership and members' team identification. In conclusion, the findings of this experiment suggest that leaders' team confidence can enhance members' team confidence and performance by fostering members' identification with the team. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Raising Confident Kids

MedlinePlus

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Increasing accuracy in the interval analysis by the improved format of interval extension based on the first order Taylor series

NASA Astrophysics Data System (ADS)

Li, Yi; Xu, Yan Long

2018-05-01

When the dependence of the function on uncertain variables is non-monotonic in interval, the interval of function obtained by the classic interval extension based on the first order Taylor series will exhibit significant errors. In order to reduce theses errors, the improved format of the interval extension with the first order Taylor series is developed here considering the monotonicity of function. Two typical mathematic examples are given to illustrate this methodology. The vibration of a beam with lumped masses is studied to demonstrate the usefulness of this method in the practical application, and the necessary input data of which are only the function value at the central point of interval, sensitivity and deviation of function. The results of above examples show that the interval of function from the method developed by this paper is more accurate than the ones obtained by the classic method.

An appraisal of statistical procedures used in derivation of reference intervals.

PubMed

Ichihara, Kiyoshi; Boyd, James C

2010-11-01

When conducting studies to derive reference intervals (RIs), various statistical procedures are commonly applied at each step, from the planning stages to final computation of RIs. Determination of the necessary sample size is an important consideration, and evaluation of at least 400 individuals in each subgroup has been recommended to establish reliable common RIs in multicenter studies. Multiple regression analysis allows identification of the most important factors contributing to variation in test results, while accounting for possible confounding relationships among these factors. Of the various approaches proposed for judging the necessity of partitioning reference values, nested analysis of variance (ANOVA) is the likely method of choice owing to its ability to handle multiple groups and being able to adjust for multiple factors. Box-Cox power transformation often has been used to transform data to a Gaussian distribution for parametric computation of RIs. However, this transformation occasionally fails. Therefore, the non-parametric method based on determination of the 2.5 and 97.5 percentiles following sorting of the data, has been recommended for general use. The performance of the Box-Cox transformation can be improved by introducing an additional parameter representing the origin of transformation. In simulations, the confidence intervals (CIs) of reference limits (RLs) calculated by the parametric method were narrower than those calculated by the non-parametric approach. However, the margin of difference was rather small owing to additional variability in parametrically-determined RLs introduced by estimation of parameters for the Box-Cox transformation. The parametric calculation method may have an advantage over the non-parametric method in allowing identification and exclusion of extreme values during RI computation.

Methods for the behavioral, educational, and social sciences: an R package.

PubMed

Kelley, Ken

2007-11-01

Methods for the Behavioral, Educational, and Social Sciences (MBESS; Kelley, 2007b) is an open source package for R (R Development Core Team, 2007b), an open source statistical programming language and environment. MBESS implements methods that are not widely available elsewhere, yet are especially helpful for the idiosyncratic techniques used within the behavioral, educational, and social sciences. The major categories of functions are those that relate to confidence interval formation for noncentral t, F, and chi2 parameters, confidence intervals for standardized effect sizes (which require noncentral distributions), and sample size planning issues from the power analytic and accuracy in parameter estimation perspectives. In addition, MBESS contains collections of other functions that should be helpful to substantive researchers and methodologists. MBESS is a long-term project that will continue to be updated and expanded so that important methods can continue to be made available to researchers in the behavioral, educational, and social sciences.

Maternal Confidence for Physiologic Childbirth: A Concept Analysis.

PubMed

Neerland, Carrie E

2018-06-06

Confidence is a term often used in research literature and consumer media in relation to birth, but maternal confidence has not been clearly defined, especially as it relates to physiologic labor and birth. The aim of this concept analysis was to define maternal confidence in the context of physiologic labor and childbirth. Rodgers' evolutionary method was used to identify attributes, antecedents, and consequences of maternal confidence for physiologic birth. Databases searched included Ovid MEDLINE, CINAHL, PsycINFO, and Sociological Abstracts from the years 1995 to 2015. A total of 505 articles were retrieved, using the search terms pregnancy, obstetric care, prenatal care, and self-efficacy and the keyword confidence. Articles were identified for in-depth review and inclusion based on whether the term confidence was used or assessed in relationship to labor and/or birth. In addition, a hand search of the reference lists of the selected articles was performed. Twenty-four articles were reviewed in this concept analysis. We define maternal confidence for physiologic birth as a woman's belief that physiologic birth can be achieved, based on her view of birth as a normal process and her belief in her body's innate ability to birth, which is supported by social support, knowledge, and information founded on a trusted relationship with a maternity care provider in an environment where the woman feels safe. This concept analysis advances the concept of maternal confidence for physiologic birth and provides new insight into how women's confidence for physiologic birth might be enhanced during the prenatal period. Further investigation of confidence for physiologic birth across different cultures is needed to identify cultural differences in constructions of the concept. © 2018 by the American College of Nurse-Midwives.

Automatic Error Analysis Using Intervals

ERIC Educational Resources Information Center

Rothwell, E. J.; Cloud, M. J.

2012-01-01

A technique for automatic error analysis using interval mathematics is introduced. A comparison to standard error propagation methods shows that in cases involving complicated formulas, the interval approach gives comparable error estimates with much less effort. Several examples are considered, and numerical errors are computed using the INTLAB…

Statistical procedures for determination and verification of minimum reporting levels for drinking water methods.

PubMed

Winslow, Stephen D; Pepich, Barry V; Martin, John J; Hallberg, George R; Munch, David J; Frebis, Christopher P; Hedrick, Elizabeth J; Krop, Richard A

2006-01-01

The United States Environmental Protection Agency's Office of Ground Water and Drinking Water has developed a single-laboratory quantitation procedure: the lowest concentration minimum reporting level (LCMRL). The LCMRL is the lowest true concentration for which future recovery is predicted to fall, with high confidence (99%), between 50% and 150%. The procedure takes into account precision and accuracy. Multiple concentration replicates are processed through the entire analytical method and the data are plotted as measured sample concentration (y-axis) versus true concentration (x-axis). If the data support an assumption of constant variance over the concentration range, an ordinary least-squares regression line is drawn; otherwise, a variance-weighted least-squares regression is used. Prediction interval lines of 99% confidence are drawn about the regression. At the points where the prediction interval lines intersect with data quality objective lines of 50% and 150% recovery, lines are dropped to the x-axis. The higher of the two values is the LCMRL. The LCMRL procedure is flexible because the data quality objectives (50-150%) and the prediction interval confidence (99%) can be varied to suit program needs. The LCMRL determination is performed during method development only. A simpler procedure for verification of data quality objectives at a given minimum reporting level (MRL) is also presented. The verification procedure requires a single set of seven samples taken through the entire method procedure. If the calculated prediction interval is contained within data quality recovery limits (50-150%), the laboratory performance at the MRL is verified.

Point and interval estimation of pollinator importance: a study using pollination data of Silene caroliniana.

PubMed

Reynolds, Richard J; Fenster, Charles B

2008-05-01

Pollinator importance, the product of visitation rate and pollinator effectiveness, is a descriptive parameter of the ecology and evolution of plant-pollinator interactions. Naturally, sources of its variation should be investigated, but the SE of pollinator importance has never been properly reported. Here, a Monte Carlo simulation study and a result from mathematical statistics on the variance of the product of two random variables are used to estimate the mean and confidence limits of pollinator importance for three visitor species of the wildflower, Silene caroliniana. Both methods provided similar estimates of mean pollinator importance and its interval if the sample size of the visitation and effectiveness datasets were comparatively large. These approaches allowed us to determine that bumblebee importance was significantly greater than clearwing hawkmoth, which was significantly greater than beefly. The methods could be used to statistically quantify temporal and spatial variation in pollinator importance of particular visitor species. The approaches may be extended for estimating the variance of more than two random variables. However, unless the distribution function of the resulting statistic is known, the simulation approach is preferable for calculating the parameter's confidence limits.

