Magnitude Of Measurement Error Research Articles

Quantifying the Impact of Measurement Errors in Consistent Linear Partial Least Squares Structural Equation Modelling: A Monte Carlo Investigation

Measurement errors wield the potential to introduce uncertainties and inaccuracies, casting shadows on data quality and jeopardizing the integrity of structural relationships. Notably robust against measurement errors, Partial Least Squares Structural Equation Modelling (PLS-SEM) has historically maintained a reputation for resilience. However, recent insights have unveiled its susceptibility to these errors, instigating a revaluation of its standing in the Structural Equation Modelling landscape. Overlooking measurement errors in PLS-SEM carry consequential repercussions, notably tainting the accuracy of structural relationships and introducing bias. This effect becomes particularly pronounced when dealing with an insufficient understanding of the intricate structural dynamics. Unfortunately, PLS-SEM currently lacks an all-encompassing remedy to address this concern. Consequently, the quantification of measurement errors impact in PLS-SEM gains paramount importance, fostering a demand for innovative strategies to propel its effectiveness forward. Notably, contemporary investigations have unmasked PLS-SEM's vulnerability to non-orthogonal errors. This revelation challenges the notion of its imperviousness to the detrimental influence of measurement errors, necessitating a comprehensive evaluation of its performance under such conditions. This study leveraged simulated data to extract empirical findings and employed parameters biasedness analysis. This analysis led to the determination that the stability of the PLS-SEM algorithm is compromised when exposed to diverse measurement error scenarios. Consequently, the outcomes generated exhibit both instability and bias. This bias becomes increasingly conspicuous as the magnitude of measurement errors intensifies. Thus, the study proposes avenues for elevating the robustness of PLS-SEM.

Consumer price measurement under the first wave of the COVID-19 spread in Japan: Scanner data evidence for retailers in Tokyo

In this paper, we examine the CPI (consumer price index) measurement errors under the first wave of the COVID-19 spread in Japan. To address this question, we construct high-frequency quality-adjusted price indices by employing daily scanner data from retail stores in Tokyo. We demonstrate the importance of using price data with the wide-ranging coverage of products and outlets by making explicit adjustments for temporary sales effects and retail service quality in examining the retail price dynamics under the COVID-19 pandemic as the voluntary lockdown constrained household purchasing behavior. Note that the sources of the CPI measurement errors under the COVID-19 pandemic differ significantly from those in the US, observed as wide-ranging and long-lasting stockouts. We show that downward bias, not upward bias generally advocated, was observed during the first wave of the COVID-19 spread in Japan. The magnitude of the downward bias is estimated at from −0.6 to −0.3 points on the CPI for food less perishables and eating out on the basis of cumulative changes from January 2020 to June. The contribution of the estimates to the overall CPI is −0.3% to −0.15% points on an annualized basis, considering that the estimation covers half-year and the weights are about a quarter of the overall CPI. The magnitude of measurement errors is deemed limited, and the overall trend of the CPI remains unchanged even after incorporating the estimated downward bias. It should be noted that this downward bias arises mainly from the “one-specification-for-one-item” policy by weakening the price representativeness in the Japanese CPI.

Realistic simulation of time-course measurements in systems biology.

In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.

Effects of pelvic obliquity and limb position on radiographic leg length discrepancy measurement: a Sawbones model

PurposePotential sources of inaccuracy in leg length discrepancy (LLD) measurements commonly arise due to postural malalignment during radiograph acquisition. Preoperative planning techniques for total hip arthroplasty (THA) are particularly susceptible to this inaccuracy, as they often rely solely on radiographic assessments. Owing to the extensive variety of pathologies that are associated with LLD, an understanding of the influence of malpositioning on LLD measurement is crucial. In the present study, we sought to characterize the effects of varying degrees of lateral pelvic obliquity (PO) and mediolateral limb movement in the coronal plane on LLD measurement error (ME).MethodsA 3-D sawbones model of the pelvis with bilateral femurs of equal-length was assembled. Anteroposterior pelvic radiographs were captured at various levels of PO: 0°, 5°, 10°, and 15°. At each level of PO, femurs were individually rotated medio-laterally to produce 0°, 5°, 10°, and 15° of abduction/adduction. LLD was measured radiographically at each position combination. For all cases of PO, the right-side of the pelvis was designated as the higher-side, and the left as the lower-side.ResultsAt 0° PO, 71% of tested variations in femoral abduction/adduction resulted in LLD ME < 0.5-cm, while 29% were ≥ 0.5-cm, but < 1-cm. ME increased progressively as one limb was further abducted while the contralateral limb was simultaneously further adducted. The highest ME occurred with one femur abducted 15° and the other adducted 15°. Similar magnitudes of ME were seen in 98% of tested femoral positions at 5° of PO. The greatest ME (~ 1 cm) occurred at the extremes of right-femur abduction and left-femur adduction. At 10° of PO, a higher prevalence of cases exhibited LLD ME > 0.5-cm (39%) and ≥ 1-cm (8%). The greatest errors occurred at femoral positions similar to those seen at 5° of PO. At 15° of PO, half of tested variations in femoral position resulted in LLD ME > 1-cm, while 22% of cases produced errors > 1.5-cm. These clinically significant errors occurred at all tested variations of right-femur abduction, with the left-femur in either neutral position, abduction, or adduction.ConclusionThis study aids surgeons in understanding the magnitude of radiographic LLD ME produced by varying degrees of PO and femoral abduction/adduction. At a PO of ≤5°, variations in femoral abduction/adduction of up to 15° produce errors of marginal clinical significance. At PO of 10° or 15°, even small changes in mediolateral limb position led to clinically significant ME (> 1-cm). This study also highlights the importance of proper patient positioning during radiograph acquisition, demonstrating the need for surgeons to assess the quality of their radiographs before performing preoperative templating for THA, and accounting for PO (> 5°) when considering the validity of LLD measurements.

