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The Net Promotor Score (NPS) is ubiquitous, relying on a single-item question to capture consumers' word-of-mouth (WOM). The question asks consumers for their likelihood of recommending a brand to friends and colleagues. Despite its popularity and advantages over longer satisfaction surveys, NPS has potential weaknesses. Among them are that the NPS question (1) is double-barreled by asking in a single question for likelihood to recommend to friends and likelihood to recommend to colleagues, (2) focuses on recommendations, and thus, ignores consumers' likelihood to spread negative WOM, and (3) ignores online WOM, which often involves recommendations to strangers rather than friends or colleagues. This paper empirically tests these three potential weaknesses of the NPS measure on the WOM conclusions derived from NPS. Specifically, three experiments vary whether NPS assesses likelihood to recommend to a friend and colleague in a single question (how NPS is currently measured) or in two separate questions. In addition, NPS is compared to responses to an explicit negative WOM question (intent to warn others about the brand). Moreover, across studies, the NPS is reported for a recent positive experience and either a recent negative experience or a recent mixed experience. NPS is also compared to likelihood to engage in online WOM in terms of posting an online review and the intended online rating. By examining these issues, this research sheds light on consumers' interpretations of NPS, the factors that influence these interpretations, and how these factors affect NPS' ability to predict negative WOM, online WOM, as well as satisfaction, loyal behavior, and WOM in general. [ABSTRACT FROM AUTHOR]

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BACKGROUND: Social desirability can negatively affect the validity of self-reported measures, including underreporting of stigmatized behaviors like alcohol consumption. The Marlowe-Crowne Social Desirability Scale (SDS) is widely implemented and comprised of Denial and Attribution Domains (i.e., tendencies to deny undesirable traits or attribute socially desirable traits to oneself, respectively). Yet, limited psychometric research has been conducted in sub-Saharan Africa, where the prevalence of unhealthy alcohol consumption is high as well as religiosity and hierarchical social norms. To address this gap, we (a) conducted an exploratory study assessing certain psychometric properties of the 28-item SDS (Runyankole-translated) among persons with HIV (PWH) in Uganda, and (b) examined the relationship between social desirability and self-reported alcohol use. METHODS: We pooled baseline data (N = 1153) from three studies of PWH engaged in alcohol use from 2017 to 2021. We assessed the translated scales construct validity (via confirmatory factor analysis), internal consistency, item performance, differential item functioning by gender, concurrent validity with the DUREL religiosity index domains, and the association between social desirability and self-reported alcohol use. RESULTS: Participants had a mean age of 40.42 years, 63% were men, and 91% had an undetectable HIV viral load. The 28-item SDS had satisfactory construct validity (Model fit indices: RMSEA = 0.07, CFI = 0.84, TLI = 0.82) and internal consistency (Denial Domain ΩTotal = 0.82, Attribution Domain ΩTotal = 0.69). We excluded Item 14 (I never hesitate to help someone in trouble) from the Attribution Domain, which mitigated differential measurement error by gender and slightly improved the construct validity (Model fit indices: RMSEA = 0.06, CFI = 0.86, TLI = 0.85) and reliability (Attribution Domain ΩTotal = 0.72) of the 27-item modified SDS. Using the 27-item SDS, we found that social desirability was weakly correlated with religiosity and inversely associated with self-reported alcohol use after adjusting for biomarker-measured alcohol use and other confounders (β = -0.05, 95% confidence interval: -0.09 to -0.01, p-value = 0.03). CONCLUSIONS: We detected and mitigated measurement error in the 28-item Runyankole-translated SDS, and found that the modified 27-item scale had satisfactory construct validity and internal consistency in our sample. Future studies should continue to evaluate the psychometric properties of the Runyankole-translated SDS, including retranslating Item 14 and reevaluating its performance.

Health agencies rely upon survey-based physical measures to estimate the prevalence of key global health indicators such as hypertension. Such measures are usually collected by nonhealthcare worker personnel and are potentially subject to measurement error due to variations in interviewer technique and setting, termed interviewer effects. In the context of physical measurements, particularly in low- and middle-income countries, interviewer-induced biases have not yet been examined. Using blood pressure as a case study, we aimed to determine the relative contribution of interviewer effects on the total variance of blood pressure measurements in three large nationally representative health surveys from the Global South. We utilized 169,681 observations between 2008 and 2019 from three health surveys (Indonesia Family Life Survey, National Income Dynamics Study of South Africa, and Longitudinal Aging Study in India). In a linear mixed model, we modeled systolic blood pressure as a continuous dependent variable and interviewer effects as random effects alongside individual factors as covariates. To quantify the interviewer effect-induced uncertainty in hypertension prevalence, we utilized a bootstrap approach comparing subsamples of observed blood pressure measurements to their adjusted counterparts. Our analysis revealed that the proportion of variation contributed by interviewers to blood pressure measurements was statistically significant but small: ∼0.24--2.2% depending on the cohort. Thus, hypertension prevalence estimates were not substantially impacted at national scales. However, individual extreme interviewers could account for measurement divergences as high as 12%. Thus, highly biased interviewers could have important impacts on hypertension estimates at the subdistrict level.

Ubiquitous mobile phone ownership makes phone surveying an attractive method of low-cost data collection. We explore differences between in-person and phone survey measures of agricultural production collected for an impact evaluation in India. Phone responses have greater mean and variance, a difference that persists even within a subset of respondents that answered the same question over both modes. Treatment effect estimation remains stable across survey mode, but estimates are less precise when using phone data. These patterns are informative for cost and sample size considerations in study design and for aggregating evidence across study sites or time periods.

Background: The current practice in guideline development is to use the control group event rate (CR) as a surrogate of baseline risk and to assume portability of the relative treatment effect across populations with low, moderate and high baseline risk. We sought to emulate this practice in a very large sample of meta-analyses. Methods: We retrieved data from all meta-analyses published in the Cochrane Database of Systematic Reviews (2003–2020) that evaluated a binary outcome, reported 2 × 2 data for each individual study and included at least 4 studies. We excluded studies with no events. We conducted meta-analyses with odds ratios and relative risks and performed subgroup analyses based on tertiles of CR. In sensitivity analyses, we evaluated the use of total event rate (TR) instead of CR and using quartiles instead of tertiles. Results: The analysis included 2,531 systematic reviews (27,692 meta-analyses, 226,975 studies, 25,669,783 patients).The percentages of meta-analyses with statistically significant interaction (P < 0.05) based on CR tertile or quartile ranged 12–18% across various sensitivity analyses. This percentage increased as the number of studies or range of CR per meta-analysis increased, reflecting increased power of the subgroup test. The percentages of meta-analyses with statistically significant interaction (P < 0.05) with TR quantiles were lower than those with CR but remained higher than expected by chance. Conclusion: This analysis suggests that when CR or TR are used as surrogates for baseline risk, relative treatment effects may not be portable across populations with varying baseline risks in many meta-analyses. Categroization of the continuous CR variable and not addressing measurement error limit inferences from such analyses and imply that CR is an undesirable source for baseline risk. Guideline developers and decision-makers should be provided with relative and absolute treatment effects that are conditioned on the baseline risk or derived from studies with similar baseline risk to their target populations. [ABSTRACT FROM AUTHOR]