A Direct Latent Variable Modeling Based Method for Point and Interval Estimation of Coefficient Alpha

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2015-01-01

A direct approach to point and interval estimation of Cronbach's coefficient alpha for multiple component measuring instruments is outlined. The procedure is based on a latent variable modeling application with widely circulated software. As a by-product, using sample data the method permits ascertaining whether the population discrepancy…

Intraclass Correlation Coefficients in Hierarchical Design Studies with Discrete Response Variables: A Note on a Direct Interval Estimation Procedure

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2015-01-01

A latent variable modeling procedure that can be used to evaluate intraclass correlation coefficients in two-level settings with discrete response variables is discussed. The approach is readily applied when the purpose is to furnish confidence intervals at prespecified confidence levels for these coefficients in setups with binary or ordinal…

Confidence Preserving Machine for Facial Action Unit Detection

PubMed Central

Zeng, Jiabei; Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F.; Xiong, Zhang

2016-01-01

Facial action unit (AU) detection from video has been a long-standing problem in automated facial expression analysis. While progress has been made, accurate detection of facial AUs remains challenging due to ubiquitous sources of errors, such as inter-personal variability, pose, and low-intensity AUs. In this paper, we refer to samples causing such errors as hard samples, and the remaining as easy samples. To address learning with the hard samples, we propose the Confidence Preserving Machine (CPM), a novel two-stage learning framework that combines multiple classifiers following an “easy-to-hard” strategy. During the training stage, CPM learns two confident classifiers. Each classifier focuses on separating easy samples of one class from all else, and thus preserves confidence on predicting each class. During the testing stage, the confident classifiers provide “virtual labels” for easy test samples. Given the virtual labels, we propose a quasi-semi-supervised (QSS) learning strategy to learn a person-specific (PS) classifier. The QSS strategy employs a spatio-temporal smoothness that encourages similar predictions for samples within a spatio-temporal neighborhood. In addition, to further improve detection performance, we introduce two CPM extensions: iCPM that iteratively augments training samples to train the confident classifiers, and kCPM that kernelizes the original CPM model to promote nonlinearity. Experiments on four spontaneous datasets GFT [15], BP4D [56], DISFA [42], and RU-FACS [3] illustrate the benefits of the proposed CPM models over baseline methods and state-of-the-art semisupervised learning and transfer learning methods. PMID:27479964

Confidence and memory: assessing positive and negative correlations.

PubMed

Roediger, Henry L; DeSoto, K Andrew

2014-01-01

The capacity to learn and remember surely evolved to help animals solve problems in their quest to reproduce and survive. In humans we assume that metacognitive processes also evolved, so that we know when to trust what we remember (i.e., when we have high confidence in our memories) and when not to (when we have low confidence). However this latter feature has been questioned by researchers, with some finding a high correlation between confidence and accuracy in reports from memory and others finding little to no correlation. In two experiments we report a recognition memory paradigm that, using the same materials (categorised lists), permits the study of positive correlations, zero correlations, and negative correlations between confidence and accuracy within the same procedure. We had subjects study words from semantic categories with the five items most frequently produced in norms omitted from the list; later, subjects were given an old/new recognition test and made confidence ratings on their judgements. Although the correlation between confidence and accuracy for studied items was generally positive, the correlation for the five omitted items was negative in some methods of analysis. We pinpoint the similarity between lures and targets as creating inversions between confidence and accuracy in memory. We argue that, while confidence is generally a useful indicant of accuracy in reports from memory, in certain environmental circumstances even adaptive processes can foster illusions of memory. Thus understanding memory illusions is similar to understanding perceptual illusions: Processes that are usually adaptive can go awry under certain circumstances.

Dynamic visual noise reduces confidence in short-term memory for visual information.

PubMed

Kemps, Eva; Andrade, Jackie

2012-05-01

Previous research has shown effects of the visual interference technique, dynamic visual noise (DVN), on visual imagery, but not on visual short-term memory, unless retention of precise visual detail is required. This study tested the prediction that DVN does also affect retention of gross visual information, specifically by reducing confidence. Participants performed a matrix pattern memory task with three retention interval interference conditions (DVN, static visual noise and no interference control) that varied from trial to trial. At recall, participants indicated whether or not they were sure of their responses. As in previous research, DVN did not impair recall accuracy or latency on the task, but it did reduce recall confidence relative to static visual noise and no interference. We conclude that DVN does distort visual representations in short-term memory, but standard coarse-grained recall measures are insensitive to these distortions.

Cochrane Qualitative and Implementation Methods Group guidance series-paper 3: methods for assessing methodological limitations, data extraction and synthesis, and confidence in synthesized qualitative findings.

PubMed

Noyes, Jane; Booth, Andrew; Flemming, Kate; Garside, Ruth; Harden, Angela; Lewin, Simon; Pantoja, Tomas; Hannes, Karin; Cargo, Margaret; Thomas, James

2018-05-01

The Cochrane Qualitative and Implementation Methods Group develops and publishes guidance on the synthesis of qualitative and mixed-method implementation evidence. Choice of appropriate methodologies, methods, and tools is essential when developing a rigorous protocol and conducting the synthesis. Cochrane authors who conduct qualitative evidence syntheses have thus far used a small number of relatively simple methods to address similarly written questions. Cochrane has invested in methodological work to develop new tools and to encourage the production of exemplar reviews to show the value of more innovative methods that address a wider range of questions. In this paper, in the series, we report updated guidance on the selection of tools to assess methodological limitations in qualitative studies and methods to extract and synthesize qualitative evidence. We recommend application of Grades of Recommendation, Assessment, Development, and Evaluation-Confidence in the Evidence from Qualitative Reviews to assess confidence in qualitative synthesized findings. This guidance aims to support review authors to undertake a qualitative evidence synthesis that is intended to be integrated subsequently with the findings of one or more Cochrane reviews of the effects of similar interventions. The review of intervention effects may be undertaken concurrently with or separate to the qualitative evidence synthesis. We encourage further development through reflection and formal testing. Copyright © 2017 Elsevier Inc. All rights reserved.

Reference Value Advisor: a new freeware set of macroinstructions to calculate reference intervals with Microsoft Excel.

PubMed

Geffré, Anne; Concordet, Didier; Braun, Jean-Pierre; Trumel, Catherine

2011-03-01

International recommendations for determination of reference intervals have been recently updated, especially for small reference sample groups, and use of the robust method and Box-Cox transformation is now recommended. Unfortunately, these methods are not included in most software programs used for data analysis by clinical laboratories. We have created a set of macroinstructions, named Reference Value Advisor, for use in Microsoft Excel to calculate reference limits applying different methods. For any series of data, Reference Value Advisor calculates reference limits (with 90% confidence intervals [CI]) using a nonparametric method when n≥40 and by parametric and robust methods from native and Box-Cox transformed values; tests normality of distributions using the Anderson-Darling test and outliers using Tukey and Dixon-Reed tests; displays the distribution of values in dot plots and histograms and constructs Q-Q plots for visual inspection of normality; and provides minimal guidelines in the form of comments based on international recommendations. The critical steps in determination of reference intervals are correct selection of as many reference individuals as possible and analysis of specimens in controlled preanalytical and analytical conditions. Computing tools cannot compensate for flaws in selection and size of the reference sample group and handling and analysis of samples. However, if those steps are performed properly, Reference Value Advisor, available as freeware at http://www.biostat.envt.fr/spip/spip.php?article63, permits rapid assessment and comparison of results calculated using different methods, including currently unavailable methods. This allows for selection of the most appropriate method, especially as the program provides the CI of limits. It should be useful in veterinary clinical pathology when only small reference sample groups are available. ©2011 American Society for Veterinary Clinical Pathology.

Confidence to cook vegetables and the buying habits of Australian households.

PubMed

Winkler, Elisabeth; Turrell, Gavin

2009-10-01

Cooking skills are emphasized in nutrition promotion but their distribution among population subgroups and relationship to dietary behavior is researched by few population-based studies. This study examined the relationships between confidence to cook, sociodemographic characteristics, and household vegetable purchasing. This cross-sectional study of 426 randomly selected households in Brisbane, Australia, used a validated questionnaire to assess household vegetable purchasing habits and the confidence to cook of the person who most often prepares food for these households. The mutually adjusted odds ratios (ORs) of lacking confidence to cook were assessed across a range of demographic subgroups using multiple logistic regression models. Similarly, mutually adjusted mean vegetable purchasing scores were calculated using multiple linear regression for different population groups and for respondents with varying confidence levels. Lacking confidence to cook using a variety of techniques was more common among respondents with less education (OR 3.30; 95% confidence interval [CI] 1.01 to 10.75) and was less common among respondents who lived with minors (OR 0.22; 95% CI 0.09 to 0.53) and other adults (OR 0.43; 95% CI 0.24 to 0.78). Lack of confidence to prepare vegetables was associated with being male (OR 2.25; 95% CI 1.24 to 4.08), low education (OR 6.60; 95% CI 2.08 to 20.91), lower household income (OR 2.98; 95% CI 1.02 to 8.72) and living with other adults (OR 0.53; 95% CI 0.29 to 0.98). Households bought a greater variety of vegetables on a regular basis when the main chef was confident to prepare them (difference: 18.60; 95% CI 14.66 to 22.54), older (difference: 8.69; 95% CI 4.92 to 12.47), lived with at least one other adult (difference: 5.47; 95% CI 2.82 to 8.12) or at least one minor (difference: 2.86; 95% CI 0.17 to 5.55). Cooking skills may contribute to socioeconomic dietary differences, and may be a useful strategy for promoting fruit and vegetable

Overdistribution Illusions: Categorical Judgments Produce Them, Confidence Ratings Reduce Them

PubMed Central

Brainerd, C. J.; Nakamura, K.; Reyna, V. F.; Holliday, R. E.