A phenomenological approach to predicting tuberculosis cases with an assessment of measurement errors

BackgroundMeasurement errors produce bias and uncertainty. They affect the outcomes of data-fitted models. Unfortunately, tuberculosis incidence data providers do not appear to be interested in this topic. MethodWe use a phenomenological approach to describe and forecast tuberculosis incidence numbers in the US, and England and Wales as a function of time. We use a heuristic method to evaluate if the lack of fit of our descriptive models to the data could be explained by measurement errors. FindingsWe find that if the lack of fit of our proposed models to the data is due to measurement errors, these are not so large as to make the models useless. We find numerical regularities that provide trustworthy tools to make a description and forecast the tuberculosis incidence trends. InterpretationMeasurement errors of epidemiological data collected "in the field" are probably large and could justify the lack of fit of an epidemic model to the data. Thus, to choose an appropriate model to describe an epidemic, we propose to assess the magnitude of measurement errors and apply the epidemic theory.

Assessing the Impact of Equating Error on Group Means and Group Mean Differences

AbstractEquating error is usually small relative to the magnitude of measurement error, but it could be one of the major sources of error contributing to mean scores of large groups in educational measurement, such as the year‐to‐year state mean score fluctuations. Though testing programs may routinely calculate the standard error of equating (SEE), the commonly used summary statistics of SEE are not direct quantifications of the impact of equating error on group means. This article proposed summary statistics that directly quantify the impact of equating error on group means or group mean differences and provided empirical and analytical methods to estimate these statistics. Examples based on empirical data were used to illustrate practical applications of these statistics.

The effect of process design on refiner pulp quality control performance

Abstract In this study, the effect of process- and online analyser configuration on pulp quality control is explored. The following parameters were included: analyser sampling interval, time delay, measurement error magnitude, and latency chest residence time. Using different values of parameters in a process model, a range of configurations were constructed. For each configuration, the achievable control performance was evaluated using an optimization approach. PI controller settings were chosen based on minimization of the integrated absolute error (IAE) in pulp quality after an input step disturbance. The results show that reducing the sampling interval improves performance also when the interval is smaller than the chest residence time or the analyser delay. Moreover, reducing the chest residence time can reduce the IAE by up to 40 %. However, reducing the residence time to lower than 1/3 of the sampling interval does not improve performance. Further improvement is possible if the analyser delay is reduced. The compromise between reducing the IAE and avoiding creating variation by acting on measurement error has a strong influence on the results. In conclusion, pulp quality control performance can be improved significantly by making changes to the studied configuration parameters.

Estimating the Precision of Quantitative Imaging Biomarkers without Test-Retest Studies

A critical performance metric for any quantitative imaging biomarker is its ability to reliably generate similar values on repeat testing. This is known as the repeatability of the biomarker, and it is used to determine the minimum detectable change needed in order to show that a change over time is real change and not just due to measurement error. Test-retest studies are the classic approach for estimating repeatability; however, these studies can be infeasible when the imaging is expensive, time-consuming, invasive, or requires contrast agents. The objective of this study was to develop and test a method for estimating repeatability without a test-retest study. We present a statistical method for estimating repeatability and testing whether an imaging method meets a specified criterion for repeatability in the absence of a test-retest study. The new method is applicable for the particular situation where a reference standard is available. A Monte Carlo simulation study was conducted to evaluate the performance of the new method. The proposed estimator is unbiased, and hypothesis tests with the new estimator have nominal type I error rate and power similar to a test-retest study. We considered the situation where the reference standard provides the true value, as well as when the reference standard itself has various magnitudes of measurement error. An example from CT imaging biomarkers of atherosclerosis illustrates the new method. Precision of a QIB can be measured without a test-retest study in the situation where a reference standard is available.