The application of high‐frequency radar as an instrument for assimilating tsunami‐induced current fields is garnering increasing interest. The performance of surface current velocity measurements depends on the azimuthal differences between the crossing radar beams at the measurement points. This study aimed to incorporate the measurement error distributions of the east‐west and north‐south velocity components into tsunami data assimilation based on an optimal interpolation method, assuming Gaussian noise with the time‐invariant and a uniform standard deviation (STD = 5 cm/s) of radial velocity measurements. Through the empirical orthogonal function (EOF) analysis of radar‐derived surface currents in the Kii Channel, Japan, the velocities reconstructed using higher modes (EOFs 16–274) were associated with measurement errors, portraying nonuniform distribution depending on the crossing beam angle of two radar beams. Based on independent fifteen‐time assimilation experiments for two different tsunami scenarios, for a uniform water depth of 500 m, we observed a significant improvement of up to 29% and 0.9% in the assimilation performance (on average) over the along‐coast stations for scenarios with 1‐ and 5‐m maximum initial sea surface heights, respectively. The measurement errors dependent on the crossing beam angle reduced the error‐induced tsunamis, resulting in stable assimilations, with lower STDs in the fifteen‐time assimilation performances. When the STD of Gaussian noise varies with time, it is important to consider the temporal change in the radial velocity measurement errors and/or noise‐filtering techniques, to maintain a certain level of noise intensity. Plain Language Summary: High‐frequency radar is valuable for monitoring tsunamis as it measures tsunami‐induced surface current velocities, not the tsunami height, which is crucial for tsunami evacuation. In this study, we estimated a reasonable tsunami wave height using a data assimilation method incorporating high‐frequency radar measurements with tsunami numerical simulation. To minimize the adverse effects of high‐frequency radar measurement errors and obtain accurate tsunami wave heights, it is essential to estimate the errors appropriately, especially considering their dependency on the radar beam angle. We estimated the beam angle‐dependent errors through a principal component analysis and integrated them into the assimilation process. By doing so, we successfully reproduced the tsunami wave heights, demonstrating an improvement over estimations made under the assumption of constant errors and mitigating the negative impact of measurement errors. Key Points: Measurements of radar‐derived surface current velocities are influenced by the azimuthal difference between crossing radar beamsWe applied the measurement error distribution to tsunami data assimilationThe results suggest that the consideration of measurement error distribution is crucial for improved assimilation performance [ABSTRACT FROM AUTHOR]

This paper investigates the effect of Supplemental Nutrition Assistance Program (SNAP) benefit disbursement on intramonthly household level purchases made from a supermarket retailer. We find that spending, the likelihood of shopping, the bulk expenditure share and the national brand expenditure share increase by $2, 1.5, 2, and 0.6% points, respectively, on the day that SNAP benefits are disbursed. We also compare and contrast estimates that use variation in the indicator for benefit receipt to estimates that utilize variation in the probability of SNAP benefit receipt. We find substantial differences between the two approaches for the outcome of spending. [ABSTRACT FROM AUTHOR]

Introduction: While maximum strength diagnostics are applied in several sports and rehabilitative settings, dynamic strength capacity has been determined via the one-repetition maximum (1RM) testing for decades. Because the literature concerned several limitations, such as injury risk and limited practical applicability in large populations (e.g., athletic training groups), the strength prediction via the velocity profile has received increasing attention recently. Referring to relative reliability coefficients and inappropriate interpretation of agreement statistics, several previous recommendations neglected systematic and random measurement bias. Methods: This article explored the random measurement error arising from repeated testing (repeatability) and the agreement between two common sensors (vMaxPro and TENDO) within one repetition, using minimal velocity thresholds as well as the velocity = 0 m/s method. Furthermore, agreement analyses were applied to the estimated and measured 1RM in 25 young elite male soccer athletes. Results: The results reported repeatability values with an intraclass correlation coefficient (ICC) = 0.66-0.80, which was accompanied by mean absolute (percentage) errors (MAE and MAPE) of up to 0.04-0.22 m/s and =7.5%. Agreement between the two sensors within one repetition showed a systematic lower velocity for the vMaxPro device than the Tendo, with ICCs ranging from 0.28 to 0.88, which were accompanied by an MAE/MAPE of =0.13 m/s (11%). Almost all estimations systematically over/underestimated the measured 1RM, with a random scattering between 4.12% and 71.6%, depending on the velocity threshold used. Discussion: In agreement with most actual reviews, the presented results call for caution when using velocity profiles to estimate strength. Further approaches must be explored to minimize especially the random scattering. [ABSTRACT FROM AUTHOR]

Background: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction. Methods: We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Results: Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models. Conclusion: MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction. [ABSTRACT FROM AUTHOR]

County level estimates of mean sheet and rill erosion from the Conservation Effects Assessment Project (CEAP) are useful for program development and evaluation. Since county sample sizes in the CEAP survey are insufficient to support reliable direct estimators, small area estimation procedures are needed. The quantity of water runoff is a useful covariate but is unavailable for the full population. We use an estimate of mean runoff from the CEAP survey as a covariate in a small area model with sheet and rill erosion as the response. As the runoff and sheet and rill erosion are estimators from the same survey, the measurement error in the covariate is important as is the correlation between the measurement error and the sampling error. We conduct a detailed investigation of small area estimation in the presence of a correlation between the measurement error in the covariate and the sampling error in the response. In simulations, the proposed predictor is superior to small area predictors that assume the response and covariate are uncorrelated or that ignore the measurement error entirely. [ABSTRACT FROM AUTHOR]

Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population. These case count data obviously suffer from participation bias, and for prevalence estimation, these data are typically discarded in favor of infection surveys, or possibly also completed with auxiliary information. One exception is the series of infection surveys recorded by the Statistics Austria Federal Institute to study the prevalence of COVID-19 in Austria in April, May, and November 2020. In these infection surveys, participants were additionally asked if they were simultaneously recorded as COVID-19 positive in the case count data. In this article, we analyze the benefits of properly combining the outcomes from the infection survey with the case count data, to analyze the prevalence of COVID-19 in Austria in 2020, from which the case ascertainment rate can be deduced. The results show that our approach leads to a significant efficiency gain. Indeed, considerably smaller infection survey samples suffice to obtain the same level of estimation accuracy. Our estimation method can also handle measurement errors due to the sensitivity and specificity of medical testing devices and to the nonrandom sample weighting scheme of the infection survey. The proposed estimators and associated confidence intervals are implemented in the companion open source R package pempi available on the Comprehensive R Archive Network (CRAN). for this article are available online including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