2017-01-01

Overdistribution is a form of memory distortion in which an event is remembered as belonging to too many episodic states, states that are logically or empirically incompatible with each other. We investigated a response formatting method of suppressing two basic types of overdistribution, disjunction and conjunction illusions, which parallel some classic illusions in the judgment and decision making literature. In this method, subjects respond to memory probes by rating their confidence that test cues belong to specific episodic states (e.g., presented on List 1, presented on List 2), rather than by making the usual categorical judgments about those states. The central prediction, which was derived from the task calibration principle of fuzzy-trace theory, was that confidence ratings should reduce overdistribution by diminishing subjects’ reliance on noncompensatory gist memories. The data of three experiments agreed with that prediction. In Experiment 1, there were reliable disjunction illusions with categorical judgments but not with confidence ratings. In Experiment 2, both response formats produced reliable disjunction illusions, but those for confidence ratings were much smaller than those for categorical judgments. In Experiment 3, there were reliable conjunction illusions with categorical judgments but not with confidence ratings. Apropos of recent controversies over confidence-accuracy correlations in memory, such correlations were positive for hits, negative for correct rejections, and the two types of correlations were of equal magnitude. PMID:28054811

Extended score interval in the assessment of basic surgical skills.

PubMed

Acosta, Stefan; Sevonius, Dan; Beckman, Anders

2015-01-01

Introduction The Basic Surgical Skills course uses an assessment score interval of 0-3. An extended score interval, 1-6, was proposed by the Swedish steering committee of the course. The aim of this study was to analyze the trainee scores in the current 0-3 scored version compared to a proposed 1-6 scored version. Methods Sixteen participants, seven females and nine males, were evaluated in the current and proposed assessment forms by instructors, observers, and learners themselves during the first and second day. In each assessment form, 17 tasks were assessed. The inter-rater reliability between the current and the proposed score sheets were evaluated with intraclass correlation (ICC) with 95% confidence intervals (CI). Results The distribution of scores for 'knot tying' at the last time point and 'bowel anastomosis side to side' given by the instructors in the current assessment form showed that the highest score was given in 31 and 62%, respectively. No ceiling effects were found in the proposed assessment form. The overall ICC between the current and proposed score sheets after assessment by the instructors increased from 0.38 (95% CI 0.77-0.78) on Day 1 to 0.83 (95% CI 0.51-0.94) on Day 2. Discussion A clear ceiling effect of scores was demonstrated in the current assessment form, questioning its validity. The proposed score sheet provides more accurate scores and seems to be a better feedback instrument for learning technical surgical skills in the Basic Surgical Skills course.

Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance

PubMed Central

Cortese, Aurelio; Amano, Kaoru; Koizumi, Ai; Kawato, Mitsuo; Lau, Hakwan

2016-01-01

A central controversy in metacognition studies concerns whether subjective confidence directly reflects the reliability of perceptual or cognitive processes, as suggested by normative models based on the assumption that neural computations are generally optimal. This view enjoys popularity in the computational and animal literatures, but it has also been suggested that confidence may depend on a late-stage estimation dissociable from perceptual processes. Yet, at least in humans, experimental tools have lacked the power to resolve these issues convincingly. Here, we overcome this difficulty by using the recently developed method of decoded neurofeedback (DecNef) to systematically manipulate multivoxel correlates of confidence in a frontoparietal network. Here we report that bi-directional changes in confidence do not affect perceptual accuracy. Further psychophysical analyses rule out accounts based on simple shifts in reporting strategy. Our results provide clear neuroscientific evidence for the systematic dissociation between confidence and perceptual performance, and thereby challenge current theoretical thinking. PMID:27976739

Change in Breast Cancer Screening Intervals Since the 2009 USPSTF Guideline.

PubMed

Wernli, Karen J; Arao, Robert F; Hubbard, Rebecca A; Sprague, Brian L; Alford-Teaster, Jennifer; Haas, Jennifer S; Henderson, Louise; Hill, Deidre; Lee, Christoph I; Tosteson, Anna N A; Onega, Tracy

2017-08-01

In 2009, the U.S. Preventive Services Task Force (USPSTF) recommended biennial mammography for women aged 50-74 years and shared decision-making for women aged 40-49 years for breast cancer screening. We evaluated changes in mammography screening interval after the 2009 recommendations. We conducted a prospective cohort study of women aged 40-74 years who received 821,052 screening mammograms between 2006 and 2012 using data from the Breast Cancer Surveillance Consortium. We compared changes in screening intervals and stratified intervals based on whether the mammogram at the end of the interval occurred before or after the 2009 recommendation. Differences in mean interval length by woman-level characteristics were compared using linear regression. The mean interval (in months) minimally decreased after the 2009 USPSTF recommendations. Among women aged 40-49 years, the mean interval decreased from 17.2 months to 17.1 months (difference -0.16%, 95% confidence interval [CI] -0.30 to -0.01). Similar small reductions were seen for most age groups. The largest change in interval length in the post-USPSTF period was declines among women with a first-degree family history of breast cancer (difference -0.68%, 95% CI -0.82 to -0.54) or a 5-year breast cancer risk ≥2.5% (difference -0.58%, 95% CI -0.73 to -0.44). The 2009 USPSTF recommendation did not lengthen the average mammography interval among women routinely participating in mammography screening. Future studies should evaluate whether breast cancer screening intervals lengthen toward biennial intervals following new national 2016 breast cancer screening recommendations, particularly among women less than 50 years of age.

A method of bias correction for maximal reliability with dichotomous measures.

PubMed

Penev, Spiridon; Raykov, Tenko

2010-02-01

This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.

Obtaining appropriate interval estimates for age when multiple indicators are used: evaluation of an ad-hoc procedure.

PubMed

Fieuws, Steffen; Willems, Guy; Larsen-Tangmose, Sara; Lynnerup, Niels; Boldsen, Jesper; Thevissen, Patrick

2016-03-01

When an estimate of age is needed, typically multiple indicators are present as found in skeletal or dental information. There exists a vast literature on approaches to estimate age from such multivariate data. Application of Bayes' rule has been proposed to overcome drawbacks of classical regression models but becomes less trivial as soon as the number of indicators increases. Each of the age indicators can lead to a different point estimate ("the most plausible value for age") and a prediction interval ("the range of possible values"). The major challenge in the combination of multiple indicators is not the calculation of a combined point estimate for age but the construction of an appropriate prediction interval. Ignoring the correlation between the age indicators results in intervals being too small. Boldsen et al. (2002) presented an ad-hoc procedure to construct an approximate confidence interval without the need to model the multivariate correlation structure between the indicators. The aim of the present paper is to bring under attention this pragmatic approach and to evaluate its performance in a practical setting. This is all the more needed since recent publications ignore the need for interval estimation. To illustrate and evaluate the method, Köhler et al. (1995) third molar scores are used to estimate the age in a dataset of 3200 male subjects in the juvenile age range.

Optimal Interval for Repeated Gastric Cancer Screening in Normal-Risk Healthy Korean Adults: A Retrospective Cohort Study

PubMed Central

Bae, Jong-Myon; Shin, Sang Yop; Kim, Eun Hee

2015-01-01

Purpose This retrospective cohort study was conducted to estimate the optimal interval for gastric cancer screening in Korean adults with initial negative screening results. Materials and Methods This study consisted of voluntary Korean screenees aged 40 to 69 years who underwent subsequent screening gastroscopies after testing negative in the baseline screening performed between January 2007 and December 2011. A new case was defined as the presence of gastric cancer cells in biopsy specimens obtained upon gastroscopy. The follow-up periods were calculated during the months between the date of baseline screening gastroscopy and positive findings upon subsequent screenings, stratified by sex and age group. The mean sojourn time (MST) for determining the screening interval was estimated using the prevalence/incidence ratio. Results Of the 293,520 voluntary screenees for the gastric cancer screening program, 91,850 (31.29%) underwent subsequent screening gastroscopies between January 2007 and December 2011. The MSTs in men and women were 21.67 months (95% confidence intervals [CI], 17.64 to 26.88 months) and 15.14 months (95% CI, 9.44 to 25.85 months), respectively. Conclusion These findings suggest that the optimal interval for subsequent gastric screening in both men and women is 24 months, supporting the 2-year interval recommended by the nationwide gastric cancer screening program. PMID:25687874