Reliability of ActiGraph GT3X+ placement location in the estimation of energy expenditure during moderate and high-intensity physical activities in young and older adults

ABSTRACT WHO defines physical activity (PA) as any bodily movement produced by skeletal muscles that requires energy expenditure (EE). The purpose of this study was to compare the EE estimations by ActiGraph GT3X+ with a gold standard measurement, the portable gas analyser in a set of 3 different PAs. This cross-sectional study involved 56 participants, age range (years, [min, max]: young people [20, 33], older adults [65, 83]). Participants completed a single session of three experimental PAs including biking, treadmill walking, and treadmill running. Each participant wore five GT3X+ triaxial accelerometers and a portable gas analyser used as the gold standard measurement. The GT3X+ were placed on the wrists, the waist (centred at the pelvis), and the ankles. ActiGraph GT3X+ and MetaMax3B records were investigated through intraclass correlation coefficient. Magnitude of measurement error was estimated using Effect Size. The GT3X+ wrist and GT3X+ waist underestimated EE regardless of the PA type. The GT3X+ ankles strongly overestimated EE during biking (mean bias = 489 ± 392%) and walking (mean bias = 106 ± 58%), while it underestimated EE during running (mean bias = −47 ± 27%). The ActiGraph GT3X+ does not provide accurate EE estimates across a range of placement locations during moderate and high-intensity PA.

A Method to adjust for measurement error in multiple exposure variables measured with correlated errors in the absence of an internal validation study

Difficulty in obtaining the correct measurement for an individual’s longterm exposure is a major challenge in epidemiological studies that investigate the association between exposures and health outcomes. Measurement error in an exposure biases the association between the exposure and a disease outcome. Usually, an internal validation study is required to adjust for exposure measurement error; it is challenging if such a study is not available. We propose a general method for adjusting for measurement error where multiple exposures are measured with correlated errors (a multivariate method) and illustrate the method using real data. We compare the results from the multivariate method with those obtained using a method that ignores measurement error (the naive method) and a method that ignores correlations between the errors and true exposures (the univariate method). It is found that ignoring measurement error leads to bias and underestimates the standard error. A sensitivity analysis shows that the magnitude of adjustment in the multivariate method is sensitive to the magnitude of measurement error, sign, and the correlation between the errors. We conclude that the multivariate method can be used to adjust for bias in the outcome-exposure association in a case where multiple exposures are measured with correlated errors in the absence of an internal validation study. The method is also useful in conducting a sensitivity analysis on the magnitude of measurement error and the sign of the error correlation.

Wood volume errors from measured and predicted heights

Many volume functions use diameter and height as input variables. Measurement and prediction errors in heights propagate into the error of the wood volume estimate for a tree or a plot. We quantified these errors (and bias) with census data of species, heights, and stem diameters in a 1.44 ha hardwood stand in Lower Saxony (GER) dominated by beech (Fagus sylvatica L.) and with an understorey of hardwood species. We simulated simple random sampling with fixed area and variable radius plots and distorted the actual height measurements with three magnitudes of random Gaussian errors. The inventory protocol for height measurement results in a selection of three to four trees per plot, while heights for the remaining trees were predicted via two alternative models. Tree and plot level RMSE% of wood volume with a mix of measured and predicted tree heights were—across the three magnitudes of measurement errors, 18–81% greater than root-mean-squared errors obtained when all trees were measured for heights with errors. The results from this study contribute the statistical side of recommendations on the number of height measurements per forest inventory plot, where inferences on optimal numbers of height measurements will also depend on the costs.

Maximum Likelihood and Bayesian Estimation of Repeatability Index: Application of Estimating Ratio of Variance Components

An index of repeatability is constructed to evaluate the relative magnitude of measurement error. This index is constructed as a ratio of two variance components. Estimation of the index is derived under the one-way random effects model. We compare the well-known maximum likelihood estimator to the Bayesian estimation procedure using non-informative prior. Large sample variance of the of the maximum likelihood estimator are obtained using the inverse of Fisher’s information matrix and the delta method. Inference procedure using the. We also construct a test statistic on the equality of two repeatability indices using the Monte Carlo integration and sampling Importance re-sampling method. We illustrate the methodologies on the estimation of the index of repeatability of Gamma-glutamyl-transferase, an enzyme found in many organs all over the human body, with the highest concentrations found in the liver. This enzyme’s level is raised in the blood in most diseases that cause damage to the liver or bile ducts and is considered an essential serum marker for alcohol-related liver disease.