This article addresses the deconvolution problem of estimating a square-integrable probability density from observations contaminated with additive measurement errors having a known density. The estimator begins with a density estimate of the contaminated observations and minimizes a reconstruction error penalized by an integrated squared mth derivative. Theory for deconvolution has mainly focused on kernel- or wavelet-based techniques, but other methods including spline-based techniques and this smoothness-penalized estimator have been found to outperform kernel methods in simulation studies. This article fills in some of these gaps by establishing asymptotic guarantees for the smoothness-penalized approach. Consistency is established in mean integrated squared error, and rates of convergence are derived for Gaussian, Cauchy, and Laplace error densities, attaining some lower bounds already in the literature. The assumptions are weak for most results; the estimator can be used with a broader class of error densities than the deconvoluting kernel. Our application example estimates the density of the mean cytotoxicity of certain bacterial isolates under random sampling; this mean cytotoxicity can only be measured experimentally with additive error, leading to the deconvolution problem. We also describe a method for approximating the solution by a cubic spline, which reduces to a quadratic program. for this article are available online. [ABSTRACT FROM AUTHOR]

Background: In DIC studies, positional parameters and speckle size are commonly used to characterise speckle images. The influence of edge parameters is ignored. This leads to a great difference between the DIC calculation results of simulated and real images. And some contradictory results are also produced. Objective: The main objective of this paper is to investigate the effect of edge parameters. As well as to give more reasonable parameters to describe the speckle characteristics. Methods: Firstly, this paper proposes a series of more reasonable parameters to describe the speckle features based on the mathematical expression of the speckle image. Subsequently, the effect of different edge functions on the computational error of DIC is investigated. The effect of different edge functions on pre-filtering is also investigated. Finally, real speckle images are produced using Gaussian and step functions to study the difference between the simulated and real speckle images. Results: Generally, it is believed that prefiltering can reduce the computational error of DIC, but for Gaussian edges, prefiltering hardly reduces the error, whereas hybrid edges correctly exhibit this phenomenon. Although the Gaussian edge perform well in the simulation, the actual speckle images taken show that the DIC error corresponding to the camera-acquired Gaussian speckle is much larger than that of the step speckle. Conclusions: The introduction of edge parameters to describe speckle images is necessary for DIC studies. Pre-filtering always reduces the DIC error, but for Gaussian edges this property cannot be demonstrated. The most suitable edges in reality are step edges, not Gaussian edges. [ABSTRACT FROM AUTHOR]

A binocular stereo vision measurement system is widely used in fields such as industrial inspection and marine engineering due to its high accuracy, low cost, and ease of deployment. An unreasonable structural design can lead to difficulties in image matching and inaccuracies in depth computation during subsequent processing, thereby limiting the system's performance and applicability. This paper establishes a systemic error analysis model to enable the validation of changes in structural parameters on the performance of the binocular vision measurement. Specifically, the impact of structural parameters such as baseline distance and object distance on measurement error is analyzed. Extensive experiments reveal that when the ratio of baseline length to object distance is between 1 and 1.5, and the angle between the baseline and the optical axis is between 30 and 40 degrees, the system measurement error is minimized. The experimental conclusions provide guidance for subsequent measurement system research and parameter design. [ABSTRACT FROM AUTHOR]

Regularized regression methods have attracted much attention in the literature, mainly due to its application in high-dimensional variable selection problems. Most existing regularization methods assume that the predictors are directly observed and precisely measured. It is well known that in a low-dimensional regression model if some covariates are measured with error, then the naive estimators that ignore the measurement error are biased and inconsistent. However, the impact of measurement error in regularized estimation procedures is not clear. For example, it is known that the ordinary least squares estimate of the regression coefficient in a linear model is attenuated towards zero and, on the other hand, the variance of the observed surrogate predictor is inflated. Therefore, it is unclear how the interaction of these two factors affects the selection outcome. To correct for the measurement error effects, some researchers assume that the measurement error covariance matrix is known or can be estimated using external data. In this paper, we propose the regularized instrumental variable method for generalized linear measurement error models. We show that the proposed approach yields a consistent variable selection procedure and root-n consistent parameter estimators. Extensive finite sample simulation studies show that the proposed method performs satisfactorily in both linear and generalized linear models. A real data example is provided to further demonstrate the usage of the method. [ABSTRACT FROM AUTHOR]

Abstract Background The current practice in guideline development is to use the control group event rate (CR) as a surrogate of baseline risk and to assume portability of the relative treatment effect across populations with low, moderate and high baseline risk. We sought to emulate this practice in a very large sample of meta-analyses. Methods We retrieved data from all meta-analyses published in the Cochrane Database of Systematic Reviews (2003–2020) that evaluated a binary outcome, reported 2 × 2 data for each individual study and included at least 4 studies. We excluded studies with no events. We conducted meta-analyses with odds ratios and relative risks and performed subgroup analyses based on tertiles of CR. In sensitivity analyses, we evaluated the use of total event rate (TR) instead of CR and using quartiles instead of tertiles. Results The analysis included 2,531 systematic reviews (27,692 meta-analyses, 226,975 studies, 25,669,783 patients).The percentages of meta-analyses with statistically significant interaction (P

Transparency about measurement errors in macroeconomic statistics is lacking. Inaccuracy is usually acknowledged only during extraordinary situations or under specific policy demands. Despite advances in technology and methodology, the overall accuracy has not improved due to economic changes and new complex activities. Even striking historical efforts did not change the persistence of substantial inaccuracies and biases. While the pioneers of national accounting addressed inaccuracies by using multiple accounting approaches and transparently reported error estimates, current statistical offices and other data producers rarely publish error measures, thereby creating an illusion of precision. Best practices and new measurement methods, such as satellite observation, Big Data, and Artificial Intelligence, promise improvements but face significant challenges without clear standards and an awareness of measurement error and bias. Regular and transparent reporting of measurement errors is and will be essential to improve the data reliability. Empowering data users—funding agencies, academics, and journalists—through error reporting and education can challenge the status quo.

The work provides a comparative study of double and dual electric field strength sensors. The comparison examined the design, technological and metrological parameters of the sensors, as well as the complexity of their measuring circuits. Research has revealed similarities in the design and technological parameters of the sensors and differences in metrological characteristics and the complexity of the measuring circuits. In terms of metrological characteristics, dual sensors are significantly superior to double ones. It has been established that a dual sensor having sensitive elements with angular dimensions 1=46,38 , and 2=90 , has a smaller error due to field inhomogeneity. The error does not exceed  ±2,76 % in the spatial measurement range 0a1. With the same error, double sensors can only be used in a limited spatial measurement range of 0a0,89. It is shown that dual sensors are superior to dual sensors in terms of metrological parameters. This indicates the advisability of their use.

Abstract Background Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction. Methods We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Results Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models. Conclusion MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction.