A NEW METHOD OF SWEAT TESTING: THE CF QUANTUM® SWEAT TEST

PubMed Central

Rock, Michael J.; Makholm, Linda; Eickhoff, Jens

2015-01-01

Background Conventional methods of sweat testing are time consuming and have many steps that can and do lead to errors. This study compares conventional sweat testing to a new quantitative method, the CF Quantum® (CFQT) sweat test. This study tests the diagnostic accuracy and analytic validity of the CFQT. Methods Previously diagnosed CF patients and patients who required a sweat test for clinical indications were invited to have the CFQT test performed. Both conventional sweat testing and the CFQT were performed bilaterally on the same day. Pairs of data from each test are plotted as a correlation graph and Bland Altman plot. Sensitivity and specificity were calculated as well as the means and coefficient of variation by test and by extremity. After completing the study, subjects or their parents were asked for their preference of the CFQT and conventional sweat testing. Results The correlation coefficient between the CFQT and conventional sweat testing was 0.98 (95% confidence interval: 0.97–0.99). The sensitivity and specificity of the CFQT in diagnosing CF was 100% (95% confidence interval: 94–100%) and 96% (95% confidence interval: 89–99%), respectively. In one center in this three center multicenter study, there were higher sweat chloride values in patients with CF and also more tests that were invalid due to discrepant values between the two extremities. The percentage of invalid tests was higher in the CFQT method (16.5%) compared to conventional sweat testing (3.8%)(p < 0.001). In the post-test questionnaire, 88% of subjects/parents preferred the CFQT test. Conclusions The CFQT is a fast and simple method of quantitative sweat chloride determination. This technology requires further refinement to improve the analytic accuracy at higher sweat chloride values and to decrease the number of invalid tests. PMID:24862724

The idiosyncratic nature of confidence

PubMed Central

Navajas, Joaquin; Hindocha, Chandni; Foda, Hebah; Keramati, Mehdi; Latham, Peter E; Bahrami, Bahador

2017-01-01

Confidence is the ‘feeling of knowing’ that accompanies decision making. Bayesian theory proposes that confidence is a function solely of the perceived probability of being correct. Empirical research has suggested, however, that different individuals may perform different computations to estimate confidence from uncertain evidence. To test this hypothesis, we collected confidence reports in a task where subjects made categorical decisions about the mean of a sequence. We found that for most individuals, confidence did indeed reflect the perceived probability of being correct. However, in approximately half of them, confidence also reflected a different probabilistic quantity: the perceived uncertainty in the estimated variable. We found that the contribution of both quantities was stable over weeks. We also observed that the influence of the perceived probability of being correct was stable across two tasks, one perceptual and one cognitive. Overall, our findings provide a computational interpretation of individual differences in human confidence. PMID:29152591

Confidence of compliance: a Bayesian approach for percentile standards.

PubMed

McBride, G B; Ellis, J C

2001-04-01

Rules for assessing compliance with percentile standards commonly limit the number of exceedances permitted in a batch of samples taken over a defined assessment period. Such rules are commonly developed using classical statistical methods. Results from alternative Bayesian methods are presented (using beta-distributed prior information and a binomial likelihood), resulting in "confidence of compliance" graphs. These allow simple reading of the consumer's risk and the supplier's risks for any proposed rule. The influence of the prior assumptions required by the Bayesian technique on the confidence results is demonstrated, using two reference priors (uniform and Jeffreys') and also using optimistic and pessimistic user-defined priors. All four give less pessimistic results than does the classical technique, because interpreting classical results as "confidence of compliance" actually invokes a Bayesian approach with an extreme prior distribution. Jeffreys' prior is shown to be the most generally appropriate choice of prior distribution. Cost savings can be expected using rules based on this approach.

Fuzzy interval Finite Element/Statistical Energy Analysis for mid-frequency analysis of built-up systems with mixed fuzzy and interval parameters

NASA Astrophysics Data System (ADS)

Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan

2016-10-01

This paper introduces mixed fuzzy and interval parametric uncertainties into the FE components of the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model for mid-frequency analysis of built-up systems, thus an uncertain ensemble combining non-parametric with mixed fuzzy and interval parametric uncertainties comes into being. A fuzzy interval Finite Element/Statistical Energy Analysis (FIFE/SEA) framework is proposed to obtain the uncertain responses of built-up systems, which are described as intervals with fuzzy bounds, termed as fuzzy-bounded intervals (FBIs) in this paper. Based on the level-cut technique, a first-order fuzzy interval perturbation FE/SEA (FFIPFE/SEA) and a second-order fuzzy interval perturbation FE/SEA method (SFIPFE/SEA) are developed to handle the mixed parametric uncertainties efficiently. FFIPFE/SEA approximates the response functions by the first-order Taylor series, while SFIPFE/SEA improves the accuracy by considering the second-order items of Taylor series, in which all the mixed second-order items are neglected. To further improve the accuracy, a Chebyshev fuzzy interval method (CFIM) is proposed, in which the Chebyshev polynomials is used to approximate the response functions. The FBIs are eventually reconstructed by assembling the extrema solutions at all cut levels. Numerical results on two built-up systems verify the effectiveness of the proposed methods.

Overdistribution illusions: Categorical judgments produce them, confidence ratings reduce them.

PubMed

Brainerd, C J; Nakamura, K; Reyna, V F; Holliday, R E

2017-01-01

Overdistribution is a form of memory distortion in which an event is remembered as belonging to too many episodic states, states that are logically or empirically incompatible with each other. We investigated a response formatting method of suppressing 2 basic types of overdistribution, disjunction and conjunction illusions, which parallel some classic illusions in the judgment and decision making literature. In this method, subjects respond to memory probes by rating their confidence that test cues belong to specific episodic states (e.g., presented on List 1, presented on List 2), rather than by making the usual categorical judgments about those states. The central prediction, which was derived from the task calibration principle of fuzzy-trace theory, was that confidence ratings should reduce overdistribution by diminishing subjects' reliance on noncompensatory gist memories. The data of 3 experiments agreed with that prediction. In Experiment 1, there were reliable disjunction illusions with categorical judgments but not with confidence ratings. In Experiment 2, both response formats produced reliable disjunction illusions, but those for confidence ratings were much smaller than those for categorical judgments. In Experiment 3, there were reliable conjunction illusions with categorical judgments but not with confidence ratings. Apropos of recent controversies over confidence-accuracy correlations in memory, such correlations were positive for hits, negative for correct rejections, and the 2 types of correlations were of equal magnitude. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A method to deconvolve stellar rotational velocities II. The probability distribution function via Tikhonov regularization

NASA Astrophysics Data System (ADS)

Christen, Alejandra; Escarate, Pedro; Curé, Michel; Rial, Diego F.; Cassetti, Julia

2016-10-01

Aims: Knowing the distribution of stellar rotational velocities is essential for understanding stellar evolution. Because we measure the projected rotational speed v sin I, we need to solve an ill-posed problem given by a Fredholm integral of the first kind to recover the "true" rotational velocity distribution. Methods: After discretization of the Fredholm integral we apply the Tikhonov regularization method to obtain directly the probability distribution function for stellar rotational velocities. We propose a simple and straightforward procedure to determine the Tikhonov parameter. We applied Monte Carlo simulations to prove that the Tikhonov method is a consistent estimator and asymptotically unbiased. Results: This method is applied to a sample of cluster stars. We obtain confidence intervals using a bootstrap method. Our results are in close agreement with those obtained using the Lucy method for recovering the probability density distribution of rotational velocities. Furthermore, Lucy estimation lies inside our confidence interval. Conclusions: Tikhonov regularization is a highly robust method that deconvolves the rotational velocity probability density function from a sample of v sin I data directly without the need for any convergence criteria.

EVALUATION OF ANALYTICAL REPORTING ERRORS GENERATED AS DESCRIBED IN SW-846 METHOD 8261A

EPA Science Inventory

SW-846 Method 8261A incorporates the vacuum distillation of analytes from samples, and their recoveries are characterized by internal standards. The internal standards measure recoveries with confidence intervals as functions of physical properties. The frequency the calculate...

Importance of Building Confidence in Patient Communication and Clinical Skills Among Chiropractic Students

PubMed Central

Hecimovich, Mark D.; Volet, Simone E.