Reflection on modern methods: five myths about measurement error in epidemiological research.

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study’s inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

A method of determining geometry of cone beam CT scanner

The task of determining the geometry of a cone-beam CT scanner with flat panel detector and circular/spiral source trajectory is considered. Accomplishing this task implies analyzing projections of a set of points referred to as calibrating set or calibrating phantom. We take advantage of the fact that observed coordinates of a point’s projection are rational functions of the point’s location. Unknown coefficients of these functions can be recovered exactly from six projections of the point. Location of the source as well as position and orientation of the detector are determined in the scanner reference frame, which is constituted by rotation axis and central plane of the scanner. Two different projections of a calibrating set are enough to solve the task if the source trajectory is a circle. In applications where a shift of an object transversally to the central plane is required, two additional projections have to be collected in order to identify the direction of the shift. The developed formalism becomes especially simple when the detector is aligned with the rotation axis. In this case four projections of a single calibrating point rotated successfully about the rotation axis are sufficient. The error analysis carried out in the paper shows that the magnitude of deviation from the true values is of the order of the magnitude of measurement errors.

Applications of Measurement Error Correction Approaches in Statistical Road Safety Modeling

Road safety modelers frequently use average annual daily traffic (AADT) as a measure of exposure in regression models of expected crash frequency for road segments and intersections. Recorded AADT values at most locations are estimated by state and local transportation agencies with significant uncertainty, often by extrapolating short-term traffic counts over time and space. This uncertainty in the traffic volume estimates, often termed in a modeling context as measurement error in right-hand-side variables, can have serious effects on model estimation, including: 1) biased regression coefficient estimates; and 2) increases in dispersion. The structure and magnitude of measurement error in AADT estimates are not clearly understood by researchers or practitioners, leading to difficulties in explicitly accounting for this error in statistical road safety models, and ultimately in finding solutions for its correction. This study explores the impacts of measurement error in traffic volume estimates on statistical road safety models by employing measurement error correction approaches, including regression calibration and simulation extrapolation. The concept is demonstrated using crash, traffic, and roadway data from rural, two-lane horizontal curves in the State of Washington. The overall results show that the regression coefficient estimates with a positive coefficient were larger and those with a negative coefficient were smaller (i.e., more negative) when the measurement error correction methods were applied to the regression models of expected crash frequency. Future directions in applications of measurement error correction approaches to road safety research are provided.

UAV Landing Based on the Optical Flow Videonavigation.

An automatic landing of an unmanned aerial vehicle (UAV) is a non-trivial task requiring a solution of a variety of technical and computational problems. The most important is the precise determination of altitude, especially at the final stage of approaching to the earth. With current altimeters, the magnitude of measurement errors at the final phase of the descent may be unacceptably high for constructing an algorithm for controlling the landing manoeuvre. Therefore, it is desirable to have an additional sensor, which makes possible to estimate the height above the surface of the runway. It is possible to estimate all linear and angular UAV velocities simultaneously with the help of so-called optical flow (OF), determined by the sequence of images recorded by an onboard camera, however in pixel scale. To transform them into the real metrical values it is necessary to know the current flight altitude and the camera angular position values. The critical feature of the OF is its susceptibility to the camera resolution and the shift rate of the observed scene. During the descent phase of flight, these parameters change at least one hundred times together with the altitude. Therefore, for reliable application of the OF one needs to coordinate the shooting parameters with the current altitude. However, in case of the altimeter fault presence, the altitude is also still to be estimated with the aid of the OF, so one needs to have another tool for the camera control. One of the possible and straightforward ways is the camera resolution change by pixels averaging in computer part which performed in coordination with theoretically estimated and measured OF velocity. The article presents results of such algorithms testing from real video sequences obtained in flights with different approaches to the runway with simultaneous recording of telemetry and video data.