Abstract Objective To assess the cost-effectiveness of using cheaper-but-noisier outcome measures, such as a short questionnaire, for large simple clinical trials. Background To detect associations reliably, trials must avoid bias and random error. To reduce random error, we can increase the size of the trial and increase the accuracy of the outcome measurement process. However, with fixed resources, there is a trade-off between the number of participants a trial can enrol and the amount of information that can be collected on each participant during data collection. Methods To consider the effect on measurement error of using outcome scales with varying numbers of categories, we define and calculate the variance from categorisation that would be expected from using a category midpoint; define the analytic conditions under which such a measure is cost-effective; use meta-regression to estimate the impact of participant burden, defined as questionnaire length, on response rates; and develop an interactive web-app to allow researchers to explore the cost-effectiveness of using such a measure under plausible assumptions. Results An outcome scale with only a few categories greatly reduced the variance of non-measurement. For example, a scale with five categories reduced the variance of non-measurement by 96% for a uniform distribution. We show that a simple measure will be more cost-effective than a gold-standard measure if the relative increase in variance due to using it is less than the relative increase in cost from the gold standard, assuming it does not introduce bias in the measurement. We found an inverse power law relationship between participant burden and response rates such that a doubling the burden on participants reduces the response rate by around one third. Finally, we created an interactive web-app ( https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/ ) to allow exploration of when using a cheap-but-noisy measure will be more cost-effective using realistic parameters. Conclusion Cheaper-but-noisier questionnaires containing just a few questions can be a cost-effective way of maximising power. However, their use requires a judgement on the trade-off between the potential increase in risk of information bias and the reduction in the potential of selection bias due to the expected higher response rates.

Abstract Background Survival analysis has been used to characterize the time-to-event data. In medical studies, a typical application is to analyze the survival time of specific cancers by using high-dimensional gene expressions. The main challenges include the involvement of non-informaive gene expressions and possibly nonlinear relationship between survival time and gene expressions. Moreover, due to possibly imprecise data collection or wrong record, measurement error might be ubiquitous in the survival time and its censoring status. Ignoring measurement error effects may incur biased estimator and wrong conclusion. Results To tackle those challenges and derive a reliable estimation with efficiently computational implementation, we develop the R package AFFECT, which is referred to Accelerated Functional Failure time model with Error-Contaminated survival Times. Conclusions This package aims to correct for measurement error effects in survival times and implements a boosting algorithm under corrected data to determine informative gene expressions as well as derive the corresponding nonlinear functions.

This study considers the inverse problems inherent in interpreting temperature logging data from an isolated segment of the injection well in order to ascertain its operating period and the thermophysical properties of the oil reservoir.The forward problem of thermal conductivity was reduced to a one-dimensional axisymmetric formulation within the oil reservoir layer, disregarding the vertical thermal exchange with neighboring layers.The inverse problem of determining the well operating period was solved by reformulating the forward problem with regard to the temperature field derivative, which enabled the use of first-order optimization methods. Thus, Nesterov’s method was applied. An algorithm to automatically scale one of the method’s parameters (step length) was developed, and the optimal value of the second parameter (inertial step) was calculated. This increased the efficiency of the method by 10 – 15 % in solving the problem under consideration.The algorithm’s stability against perturbations in the initial data on temperature and thermophysical properties was demonstrated. The sensitivity analysis revealed that a 1 % error in the temperature measurements results in a standard deviation of the solution, which is about 2 % from the true value of the well operating period. A similar level of error was seen when the thermal diffusivity was overor underestimated by approximately 15 %. The solution was little sensitive to variations in the heat transfer coefficient between the oil reservoir and the well at characteristic magnitudes; even with a twofold distortion, the error in the determination of the well operating period did not exceed 1.5 %. To mitigate the error in thermometry interpretation to 1 %, temperature measurements must have an error margin of no more than 0.25 %, alongside precisely specified thermophysical properties of the oil reservoir, or, alternatively, when temperature is measured accurately, the rock thermal diffusivity must be set within an error margin of less than 3 %, but it is nearly impossible under real conditions.Increasing the number of temperature measurements diminishes the sensitivity to measurement errors, with the optimal efficacy achieved at 10 measurements, rendering further increments impractical.Therefore, the algorithm’s stability and the solution’s sensitivity of the inverse problem of determining the reservoir thermal diffusivity for a given operating period of the well relative to temperature measurement errors were found. The results show that a 1 % error in temperature measurements leads to a standard deviation of about 6 % from the true value.

A new method for measuring electric field strength is proposed. The method is based on the mean value measurement method, which uses a dual electroinduction spherical sensor. The use of the dual sensor for the method implementation allows to extend the functionality of the method by mean value. The method of measurement by the mean value assumes simultaneous measurement of two values of intensity E1 and E2 in one point of electric field with opposite sign errors. As the theory of construction of the new method has shown, it is possible to determine not only the measurement result as an average of two values of the strengths, but also to estimate the error of the measurement result and tentatively determine the distance to the field source. In order to determine the distance to the field source, a coefficient of the ratio of the two found strengths at each point of the field is introduced, by which the empirical dependence of the relative distance to the field source is found. The relative distance is the ratio between the radius of the spherical sensor and the distance to the field source. By determining the relative distance at each measuring point according to this relationship and substituting it into the known expression for the error, the error of the result at the measuring point is determined. The relative distance is also used to determine the distance to the field source. Thus, the new method of measurement allows to increase the accuracy of measurement of electric field strength, determined by the relative distance to the source of the field.

Abstract Background Social desirability can negatively affect the validity of self-reported measures, including underreporting of stigmatized behaviors like alcohol consumption. The Marlowe-Crowne Social Desirability Scale (SDS) is widely implemented and comprised of Denial and Attribution Domains (i.e., tendencies to deny undesirable traits or attribute socially desirable traits to oneself, respectively). Yet, limited psychometric research has been conducted in sub-Saharan Africa, where the prevalence of unhealthy alcohol consumption is high as well as religiosity and hierarchical social norms. To address this gap, we (a) conducted an exploratory study assessing certain psychometric properties of the 28-item SDS (Runyankole-translated) among persons with HIV (PWH) in Uganda, and (b) examined the relationship between social desirability and self-reported alcohol use. Methods We pooled baseline data (N = 1153) from three studies of PWH engaged in alcohol use from 2017 to 2021. We assessed the translated scale’s construct validity (via confirmatory factor analysis), internal consistency, item performance, differential item functioning by gender, concurrent validity with the DUREL religiosity index domains, and the association between social desirability and self-reported alcohol use. Results Participants had a mean age of 40.42 years, 63% were men, and 91% had an undetectable HIV viral load. The 28-item SDS had satisfactory construct validity (Model fit indices: RMSEA = 0.07, CFI = 0.84, TLI = 0.82) and internal consistency (Denial Domain ΩTotal = 0.82, Attribution Domain ΩTotal = 0.69). We excluded Item 14 (“I never hesitate to help someone in trouble”) from the Attribution Domain, which mitigated differential measurement error by gender and slightly improved the construct validity (Model fit indices: RMSEA = 0.06, CFI = 0.86, TLI = 0.85) and reliability (Attribution Domain ΩTotal = 0.72) of the 27-item modified SDS. Using the 27-item SDS, we found that social desirability was weakly correlated with religiosity and inversely associated with self-reported alcohol use after adjusting for biomarker-measured alcohol use and other confounders (β = -0.05, 95% confidence interval: -0.09 to -0.01, p-value = 0.03). Conclusions We detected and mitigated measurement error in the 28-item Runyankole-translated SDS, and found that the modified 27-item scale had satisfactory construct validity and internal consistency in our sample. Future studies should continue to evaluate the psychometric properties of the Runyankole-translated SDS, including retranslating Item 14 and reevaluating its performance.