2009-01-01

Purpose: One important objective of chiropractic education is to foster student professional confidence and competence in patient communication and clinical skills. Therefore, the aim of this article is to review the extant literature on this topic, stressing the significance of building students' confidence for effective practice and the need for more research in this area. Methods: The authors reviewed MEDLINE and ERIC from 1980 through 2008 using several key words pertinent to confidence and health care. Three distinct, but interrelated, bodies of literature were assessed, including professional confidence in health care research, the nature and development of confidence in educational psychology research, and fostering professional confidence in chiropractic education. Results: It was apparent through the review that chiropractic education has developed educational methods and opportunities that may help develop and build student confidence in patient communication and clinical skills. However, there has not been sufficient research to provide empirical evidence of the impact. Conclusion: Fostering chiropractic students' development of confidence in what they say and do is of paramount importance not only to them as new practitioners but more importantly to the patient. There is no doubt that a better understanding of how confidence can be developed and consolidated during tertiary study should be a major goal of chiropractic education PMID:19826543

Market Confidence Predicts Stock Price: Beyond Supply and Demand

PubMed Central

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price. PMID:27391816

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

PubMed

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

INAPPROPRIATE CONFIDENCE AND RETIREMENT PLANNING: FOUR STUDIES WITH A NATIONAL SAMPLE

PubMed Central

Parker, Andrew M.; de Bruin, Wändi Bruine; Yoong, Joanne; Willis, Robert

2011-01-01

Financial decisions about investing and saving for retirement are increasingly complex, requiring financial knowledge and confidence in that knowledge. Few studies have examined whether direct assessments of individuals’ confidence are related to the outcomes of their financial decisions. Here, we analyzed data from a national sample recruited through RAND’s American Life Panel (ALP), an internet panel of U.S. adults aged 18 to 88. We examined the relationship of confidence with self-reported and actual financial decisions, using four different tasks, each performed by overlapping samples of ALP participants. The four tasks were designed by different researchers for different purposes, using different methods to assess confidence. Yet, measures of confidence were correlated across tasks, and results were consistent across methodologies. Confidence and knowledge showed only modest positive correlations. However, even after controlling for actual knowledge, individuals with greater confidence were more likely to report financial planning for retirement and to successfully minimize fees on a hypothetical investment task. Implications for the role of confidence (even if it is unjustified) in investment behavior is discussed. PMID:23049164

Extended-Interval Gentamicin Dosing in Achieving Therapeutic Concentrations in Malaysian Neonates

PubMed Central

Tan, Sin Li; Wan, Angeline SL

2015-01-01

OBJECTIVE: To evaluate the usefulness of extended-interval gentamicin dosing practiced in neonatal intensive care unit (NICU) and special care nursery (SCN) of a Malaysian hospital. METHODS: Cross-sectional observational study with pharmacokinetic analysis of all patients aged ≤28 days who received gentamicin treatment in NICU/SCN. Subjects received dosing according to a regimen modified from an Australian-based pediatric guideline. During a study period of 3 months, subjects were evaluated for gestational age, body weight, serum creatinine concentration, gentamicin dose/interval, serum peak and trough concentrations, and pharmacokinetic parameters. Descriptive percentages were used to determine the overall dosing accuracy, while analysis of variance (ANOVA) was conducted to compare the accuracy rates among different gestational ages. Pharmacokinetic profile among different gestational age and body weight groups were compared by using ANOVA. RESULTS: Of the 113 subjects included, 82.3% (n = 93) achieved therapeutic concentrations at the first drug-monitoring assessment. There was no significant difference found between the percentage of term neonates who achieved therapeutic concentrations and the premature group (87.1% vs. 74.4%), p = 0.085. A total of 112 subjects (99.1%) achieved desired therapeutic trough concentration of <2 mg/L. Mean gentamicin peak concentration was 8.52 mg/L (95% confidence interval [Cl], 8.13–8.90 mg/L) and trough concentration was 0.54 mg/L (95% CI, 0.48–0.60 mg/L). Mean volume of distribution, half-life, and elimination rate were 0.65 L/kg (95% CI, 0.62–0.68 L/kg), 6.96 hours (95% CI, 6.52–7.40 hours), and 0.11 hour−1 (95% CI, 0.10–0.11 hour−1), respectively. CONCLUSION: The larger percentage of subjects attaining therapeutic range with extended-interval gentamicin dosing suggests that this regimen is appropriate and can be safely used among Malaysian neonates. PMID:25964729

Associations of Menthol Use with Motivation and Confidence to Quit Smoking

PubMed Central

Reitzel, Lorraine R.; Etzel, Carol J.; Cao, Yumei; Okuyemi, Kolawole S.; Ahluwalia, Jasjit S.

2013-01-01

Objectives To examine associations of menthol cigarette use with motivation and confidence to quit smoking, and potential moderation by race, among adult current smokers (N = 1067; 85% White, 15% Black). Methods Regression analyses, adjusted for sociodemographics and tobacco dependence, examined associations of menthol use with motivation and confidence to quit smoking with and without an interaction term for race. Results Main effects were not significant; however, there was a significant interaction for confidence to quit smoking (p = .02). Stratified analyses indicated that Black menthol users were more confident about quitting than Black non-menthol users (p = .01). Conclusions Given their relatively lower quit rates as cited in previous literature, Black menthol users appear overly confident about their ability to quit smoking. PMID:23985285

The Use of One-Sample Prediction Intervals for Estimating CO2 Scrubber Canister Durations

DTIC Science & Technology

2012-10-01

Grade and 812 D-Grade Sofnolime.3 Definitions According to Devore,4 A CI (confidence interval) refers to a parameter, or population ... characteristic , whose value is fixed but unknown to us. In contrast, a future value of Y is not a parameter but instead a random variable; for this

The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables.

PubMed

Yin, Kedong; Wang, Pengyu; Li, Xuemei

2017-12-13

With respect to multi-attribute group decision-making (MAGDM) problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs) and the weights (including expert and attribute weight) are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV.

An Aquatic Decomposition Scoring Method to Potentially Predict the Postmortem Submersion Interval of Bodies Recovered from the North Sea.

PubMed

van Daalen, Marjolijn A; de Kat, Dorothée S; Oude Grotebevelsborg, Bernice F L; de Leeuwe, Roosje; Warnaar, Jeroen; Oostra, Roelof Jan; M Duijst-Heesters, Wilma L J

2017-03-01

This study aimed to develop an aquatic decomposition scoring (ADS) method and investigated the predictive value of this method in estimating the postmortem submersion interval (PMSI) of bodies recovered from the North Sea. This method, consisting of an ADS item list and a pictorial reference atlas, showed a high interobserver agreement (Krippendorff's alpha ≥ 0.93) and hence proved to be valid. This scoring method was applied to data, collected from closed cases-cases in which the postmortal submersion interval (PMSI) was known-concerning bodies recovered from the North Sea from 1990 to 2013. Thirty-eight cases met the inclusion criteria and were scored by quantifying the observed total aquatic decomposition score (TADS). Statistical analysis demonstrated that TADS accurately predicts the PMSI (p < 0.001), confirming that the decomposition process in the North Sea is strongly correlated to time. © 2017 American Academy of Forensic Sciences.

Sources of sport confidence, imagery type and performance among competitive athletes: the mediating role of sports confidence.

PubMed

Levy, A R; Perry, J; Nicholls, A R; Larkin, D; Davies, J

2015-01-01

This study explored the mediating role of sport confidence upon (1) sources of sport confidence-performance relationship and (2) imagery-performance relationship. Participants were 157 competitive athletes who completed state measures of confidence level/sources, imagery type and performance within one hour after competition. Among the current sample, confirmatory factor analysis revealed appropriate support for the nine-factor SSCQ and the five-factor SIQ. Mediational analysis revealed that sport confidence had a mediating influence upon the achievement source of confidence-performance relationship. In addition, both cognitive and motivational imagery types were found to be important sources of confidence, as sport confidence mediated imagery type- performance relationship. Findings indicated that athletes who construed confidence from their own achievements and report multiple images on a more frequent basis are likely to benefit from enhanced levels of state sport confidence and subsequent performance.

An MLE method for finding LKB NTCP model parameters using Monte Carlo uncertainty estimates

NASA Astrophysics Data System (ADS)

Carolan, Martin; Oborn, Brad; Foo, Kerwyn; Haworth, Annette; Gulliford, Sarah; Ebert, Martin

2014-03-01

The aims of this work were to establish a program to fit NTCP models to clinical data with multiple toxicity endpoints, to test the method using a realistic test dataset, to compare three methods for estimating confidence intervals for the fitted parameters and to characterise the speed and performance of the program.

Five-year risk of interval-invasive second breast cancer.