On the Index of Repeatability: Estimation and Sample Size Requirements

Background: Repeatability is a statement on the magnitude of measurement error. When biomarkers are used for disease diagnoses, they should be measured accurately. Objectives: We derive an index of repeatability based on the ratio of two variance components. Estimation of the index is derived from the one-way Analysis of Variance table based on the one-way random effects model. We estimate the large sample variance of the estimator and assess its adequacy using bootstrap methods. An important requirement for valid estimation of repeatability is the availability of multiple observations on each subject taken by the same rater and under the same conditions. Methods: We use the delta method to derive the large sample variance of the estimate of repeatability index. The question related to the number of required repeats per subjects is answered by two methods. In first methods we estimate the number of repeats that minimizes the variance of the estimated repeatability index, and the second determine the number of repeats needed under cost-constraints. Results and Novel Contribution: The situation when the measurements do not follow Gaussian distribution will be dealt with. It is shown that the required sample size is quite sensitive to the relative cost. We illustrate the methodologies on the Serum Alanine-aminotransferase (ALT) available from hospital registry data for samples of males and females. Repeatability is higher among females in comparison to males.

Differential self-report error by socioeconomic status in hypertension and hypercholesterolemia: INSEF 2015 study.

This study aimed to compare self-reported and examination-based prevalence of hypertension and hypercholesterolemia in Portugal in 2015 and to identify factors associated with the measurement error in self-reports. We used data from the Portuguese National Health Examination Survey (n = 4911), that combines personal interview, blood collection and, physical examination. Sensitivity and specificity of self-reported hypertension and hypercholesterolemia were calculated. Poisson regression was used to estimate prevalence ratios (PRs) of underreport of hypertension and hypercholesterolemia according to sex, age, socioeconomic status (education and income) and general practitioner (GP) consultation in the past year. Sensitivity of self-reports was 69.8% for hypertension and 38.2% for hypercholesterolemia. Underreport of hypertension was associated with male gender (PR = 1.54), lack of GP consultation (PR = 1.70) and being 25-44 years old (PR = 2.45) or 45-54 years old (PR = 2.37). Underreport of hypercholesterolemia was associated with lack of GP consultation (PR = 1.15), younger age (PR = 1.83 for 25-44 age group and PR = 1.52 for 45-54 age group), secondary (PR = 1.30) and higher (PR = 1.27) education. Self-reported data underestimate prevalence of hypertension and hypercholesterolemia. Magnitude of measurement error in self-reports varies by health conditions and population characteristics. Adding objective measurements to self-reported questionnaires improve data accuracy allowing better understanding of socioeconomic inequalities in health.

Measurement error and misclassification in electronic medical records: methods to mitigate bias.

We sought to: 1) examine common sources of measurement error in research using data from electronic medical records (EMR), 2) discuss methods to assess the extent and type of measurement error, and 3) describe recent developments in methods to address this source of bias. We identified eight sources of measurement error frequently encountered in EMR studies, the most prominent being that EMR data usually reflect only the health services and medications delivered within the specific health facility/system contributing to the EMR data. Methods for assessing measurement error in EMR data usually require gold standard or validation data, which may be possible using data linkage. Recent methodological developments to address the impact of measurement error in EMR analyses were particularly rich in the multiple imputation literature. Presently, sources of measurement error impacting EMR studies are still being elucidated, as are methods for assessing and addressing them. Given the magnitude of measurement error that has been reported, investigators are urged to carefully evaluate and rigorously address this potential source of bias in studies based in EMR data.

Paulik revisited: Statistical framework and estimation performance of multistage recruitment functions

Multiple processes act at different stages and different intensities within the timeline between spawning and the age designated as “recruitment”. However, common practice is to model only a single step between spawning stock and recruits. Reasons for this practice include lack of data on the intermediate stages, lack of understanding of the mechanisms and functional form governing intermediate stages, and lack of computational resources to model a multistage process in the appropriate statistical framework. We develop a state-space framework and, using a simulation study, we explore the estimation of multistage stock-recruit functions. We evaluated four different functions (Ricker, Beverton-Holt, Shepherd, and Generalized), and examined the effects on estimation of several factors, including the form of density dependence, the magnitude of measurement error associated with each stage, type of prior on measurement error, and the magnitude of process error between stages. Three-parameter stock-recruit functions (Shepherd, Generalized) correctly identified the form of density dependence in each stage, although the Shepherd model exhibited problems with convergence. Model misspecification resulted in bias, especially in parameters specifying measurement and process error; an informative prior on measurement error improved precision and bias. The Deviance Information Criterion selected the Ricker model too often, even when the true model was Beverton-Holt. Sequential density-dependent stages, even multiple overcompensatory stages, lead to an overall function that appears fairly flat, suggesting that a function capable of producing asymptotic dynamics is a practical default. The common practice of bypassing stages between the first (spawning stock) and the last (recruits) worked reasonably well, except when fitting a Ricker model, most likely because the true function was nearly flat over most of the range of the first stage. An application to data on North Sea herring illustrates that a multi-stage stock-recruit model can generate a stock-recruit function that is intermediate between Ricker and Beverton-Holt models, and that does not match existing three-parameter forms.