Abstract We present evidence on the extent of measurement error in German longitudinal earnings data. Qualitatively, we confirm the main result of the international literature: longitudinal earnings data are relatively reliable in a cross section but much less so in first differences. Quantitatively, in the cross section our findings are very similar to those of Bound and Krueger (J Labor Econ 9:1–24, 1991) and Pischke (J Bus Econ Stat 13:305–314, 1995) for the United States while we find even stronger evidence that first-differencing exacerbates measurement error problems. We also show that measurement error in our survey data is not “classical” as it is negatively correlated with administrative earnings and positively autocorrelated over an extended period of time. Additionally, we estimate a model of measurement error stemming from underreporting of transitory earnings shocks in combination with a white-noise component and make a number of methodological contributions. Our results are robust to the use of two different linked survey-administrative data sets and various other sensitivity checks.

Objective: To assess the cost-effectiveness of using cheaper-but-noisier outcome measures, such as a short questionnaire, for large simple clinical trials. Background: To detect associations reliably, trials must avoid bias and random error. To reduce random error, we can increase the size of the trial and increase the accuracy of the outcome measurement process. However, with fixed resources, there is a trade-off between the number of participants a trial can enrol and the amount of information that can be collected on each participant during data collection. Methods: To consider the effect on measurement error of using outcome scales with varying numbers of categories, we define and calculate the variance from categorisation that would be expected from using a category midpoint; define the analytic conditions under which such a measure is cost-effective; use meta-regression to estimate the impact of participant burden, defined as questionnaire length, on response rates; and develop an interactive web-app to allow researchers to explore the cost-effectiveness of using such a measure under plausible assumptions. Results: An outcome scale with only a few categories greatly reduced the variance of non-measurement. For example, a scale with five categories reduced the variance of non-measurement by 96% for a uniform distribution. We show that a simple measure will be more cost-effective than a gold-standard measure if the relative increase in variance due to using it is less than the relative increase in cost from the gold standard, assuming it does not introduce bias in the measurement. We found an inverse power law relationship between participant burden and response rates such that a doubling the burden on participants reduces the response rate by around one third. Finally, we created an interactive web-app (https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/) to allow exploration of when using a cheap-but-noisy measure will be more cost-effective using realistic parameters. Conclusion: Cheaper-but-noisier questionnaires containing just a few questions can be a cost-effective way of maximising power. However, their use requires a judgement on the trade-off between the potential increase in risk of information bias and the reduction in the potential of selection bias due to the expected higher response rates. Key messages: A cheaper-but-noisier outcome measure, like a short form questionnaire, is a more cost-effective method of maximising power in large simple clinical trials than an error free gold standard measure when the percentage increase in noise from using the cheaper-but-noisier measure is less than the relative difference in the cost of administering the two measures. We have created an R-shiny app to facilitate the exploration of when this condition is met at https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/ Cheaper-but-noisier outcome measures are more likely to introduce information bias than a gold standard but may reduce selection bias because they reduce loss-to-follow-up. Researchers therefore need to form a judgement about the relative increase or decrease in bias before using a cheap-but-noisy measure. We encourage the development and validation of short form questionnaires to enable the use of high quality cheaper-but-noisier outcome measures in randomised controlled trials. [ABSTRACT FROM AUTHOR]

Background: Survival analysis has been used to characterize the time-to-event data. In medical studies, a typical application is to analyze the survival time of specific cancers by using high-dimensional gene expressions. The main challenges include the involvement of non-informaive gene expressions and possibly nonlinear relationship between survival time and gene expressions. Moreover, due to possibly imprecise data collection or wrong record, measurement error might be ubiquitous in the survival time and its censoring status. Ignoring measurement error effects may incur biased estimator and wrong conclusion. Results: To tackle those challenges and derive a reliable estimation with efficiently computational implementation, we develop the R package AFFECT, which is referred to Accelerated Functional Failure time model with Error-Contaminated survival Times. Conclusions: This package aims to correct for measurement error effects in survival times and implements a boosting algorithm under corrected data to determine informative gene expressions as well as derive the corresponding nonlinear functions. [ABSTRACT FROM AUTHOR]

Introduction: While randomized controlled trials remain the reference standard for evaluating treatment efficacy, there is an increased interest in the use of external control arms (ECA), namely in oncology, using real-world data (RWD). Challenges related to measurement of real-world oncology endpoints, like progression-free survival (PFS), are one factor limiting the use and acceptance of ECAs as comparators to trial populations. Differences in how and when disease assessments occur in the real-world may introduce measurement error and limit the comparability of real-world PFS (rwPFS) to trial progression-free survival. While measurement error is a known challenge when conducting an externally-controlled trial with real-world data, there is limited literature describing key contributing factors, particularly in the context of multiple myeloma (MM). Methods: We distinguish between biases attributed to how endpoints are derived or ascertained (misclassification bias) and when outcomes are observed or assessed (surveillance bias). We further describe how misclassification of progression events (i.e., false positives, false negatives) and irregular assessment frequencies in multiple myeloma RWD can contribute to these biases, respectively. We conduct a simulation study to illustrate how these biases may behave, both individually and together. Results: We observe in simulation that certain types of measurement error may have more substantial impacts on comparability between mismeasured median PFS (mPFS) and true mPFS than others. For instance, when the observed progression events are misclassified as either false positives or false negatives, mismeasured mPFS may be biased towards earlier (mPFS bias = -6.4 months) or later times (mPFS bias = 13 months), respectively. However, when events are correctly classified but assessment frequencies are irregular, mismeasured mPFS is more similar to the true mPFS (mPFS bias = 0.67 months). Discussion: When misclassified progression events and irregular assessment times occur simultaneously, they may generate bias that is greater than the sum of their parts. Improved understanding of endpoint measurement error and how resulting biases manifest in RWD is important to the robust construction of ECAs in oncology and beyond. Simulations that quantify the impact of measurement error can help when planning for ECA studies and can contextualize results in the presence of endpoint measurement differences. [ABSTRACT FROM AUTHOR]