PubMed

Lee, Janie M; Buist, Diana S M; Houssami, Nehmat; Dowling, Emily C; Halpern, Elkan F; Gazelle, G Scott; Lehman, Constance D; Henderson, Louise M; Hubbard, Rebecca A

2015-07-01

Earlier detection of second breast cancers after primary breast cancer (PBC) treatment improves survival, yet mammography is less accurate in women with prior breast cancer. The purpose of this study was to examine women presenting clinically with second breast cancers after negative surveillance mammography (interval cancers), and to estimate the five-year risk of interval-invasive second cancers for women with varying risk profiles. We evaluated a prospective cohort of 15 114 women with 47 717 surveillance mammograms diagnosed with stage 0-II unilateral PBC from 1996 through 2008 at facilities in the Breast Cancer Surveillance Consortium. We used discrete time survival models to estimate the association between odds of an interval-invasive second breast cancer and candidate predictors, including demographic, PBC, and imaging characteristics. All statistical tests were two-sided. The cumulative incidence of second breast cancers after five years was 54.4 per 1000 women, with 325 surveillance-detected and 138 interval-invasive second breast cancers. The five-year risk of interval-invasive second cancer for women with referent category characteristics was 0.60%. For women with the most and least favorable profiles, the five-year risk ranged from 0.07% to 6.11%. Multivariable modeling identified grade II PBC (odds ratio [OR] = 1.95, 95% confidence interval [CI] = 1.15 to 3.31), treatment with lumpectomy without radiation (OR = 3.27, 95% CI = 1.91 to 5.62), interval PBC presentation (OR = 2.01, 95% CI 1.28 to 3.16), and heterogeneously dense breasts on mammography (OR = 1.54, 95% CI = 1.01 to 2.36) as independent predictors of interval-invasive second breast cancers. PBC diagnosis and treatment characteristics contribute to variation in subsequent-interval second breast cancer risk. Consideration of these factors may be useful in developing tailored post-treatment imaging surveillance plans. © The Author 2015. Published by Oxford University Press. All rights reserved

Using a Nonparametric Bootstrap to Obtain a Confidence Interval for Pearson's "r" with Cluster Randomized Data: A Case Study

ERIC Educational Resources Information Center

Wagstaff, David A.; Elek, Elvira; Kulis, Stephen; Marsiglia, Flavio

2009-01-01

A nonparametric bootstrap was used to obtain an interval estimate of Pearson's "r," and test the null hypothesis that there was no association between 5th grade students' positive substance use expectancies and their intentions to not use substances. The students were participating in a substance use prevention program in which the unit of…

Evaluating the Impact of Guessing and Its Interactions with Other Test Characteristics on Confidence Interval Procedures for Coefficient Alpha

ERIC Educational Resources Information Center

Paek, Insu

2016-01-01

The effect of guessing on the point estimate of coefficient alpha has been studied in the literature, but the impact of guessing and its interactions with other test characteristics on the interval estimators for coefficient alpha has not been fully investigated. This study examined the impact of guessing and its interactions with other test…

A computer program for uncertainty analysis integrating regression and Bayesian methods

USGS Publications Warehouse

Lu, Dan; Ye, Ming; Hill, Mary C.; Poeter, Eileen P.; Curtis, Gary

2014-01-01

This work develops a new functionality in UCODE_2014 to evaluate Bayesian credible intervals using the Markov Chain Monte Carlo (MCMC) method. The MCMC capability in UCODE_2014 is based on the FORTRAN version of the differential evolution adaptive Metropolis (DREAM) algorithm of Vrugt et al. (2009), which estimates the posterior probability density function of model parameters in high-dimensional and multimodal sampling problems. The UCODE MCMC capability provides eleven prior probability distributions and three ways to initialize the sampling process. It evaluates parametric and predictive uncertainties and it has parallel computing capability based on multiple chains to accelerate the sampling process. This paper tests and demonstrates the MCMC capability using a 10-dimensional multimodal mathematical function, a 100-dimensional Gaussian function, and a groundwater reactive transport model. The use of the MCMC capability is made straightforward and flexible by adopting the JUPITER API protocol. With the new MCMC capability, UCODE_2014 can be used to calculate three types of uncertainty intervals, which all can account for prior information: (1) linear confidence intervals which require linearity and Gaussian error assumptions and typically 10s–100s of highly parallelizable model runs after optimization, (2) nonlinear confidence intervals which require a smooth objective function surface and Gaussian observation error assumptions and typically 100s–1,000s of partially parallelizable model runs after optimization, and (3) MCMC Bayesian credible intervals which require few assumptions and commonly 10,000s–100,000s or more partially parallelizable model runs. Ready access allows users to select methods best suited to their work, and to compare methods in many circumstances.

Hematology and biochemistry reference intervals for Ontario commercial nursing pigs close to the time of weaning

PubMed Central

Perri, Amanda M.; O’Sullivan, Terri L.; Harding, John C.S.; Wood, R. Darren; Friendship, Robert M.

2017-01-01

The evaluation of pig hematology and biochemistry parameters is rarely done largely due to the costs associated with laboratory testing and labor, and the limited availability of reference intervals needed for interpretation. Within-herd and between-herd biological variation of these values also make it difficult to establish reference intervals. Regardless, baseline reference intervals are important to aid veterinarians in the interpretation of blood parameters for the diagnosis and treatment of diseased swine. The objective of this research was to provide reference intervals for hematology and biochemistry parameters of 3-week-old commercial nursing piglets in Ontario. A total of 1032 pigs lacking clinical signs of disease from 20 swine farms were sampled for hematology and iron panel evaluation, with biochemistry analysis performed on a subset of 189 randomly selected pigs. The 95% reference interval, mean, median, range, and 90% confidence intervals were calculated for each parameter. PMID:28373729

Exploring separable components of institutional confidence.

PubMed

Hamm, Joseph A; PytlikZillig, Lisa M; Tomkins, Alan J; Herian, Mitchel N; Bornstein, Brian H; Neeley, Elizabeth M

2011-01-01

Despite its contemporary and theoretical importance in numerous social scientific disciplines, institutional confidence research is limited by a lack of consensus regarding the distinctions and relationships among related constructs (e.g., trust, confidence, legitimacy, distrust, etc.). This study examined four confidence-related constructs that have been used in studies of trust/confidence in the courts: dispositional trust, trust in institutions, obligation to obey the law, and cynicism. First, the separability of the four constructs was examined by exploratory factor analyses. Relationships among the constructs were also assessed. Next, multiple regression analyses were used to explore each construct's independent contribution to confidence in the courts. Finally, a second study replicated the first study and also examined the stability of the institutional confidence constructs over time. Results supported the hypothesized separability of, and correlations among, the four confidence-related constructs. The extent to which the constructs independently explained the observed variance in confidence in the courts differed as a function of the specific operationalization of confidence in the courts and the individual predictor measures. Implications for measuring institutional confidence and future research directions are discussed. Copyright © 2011 John Wiley & Sons, Ltd.

Disconnections Between Teacher Expectations and Student Confidence in Bioethics

NASA Astrophysics Data System (ADS)

Hanegan, Nikki L.; Price, Laura; Peterson, Jeremy

2008-09-01

This study examines how student practice of scientific argumentation using socioscientific bioethics issues affects both teacher expectations of students’ general performance and student confidence in their own work. When teachers use bioethical issues in the classroom students can gain not only biology content knowledge but also important decision-making skills. Learning bioethics through scientific argumentation gives students opportunities to express their ideas, formulate educated opinions and value others’ viewpoints. Research has shown that science teachers’ expectations of student success and knowledge directly influence student achievement and confidence levels. Our study analyzes pre-course and post-course surveys completed by students enrolled in a university level bioethics course ( n = 111) and by faculty in the College of Biology and Agriculture faculty ( n = 34) based on their perceptions of student confidence. Additionally, student data were collected from classroom observations and interviews. Data analysis showed a disconnect between faculty and students perceptions of confidence for both knowledge and the use of science argumentation. Student reports of their confidence levels regarding various bioethical issues were higher than faculty reports. A further disconnect showed up between students’ preferred learning styles and the general faculty’s common teaching methods; students learned more by practicing scientific argumentation than listening to traditional lectures. Students who completed a bioethics course that included practice in scientific argumentation, significantly increased their confidence levels. This study suggests that professors’ expectations and teaching styles influence student confidence levels in both knowledge and scientific argumentation.

Estimation of the Undiagnosed Intervals of HIV-Infected Individuals by a Modified Back-Calculation Method for Reconstructing the Epidemic Curves.