Ranked set sampling is widely acknowledged for its superior efficiency compared with simple random sampling. Only a small amount of work has been conducted using ranked set sampling when measurement errors are present. This study introduces innovative estimators utilizing ranked set sampling to assess the population mean when faced with both correlated and uncorrelated measurement errors. The expressions for the bias and mean squared error of the proposed estimators are derived up to the first-order approximation, revealing their superior performance compared to the other examined estimators. The efficacy of the suggested estimators in handling measurement errors was demonstrated through numerical illustration and simulation study investigations. The recommended estimators are further compared to the existing ones using the percentage relative efficiency and mean squared error, and the impact of measurement errors on the results is highlighted through the percentage computation of measurement errors. The innovative estimators suggested were formulated by judiciously incorporating ratio, exponential, and log estimators. Numerical examples involving expenditure and income, as well as simulated data generated from a normal population using R software, affirm the superior performance of the proposed estimators over existing ones such as the usual mean estimator and those proposed by Vishwakarma and Singh (2022), as evidenced by the higher percent relative efficiency and lower mean squared error. The evaluation of the percentage contribution of measurement error values confirms the impact of measurement errors on the properties of the estimators. [ABSTRACT FROM AUTHOR]

Population size is a key parameter for the conservation of animal species. Close‐kin mark‐recapture (CKMR) relies on the observed frequency and type of kinship among individuals sampled from the population to estimate population size. Knowledge of the age of the individuals, or a surrogate thereof, is essential for inference with acceptable precision. One common approach, particularly in fish studies, is to measure animal length and infer age using an assumed age‐length relationship (a 'growth curve'). We used simulation to test the effect of misspecifying the length measurement error and the growth curve on population size estimation. Simulated populations represented two fictional shark species, one with a relatively simple life history and the other with a more complex life history based on the grey reef shark (Carcharhinus amblyrhynchos). We estimated sex‐specific adult abundance, which we assumed to be constant in time. We observed small median biases in these estimates ranging from 1.35% to 2.79% when specifying the correct measurement error standard deviation and growth curve. CI coverage was adequate whenever the growth curve was correctly specified. Introducing error via misspecified growth curves resulted in changes in the magnitude of the estimated adult population, where underestimating age negatively biased the abundance estimates. Over‐ and underestimating the standard deviation of length measurement error did not introduce a bias and had negligible effect on the variance in the estimates. Our findings show that assuming an incorrect standard deviation of length measurement error has little effect on estimation, but having an accurate growth curve is crucial for CKMR whenever ageing is based on length measurements. If ageing could be biased, researchers should be cautious when interpreting CKMR results and consider the potential biases arising from inaccurate age inference. [ABSTRACT FROM AUTHOR]

Google Trends is a popular data source that has been utilized in hundreds of studies across various fields, including information technology, business, economics, healthcare, and political science. While several previous research has addressed sampling error issues, this article focuses on the measurement errors resulting from changes in Google Trends' data collection method. By examining key phrases such as 'covid-19,' 'decoupling China,' and 'debt trap diplomacy,' this article reveals that, after 2022 when the data collection method was updated, Google Trends generates search results for terms that were not yet coined or yields too few results. These 'strange' search results indicate the presence of measurement errors in Google Trends data. Scholars are advised to validate the contents of Google Trends data through additional steps. In particular, authors in fields like business, economics, and healthcare are encouraged to replicate their research conducted before 2022 to assess the forecasting errors arising from the new data sets retrieved after that period. [ABSTRACT FROM AUTHOR]

Monitoring the ratio of two correlated normal random variables is often used in many industrial manufacturing processes. At the same time, measurement errors inevitably exist in most processes, which have different effects on the performance of various charting schemes. This paper comprehensively analyses the impacts of measurement errors on the detection ability of the cumulative sum (CUSUM) charting schemes for the ratio of two correlated normal variables. A thorough numerical assessment is performed using the Monte Carlo simulation, and the results indicate that the measurement errors negatively impact the performance of the CUSUM scheme for the ratio of two correlated normal variables. Increasing the number of measurements per set is not a lucrative approach for minimizing the negative impact of measurement errors on the performance of the CUSUM charting scheme when monitoring the ratio of two correlated normal variables. We consider a food formulation as an example that illustrates the quality control problems involving the ratio of two correlated normal variables in an industry with a measurement error. The results are presented, along with some suggestions for further study. [ABSTRACT FROM AUTHOR]

Partial least squares structural equation modeling (PLS-SEM) is a popular method of data analysis in marketing, information systems research, and related fields. Unfortunately, the literature on PLS-SEM contains a number of misstatements that ascribe characteristics to PLS-SEM that it does not possess. In our study, we consider why these misstatements continue to be made and reinforced. We show how inaccuracies, omissions, repetitions of past misconceptions, and the introduction of additional misconceptions lead to the so-called Woozle effect. As an example, we use perhaps the most serious misconception about PLS-SEM namely its alleged suitability for estimating the parameters of reflective measurement models. The Woozle effect is a cumulative process by which falsehoods become established as fact, and the self-correcting mechanisms of science are suspended. Through a literature review, we identify a number of factors that are likely to have contributed to the Woozle effect in the PLS-SEM literature. For the Woozle effect to disappear, researchers need to acknowledge that PLS-SEM, in its current incarnation, is not suitable for scientific work with reflective measurement models. [ABSTRACT FROM AUTHOR]

Abstract The application of high‐frequency radar as an instrument for assimilating tsunami‐induced current fields is garnering increasing interest. The performance of surface current velocity measurements depends on the azimuthal differences between the crossing radar beams at the measurement points. This study aimed to incorporate the measurement error distributions of the east‐west and north‐south velocity components into tsunami data assimilation based on an optimal interpolation method, assuming Gaussian noise with the time‐invariant and a uniform standard deviation (STD = 5 cm/s) of radial velocity measurements. Through the empirical orthogonal function (EOF) analysis of radar‐derived surface currents in the Kii Channel, Japan, the velocities reconstructed using higher modes (EOFs 16–274) were associated with measurement errors, portraying nonuniform distribution depending on the crossing beam angle of two radar beams. Based on independent fifteen‐time assimilation experiments for two different tsunami scenarios, for a uniform water depth of 500 m, we observed a significant improvement of up to 29% and 0.9% in the assimilation performance (on average) over the along‐coast stations for scenarios with 1‐ and 5‐m maximum initial sea surface heights, respectively. The measurement errors dependent on the crossing beam angle reduced the error‐induced tsunamis, resulting in stable assimilations, with lower STDs in the fifteen‐time assimilation performances. When the STD of Gaussian noise varies with time, it is important to consider the temporal change in the radial velocity measurement errors and/or noise‐filtering techniques, to maintain a certain level of noise intensity.

IntroductionWhile maximum strength diagnostics are applied in several sports and rehabilitative settings, dynamic strength capacity has been determined via the one-repetition maximum (1RM) testing for decades. Because the literature concerned several limitations, such as injury risk and limited practical applicability in large populations (e.g., athletic training groups), the strength prediction via the velocity profile has received increasing attention recently. Referring to relative reliability coefficients and inappropriate interpretation of agreement statistics, several previous recommendations neglected systematic and random measurement bias.MethodsThis article explored the random measurement error arising from repeated testing (repeatability) and the agreement between two common sensors (vMaxPro and TENDO) within one repetition, using minimal velocity thresholds as well as the velocity = 0 m/s method. Furthermore, agreement analyses were applied to the estimated and measured 1RM in 25 young elite male soccer athletes.ResultsThe results reported repeatability values with an intraclass correlation coefficient (ICC) = 0.66–0.80, which was accompanied by mean absolute (percentage) errors (MAE and MAPE) of up to 0.04–0.22 m/s and ≤7.5%. Agreement between the two sensors within one repetition showed a systematic lower velocity for the vMaxPro device than the Tendo, with ICCs ranging from 0.28 to 0.88, which were accompanied by an MAE/MAPE of ≤0.13 m/s (11%). Almost all estimations systematically over/ underestimated the measured 1RM, with a random scattering between 4.12% and 71.6%, depending on the velocity threshold used.DiscussionIn agreement with most actual reviews, the presented results call for caution when using velocity profiles to estimate strength. Further approaches must be explored to minimize especially the random scattering.

The Gradient-Free Kernel Conditional Stein Discrepancy (GF-KCSD), presented in our prior work, represents a significant advancement in goodness-of-fit testing for conditional distributions. This method offers a robust alternative to previous gradient-based techniques, specially when the gradient calculation is intractable or computationally expensive. In this study, we explore previously unexamined aspects of GF-KCSD, with a particular focus on critical values and test power—essential components for effective hypothesis testing. We also present novel investigation on the impact of measurement errors on the performance of GF-KCSD in comparison to established benchmarks, enhancing our understanding of its resilience to these errors. Through controlled experiments using synthetic data, we demonstrate GF-KCSD’s superior ability to control type-I error rates and maintain high statistical power, even in the presence of measurement inaccuracies. Our empirical evaluation extends to real-world datasets, including brain MRI data. The findings confirm that GF-KCSD performs comparably to KCSD in hypothesis testing effectiveness while requiring significantly less computational time. This demonstrates GF-KCSD’s capability as an efficient tool for analyzing complex data, enhancing its value for scenarios that demand rapid and robust statistical analysis.

Abstract Background and aim There is evidence to suggest that assessing back-specific altered self-perception may be useful when seeking to understand and manage low back pain (LBP). The Fremantle Back Awareness Questionnaire (FreBAQ) is a patient-reported measure of back-specific body perception that has never been adapted and psychometrically analysed in Italian. Hence, the objectives of this research were to cross-culturally adapt and validate the Italian version of this outcome measure (namely, the FreBAQ-I), to make it available for use with Italians suffering from chronic LBP. Methods The FreBAQ-I was developed by forward and backward translation, review by a committee skilled in patient-reported measures and test of the pre-final version to assess its clarity, acceptability, and relevance. The statistical analyses examined: structural validity based on Rasch analysis; hypotheses testing by investigating correlations of the FreBAQ-I with the Roland Morris Disability Questionnaire (RMDQ), a pain intensity numerical rating scale (PI-NRS), the Pain Catastrophising Scale (PCS), and the Tampa Scale of Kinesiophobia (TSK) (Pearson’s correlations); reliability by internal consistency (Cronbach’s alpha) and test–retest repeatability (intraclass correlation coefficient, ICC (2,1)); and measurement error by determining the minimum detectable change (MDC). After the development of a consensus-based translation of the FreBAQ-I, the new outcome measure was delivered to 100 people with chronic LBP. Results Rasch analysis confirmed the substantial unidimensionality and the structural validity of the FreBAQ-I. Hypothesis testing was considered good as at least 75% of the hypotheses were confirmed; correlations: RMDQ (r = 0.35), PI-NRS (r = 0.25), PCS (r = 0.41) and TSK (r = 0.38). Internal consistency was acceptable (alpha = 0.82) and test–retest repeatability was excellent (ICC (2,1) = 0.88, 95% CI: 0.83, 0.92). The MDC95 corresponded to 6.7 scale points. Conclusion The FreBAQ-I was found to be a unidimensional, valid, and reliable outcome measure in Italians with chronic LBP. Its application is advised for clinical and research use within the Italian speaking community.

Abstract Background Tracking gait and balance impairment in time is paramount in the care of older neurological patients. The Minimal Detectable Change (MDC), built upon the Standard Error of the Measurement (SEM), is the smallest modification of a measure exceeding the measurement error. Here, a novel method based on linear mixed-effects models (LMMs) is applied to estimate the standard error of the measurement from data collected before and after rehabilitation and calculate the MDC of gait and balance measures. Methods One hundred nine older adults with a gait impairment due to neurological disease (66 stroke patients) completed two assessment sessions before and after inpatient rehabilitation. In each session, two trials of the 10-meter walking test and the Timed Up and Go (TUG) test, instrumented with inertial sensors, have been collected. The 95% MDC was calculated for the gait speed, TUG test duration (TTD) and other measures from the TUG test, including the angular velocity peak (ωpeak) in the TUG test’s turning phase. Random intercepts and slopes LMMs with sessions as fixed effects were used to estimate SEM. LMMs assumptions (residuals normality and homoscedasticity) were checked, and the predictor variable ln-transformed if needed. Results The MDC of gait speed was 0.13 m/s. The TTD MDC, ln-transformed and then expressed as a percentage of the baseline value to meet LMMs’ assumptions, was 15%, i.e. TTD should be

Background Variables such as dietary intake are measured with error yet frequently used in observational epidemiology. Although this limitation is sometimes noted, these variables are still often modeled as covariates without formal correction or sincere dialogue about measurement unreliability potentially weakening the validity of statistical conclusions. Further, larger sample sizes increase power (bias) to detect spurious correlations. Counterintuitively, recent work suggested a non-monotonic relationship between confounder unreliability and how much controlling for the confounder reduces (or induces) bias when testing for an exposure-outcome association. If true, such non-monotonicity would be especially concerning for applications such as nutrition, where measurement reliability varies substantially, and large sample sizes are common. Methods We offer a detailed derivations of the square partial correlation between the outcome and exposure, controlling for the confounder. In our derivation, the measurement reliabilities of exposures and confounders are not arbitrarily constrained to be equal. Further, our theoretical results are investigated using simulations. Results Reassuringly, these derivations and simulations show that the counterintuitive non-monotonicity relationship between confounder unreliability and how much controlling for the confounder reduces (or induces) bias when testing for an exposure-outcome association is an artifact of the arbitrary constraint which forces the measurement reliabilities of exposures and confounders to be equal, which that does not always hold. Conclusions The profound and manifold effects of measurement error on estimation and statistical conclusion validity in realistic scenarios indicate that merely mentioning measurement error as a limitation and then dispensing with it is not an adequate response. We also explore questions for optimal study design subject to resource constraints when considering reliability of exposures, covariates, and outcomes.