PubMed

Wong, Ngai Sze; Wong, Ka Hing; Lee, Man Po; Tsang, Owen T Y; Chan, Denise P C; Lee, Shui Shan

2016-01-01

Undiagnosed infections accounted for the hidden proportion of HIV cases that have escaped from public health surveillance. To assess the population risk of HIV transmission, we estimated the undiagnosed interval of each known infection for constructing the HIV incidence curves. We used modified back-calculation methods to estimate the seroconversion year for each diagnosed patient attending any one of the 3 HIV specialist clinics in Hong Kong. Three approaches were used, depending on the adequacy of CD4 data: (A) estimating one's pre-treatment CD4 depletion rate in multilevel model;(B) projecting one's seroconversion year by referencing seroconverters' CD4 depletion rate; or (C) projecting from the distribution of estimated undiagnosed intervals in (B). Factors associated with long undiagnosed interval (>2 years) were examined in univariate analyses. Epidemic curves constructed from estimated seroconversion data were evaluated by modes of transmission. Between 1991 and 2010, a total of 3695 adult HIV patients were diagnosed. The undiagnosed intervals were derived from method (A) (28%), (B) (61%) and (C) (11%) respectively. The intervals ranged from 0 to 10 years, and were shortened from 2001. Heterosexual infection, female, Chinese and age >64 at diagnosis were associated with long undiagnosed interval. Overall, the peaks of the new incidence curves were reached 4-6 years ahead of reported diagnoses, while their contours varied by mode of transmission. Characteristically, the epidemic growth of heterosexual male and female declined after 1998 with slight rebound in 2004-2006, but that of MSM continued to rise after 1998. By determining the time of seroconversion, HIV epidemic curves could be reconstructed from clinical data to better illustrate the trends of new infections. With the increasing coverage of antiretroviral therapy, the undiagnosed interval can add to the measures for assessing HIV transmission risk in the population.

Distinguishing highly confident accurate and inaccurate memory: insights about relevant and irrelevant influences on memory confidence.

PubMed

Chua, Elizabeth F; Hannula, Deborah E; Ranganath, Charan

2012-01-01

It is generally believed that accuracy and confidence in one's memory are related, but there are many instances when they diverge. Accordingly it is important to disentangle the factors that contribute to memory accuracy and confidence, especially those factors that contribute to confidence, but not accuracy. We used eye movements to separately measure fluent cue processing, the target recognition experience, and relative evidence assessment on recognition confidence and accuracy. Eye movements were monitored during a face-scene associative recognition task, in which participants first saw a scene cue, followed by a forced-choice recognition test for the associated face, with confidence ratings. Eye movement indices of the target recognition experience were largely indicative of accuracy, and showed a relationship to confidence for accurate decisions. In contrast, eye movements during the scene cue raised the possibility that more fluent cue processing was related to higher confidence for both accurate and inaccurate recognition decisions. In a second experiment we manipulated cue familiarity, and therefore cue fluency. Participants showed higher confidence for cue-target associations for when the cue was more familiar, especially for incorrect responses. These results suggest that over-reliance on cue familiarity and under-reliance on the target recognition experience may lead to erroneous confidence.

Distinguishing highly confident accurate and inaccurate memory: insights about relevant and irrelevant influences on memory confidence

PubMed Central

Chua, Elizabeth F.; Hannula, Deborah E.; Ranganath, Charan

2012-01-01

It is generally believed that accuracy and confidence in one’s memory are related, but there are many instances when they diverge. Accordingly, it is important to disentangle the factors which contribute to memory accuracy and confidence, especially those factors that contribute to confidence, but not accuracy. We used eye movements to separately measure fluent cue processing, the target recognition experience, and relative evidence assessment on recognition confidence and accuracy. Eye movements were monitored during a face-scene associative recognition task, in which participants first saw a scene cue, followed by a forced-choice recognition test for the associated face, with confidence ratings. Eye movement indices of the target recognition experience were largely indicative of accuracy, and showed a relationship to confidence for accurate decisions. In contrast, eye movements during the scene cue raised the possibility that more fluent cue processing was related to higher confidence for both accurate and inaccurate recognition decisions. In a second experiment, we manipulated cue familiarity, and therefore cue fluency. Participants showed higher confidence for cue-target associations for when the cue was more familiar, especially for incorrect responses. These results suggest that over-reliance on cue familiarity and under-reliance on the target recognition experience may lead to erroneous confidence. PMID:22171810

Peer-driven contraceptive choices and preferences for contraceptive methods among students of tertiary educational institutions in Enugu, Nigeria.

PubMed

Iyoke, Ca; Ezugwu, Fo; Lawani, Ol; Ugwu, Go; Ajah, Lo; Mba, Sg

2014-01-01

To describe the methods preferred for contraception, evaluate preferences and adherence to modern contraceptive methods, and determine the factors associated with contraceptive choices among tertiary students in South East Nigeria. A questionnaire-based cross-sectional study of sexual habits, knowledge of contraceptive methods, and patterns of contraceptive choices among a pooled sample of unmarried students from the three largest tertiary educational institutions in Enugu city, Nigeria was done. Statistical analysis involved descriptive and inferential statistics at the 95% level of confidence. A total of 313 unmarried students were studied (194 males; 119 females). Their mean age was 22.5±5.1 years. Over 98% of males and 85% of females made their contraceptive choices based on information from peers. Preferences for contraceptive methods among female students were 49.2% for traditional methods of contraception, 28% for modern methods, 10% for nonpharmacological agents, and 8% for off-label drugs. Adherence to modern contraceptives among female students was 35%. Among male students, the preference for the male condom was 45.2% and the adherence to condom use was 21.7%. Multivariate analysis showed that receiving information from health personnel/media/workshops (odds ratio 9.54, 95% confidence interval 3.5-26.3), health science-related course of study (odds ratio 3.5, 95% confidence interval 1.3-9.6), and previous sexual exposure prior to university admission (odds ratio 3.48, 95% confidence interval 1.5-8.0) all increased the likelihood of adherence to modern contraceptive methods. An overwhelming reliance on peers for contraceptive information in the context of poor knowledge of modern methods of contraception among young people could have contributed to the low preferences and adherence to modern contraceptive methods among students in tertiary educational institutions. Programs to reduce risky sexual behavior among these students may need to focus on

An Inferential Confidence Interval Method of Establishing Statistical Equivalence that Corrects Tryon's (2001) Reduction Factor

ERIC Educational Resources Information Center

Tryon, Warren W.; Lewis, Charles

2008-01-01

Evidence of group matching frequently takes the form of a nonsignificant test of statistical difference. Theoretical hypotheses of no difference are also tested in this way. These practices are flawed in that null hypothesis statistical testing provides evidence against the null hypothesis and failing to reject H[subscript 0] is not evidence…

A framework for interval-valued information system

NASA Astrophysics Data System (ADS)

Yin, Yunfei; Gong, Guanghong; Han, Liang

2012-09-01

Interval-valued information system is used to transform the conventional dataset into the interval-valued form. To conduct the interval-valued data mining, we conduct two investigations: (1) construct the interval-valued information system, and (2) conduct the interval-valued knowledge discovery. In constructing the interval-valued information system, we first make the paired attributes in the database discovered, and then, make them stored in the neighbour locations in a common database and regard them as 'one' new field. In conducting the interval-valued knowledge discovery, we utilise some related priori knowledge and regard the priori knowledge as the control objectives; and design an approximate closed-loop control mining system. On the implemented experimental platform (prototype), we conduct the corresponding experiments and compare the proposed algorithms with several typical algorithms, such as the Apriori algorithm, the FP-growth algorithm and the CLOSE+ algorithm. The experimental results show that the interval-valued information system method is more effective than the conventional algorithms in discovering interval-valued patterns.

An Efficient Variable Screening Method for Effective Surrogate Models for Reliability-Based Design Optimization

DTIC Science & Technology

2014-04-01

surrogate model generation is difficult for high -dimensional problems, due to the curse of dimensionality. Variable screening methods have been...a variable screening model was developed for the quasi-molecular treatment of ion-atom collision [16]. In engineering, a confidence interval of...for high -level radioactive waste [18]. Moreover, the design sensitivity method can be extended to the variable screening method because vital

Technical Report: Algorithm and Implementation for Quasispecies Abundance Inference with Confidence Intervals from Metagenomic Sequence Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

McLoughlin, Kevin

2016-01-11

This report describes the design and implementation of an algorithm for estimating relative microbial abundances, together with confidence limits, using data from metagenomic DNA sequencing. For the background behind this project and a detailed discussion of our modeling approach for metagenomic data, we refer the reader to our earlier technical report, dated March 4, 2014. Briefly, we described a fully Bayesian generative model for paired-end sequence read data, incorporating the effects of the relative abundances, the distribution of sequence fragment lengths, fragment position bias, sequencing errors and variations between the sampled genomes and the nearest reference genomes. A distinctive featuremore » of our modeling approach is the use of a Chinese restaurant process (CRP) to describe the selection of genomes to be sampled, and thus the relative abundances. The CRP component is desirable for fitting abundances to reads that may map ambiguously to multiple targets, because it naturally leads to sparse solutions that select the best representative from each set of nearly equivalent genomes.« less

Parent's confidence as a caregiver.

PubMed

Raines, Deborah A; Brustad, Judith

2012-06-01

The purpose of this study was to describe the parent's self-reported confidence as a caregiver. The specific research questions were as follows: • What is the parent's perceived level of confidence when performing infant caregiving activities in the neonatal intensive care unit (NICU)? • What is the parent's projected level of confidence about performing infant caregiving activities on the first day at home? Participants were parents of infants with an anticipated discharge date within 5 days. Inclusion criteria were as follows: parent at least 18 years of age, infant's discharge destination is home with the parent, parent will have primary responsibility for the infant after discharge, and the infant's length of stay in the NICU was a minimum of 10 days. Descriptive, survey research. Participants perceived themselves to be confident in all but 2 caregiving activities when caring for their infants in the NICU, but parents projected a change in their level of confidence in their ability to independently complete infant care activities at home. When comparing the self-reported level of confidence in the NICU and the projected level of confidence at home, the levels of confidence decreased for 5 items, increased for 8 items, and remained unchanged for 2 items. All of the items with a decrease in score were the items with the lowest score when performed in the NICU. All of these low-scoring items are caregiving activities that are unique to the post-NICU status of the infant. Interestingly, the parent's projected level of confidence increased for the 8 items focused on handling and interacting with the infant. The findings of this research provide evidence that nurses may need to rethink when parents become active participants in their infant's medical-based caregiving activities.

Effect of interpregnancy interval on risk of spontaneous preterm birth in Emirati women, United Arab Emirates.

PubMed Central

Al-Jasmi, Fatima; Al-Mansoor, Fatima; Alsheiba, Aisha; Carter, Anne O.; Carter, Thomas P.; Hossain, M. Moshaddeque

2002-01-01

OBJECTIVE: To investigate whether a short interpregnancy interval is a risk factor for preterm birth in Emirati women, where there is a wide range of interpregnancy intervals and uniformity in potentially confounding factors. METHODS: A case-control design based on medical records was used. A case was defined as a healthy multiparous Emirati woman delivering a healthy singleton spontaneously before 37 weeks of gestation between 1997 and 2000, and a control was defined as the next eligible similar woman delivering after 37 weeks of gestation. Women were excluded if there was no information available about their most recent previous pregnancy or if it had resulted in a multiple or preterm birth. Data collected from charts and delivery room records were analysed using the STATA statistical package. All variables found to be valid, stable and significant by univariate analysis were included in multivariate logistic regression analysis. FINDINGS: There were 128 cases who met the eligibility criteria; 128 controls were selected. Short interpregnancy intervals were significantly associated with case status (P<0.05). The multivariate adjusted odds ratios for the 1st, 2nd, and 4th quartiles of interpregnancy interval compared with the lowest-risk 3rd quartile were 8.2, 5.4, and 2.0 (95% confidence intervals: 3.5-19.2, 2.4-12.6, and 0.9- 4.5 respectively). CONCLUSION: A short interpregnancy interval is a risk factor for spontaneous preterm birth in Emirati women. The magnitude of the risk and the risk gradient between exposure quartiles suggest that the risk factor is causal and that its modification would reduce the risk of preterm birth. PMID:12481208

Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants

PubMed Central

Cortese, Aurelio; Amano, Kaoru; Koizumi, Ai; Lau, Hakwan; Kawato, Mitsuo

2017-01-01

Neurofeedback studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the multi-voxel pattern decoding approach, allowing for fMRI to serve as a tool to manipulate fine-grained neural activity embedded in voxel patterns. Because of its tremendous potential for clinical applications, certain questions regarding decoded neurofeedback (DecNef) must be addressed. Specifically, can the same participants learn to induce neural patterns in opposite directions in different sessions? If so, how does previous learning affect subsequent induction effectiveness? These questions are critical because neurofeedback effects can last for months, but the short- to mid-term dynamics of such effects are unknown. Here we employed a within-subjects design, where participants underwent two DecNef training sessions to induce behavioural changes of opposing directionality (up or down regulation of perceptual confidence in a visual discrimination task), with the order of training counterbalanced across participants. Behavioral results indicated that the manipulation was strongly influenced by the order and the directionality of neurofeedback training. We applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in confidence, strength of down-regulation of confidence relative to up-regulation, maintenance of learning effects, and anterograde learning interference. Modeling results revealed that DecNef successfully induced bidirectional confidence changes in different sessions within single participants. Furthermore, the effect of up- compared to down-regulation was more prominent, and confidence changes (regardless of the direction) were largely preserved even after a week-long interval. Lastly, the effect of the second session was markedly diminished as compared to the effect of the first session, indicating strong anterograde learning interference. These results are interpreted in the framework

Association between GFR Estimated by Multiple Methods at Dialysis Commencement and Patient Survival

PubMed Central

Wong, Muh Geot; Pollock, Carol A.; Cooper, Bruce A.; Branley, Pauline; Collins, John F.; Craig, Jonathan C.; Kesselhut, Joan; Luxton, Grant; Pilmore, Andrew; Harris, David C.

2014-01-01

Summary Background and objectives The Initiating Dialysis Early and Late study showed that planned early or late initiation of dialysis, based on the Cockcroft and Gault estimation of GFR, was associated with identical clinical outcomes. This study examined the association of all-cause mortality with estimated GFR at dialysis commencement, which was determined using multiple formulas. Design, setting, participants, & measurements Initiating Dialysis Early and Late trial participants were stratified into tertiles according to the estimated GFR measured by Cockcroft and Gault, Modification of Diet in Renal Disease, or Chronic Kidney Disease-Epidemiology Collaboration formula at dialysis commencement. Patient survival was determined using multivariable Cox proportional hazards model regression. Results Only Initiating Dialysis Early and Late trial participants who commenced on dialysis were included in this study (n=768). A total of 275 patients died during the study. After adjustment for age, sex, racial origin, body mass index, diabetes, and cardiovascular disease, no significant differences in survival were observed between estimated GFR tertiles determined by Cockcroft and Gault (lowest tertile adjusted hazard ratio, 1.11; 95% confidence interval, 0.82 to 1.49; middle tertile hazard ratio, 1.29; 95% confidence interval, 0.96 to 1.74; highest tertile reference), Modification of Diet in Renal Disease (lowest tertile hazard ratio, 0.88; 95% confidence interval, 0.63 to 1.24; middle tertile hazard ratio, 1.20; 95% confidence interval, 0.90 to 1.61; highest tertile reference), and Chronic Kidney Disease-Epidemiology Collaboration equations (lowest tertile hazard ratio, 0.93; 95% confidence interval, 0.67 to 1.27; middle tertile hazard ratio, 1.15; 95% confidence interval, 0.86 to 1.54; highest tertile reference). Conclusion Estimated GFR at dialysis commencement was not significantly associated with patient survival, regardless of the formula used. However, a

A new method of sweat testing: the CF Quantum®sweat test.

PubMed

Rock, Michael J; Makholm, Linda; Eickhoff, Jens

2014-09-01

Conventional methods of sweat testing are time consuming and have many steps that can and do lead to errors. This study compares conventional sweat testing to a new quantitative method, the CF Quantum® (CFQT) sweat test. This study tests the diagnostic accuracy and analytic validity of the CFQT. Previously diagnosed CF patients and patients who required a sweat test for clinical indications were invited to have the CFQT test performed. Both conventional sweat testing and the CFQT were performed bilaterally on the same day. Pairs of data from each test are plotted as a correlation graph and Bland-Altman plot. Sensitivity and specificity were calculated as well as the means and coefficient of variation by test and by extremity. After completing the study, subjects or their parents were asked for their preference of the CFQT and conventional sweat testing. The correlation coefficient between the CFQT and conventional sweat testing was 0.98 (95% confidence interval: 0.97-0.99). The sensitivity and specificity of the CFQT in diagnosing CF was 100% (95% confidence interval: 94-100%) and 96% (95% confidence interval: 89-99%), respectively. In one center in this three center multicenter study, there were higher sweat chloride values in patients with CF and also more tests that were invalid due to discrepant values between the two extremities. The percentage of invalid tests was higher in the CFQT method (16.5%) compared to conventional sweat testing (3.8%) (p < 0.001). In the post-test questionnaire, 88% of subjects/parents preferred the CFQT test. The CFQT is a fast and simple method of quantitative sweat chloride determination. This technology requires further refinement to improve the analytic accuracy at higher sweat chloride values and to decrease the number of invalid tests. Copyright © 2014 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.