We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further leads us to augment an amenable penalty term to the target function. We propose to estimate the regression parameter through minimizing the penalized target function. We derive the L1 and L2 convergence rates of the estimator and prove the variable selection consistency. We further establish the asymptotic normality of any subset of the parameters, where the subset can have infinitely many components as long as its cardinality grows sufficiently slow. We develop Wald and score tests based on the asymptotic normality of the estimator, which permits testing of linear functions of the members if the subset. We examine the finite sample performance of the proposed tests by extensive simulation. Finally, the proposed method is successfully applied to the Alzheimers Disease Neuroimaging Initiative study, which motivated this work initially.

Proline betaine (Pro-B) has been identified as a biomarker of dietary citrus intake, yet gaps remain in its validation as a quantitative predictor of intake during various physiological states. This study quantified sources of within-individual variation (WIV) in urinary Pro-B concentration during pregnancy and assessed its correlation with the reported usual intake of citrus fruit and juice. Pro-B concentrations were determined by 1H-NMR spectroscopy in spot and 24-h urine specimens (n = 255) collected throughout pregnancy from women participating in the MARBLES cohort study. Adjusted linear or log mixed effects models quantified WIV and tested potential temporal predictors of continuous or elevated Pro-B concentration. Pearson or Spearman correlations assessed the relationship between averaged repeated biomarker measures and usual citrus intake reported by food frequency questionnaires. The proportion of variance in urinary Pro-B attributable to WIV ranged from 0.69 to 0.74 in unadjusted and adjusted models. Citrus season was a significant predictor of Pro-B in most analyses (e.g., adjusted β [95% CI]: 0.52 [0.16, 0.88] for non-normalized Pro-B), while gestational age predicted only non-normalized Pro-B (adjusted β [95% CI]: -0.093 [-0.18, -0.0038]). Moderate correlations (rs of 0.40 to 0.42) were found between reported usual citrus intake and averaged repeated biomarker measurements, which were stronger compared to using a single measurement. Given the high degree of WIV observed in urinary Pro-B, multiple samples per participant are likely needed to assess associations between citrus consumption and health outcomes.

This study addresses the problem of parameter estimation in spatial autoregressive models with missing data and measurement errors in covariates. Specifically, a corrected likelihood estimation approach is employed to rectify the bias in the log-maximum likelihood function induced by measurement errors. Additionally, a combination of inverse probability weighting (IPW) and mean imputation is utilized to mitigate the bias caused by missing data. Under several mild conditions, it is demonstrated that the proposed estimators are consistent and possess oracle properties. The efficacy of the proposed parameter estimation process is assessed through Monte Carlo simulation studies. Finally, the applicability of the proposed method is further substantiated using the Boston Housing Dataset. [ABSTRACT FROM AUTHOR]

Measurement error is common in many statistical problems and has received considerable attention in various regression contexts. In this study, we consider the random coefficient autoregressive model with measurement error possibly present in covariates. The least squares and weighted least squares methods are used to estimate the model parameters, and the consistency and asymptotic normality of the two kinds of estimators are proved. Furthermore, we propose an empirical likelihood method based on weighted score equations to construct confidence regions for the parameters. The simulation results show that the weighted least squares estimators are superior to the least squares estimators and that the confidence regions have good finite-sample behavior. At last, the model is applied to a real data example. [ABSTRACT FROM AUTHOR]

In order to determine the junction temperature of semiconductor devices operating at different power supply and cooling conditions, their thermal parameters are needed. This article describes an original measurement set-up enabling the determination of thermal parameters of power semiconductor devices. In contrast to other set-ups described in the literature, this set-up makes it possible to measure thermal parameters which characterize the efficiency of the removal generated due to a self-heating phenomenon, as well as the parameters characterizing mutual thermal couplings. The presented set-up makes use of an indirect electrical method to determine the junction temperature of diodes, bipolar and unipolar transistors and IGBTs. The methods used to measure the self and transfer transient thermal impedances of these devices and the construction of the set-up are described. The influence of selected factors on the accuracy of the measurements is analyzed. Examples of the measurement results of thermal parameters (self and transfer transient thermal impedances) of power semiconductor devices operating at different cooling conditions are presented. The obtained research results are discussed. [ABSTRACT FROM AUTHOR]

Background: Effective rehabilitation of upper limb musculoskeletal disorders requires multimodal assessment to guide clinicians' decision-making. Furthermore, a comprehensive assessment must include reliable tests. Nevertheless, the interrelationship among various upper limb tests remains unclear. This study aimed to evaluate the reliability of easily applicable upper extremity assessments, including absolute values and asymmetries of muscle mechanical properties, pressure pain threshold, active range of motion, maximal isometric strength, and manual dexterity. A secondary aim was to explore correlations between different assessment procedures to determine their interrelationship. Methods: Thirty healthy subjects participated in two experimental sessions with 1 week between sessions. Measurements involved using a digital myotonometer, algometer, inclinometer, dynamometer, and the Nine-Hole Peg test. Intraclass correlation coefficients, standard error of the mean, and minimum detectable change were calculated as reliability indicators. Pearson's correlation was used to assess the interrelationship between tests. Results: For the absolute values of the dominant and nondominant sides, reliability was 'good' to 'excellent' for muscle mechanical properties, pressure pain thresholds, active range of motion, maximal isometric strength, and manual dexterity. Similarly, the reliability for asymmetries ranged from 'moderate' to 'excellent' across the same parameters. Faster performance in the second session was consistently found for the Nine-Hole Peg test. No systematic inter-session errors were identified for the values of the asymmetries. No significant correlations were found between tests, indicating test independence. Conclusion: These findings indicate that the sensorimotor battery of tests is reliable, while monitoring asymmetry changes may offer a more conservative approach to effectively tracking recovery of upper extremity injuries. [ABSTRACT FROM AUTHOR]

Background and aim: There is evidence to suggest that assessing back-specific altered self-perception may be useful when seeking to understand and manage low back pain (LBP). The Fremantle Back Awareness Questionnaire (FreBAQ) is a patient-reported measure of back-specific body perception that has never been adapted and psychometrically analysed in Italian. Hence, the objectives of this research were to cross-culturally adapt and validate the Italian version of this outcome measure (namely, the FreBAQ-I), to make it available for use with Italians suffering from chronic LBP. Methods: The FreBAQ-I was developed by forward and backward translation, review by a committee skilled in patient-reported measures and test of the pre-final version to assess its clarity, acceptability, and relevance. The statistical analyses examined: structural validity based on Rasch analysis; hypotheses testing by investigating correlations of the FreBAQ-I with the Roland Morris Disability Questionnaire (RMDQ), a pain intensity numerical rating scale (PI-NRS), the Pain Catastrophising Scale (PCS), and the Tampa Scale of Kinesiophobia (TSK) (Pearson's correlations); reliability by internal consistency (Cronbach's alpha) and test–retest repeatability (intraclass correlation coefficient, ICC (2,1)); and measurement error by determining the minimum detectable change (MDC). After the development of a consensus-based translation of the FreBAQ-I, the new outcome measure was delivered to 100 people with chronic LBP. Results: Rasch analysis confirmed the substantial unidimensionality and the structural validity of the FreBAQ-I. Hypothesis testing was considered good as at least 75% of the hypotheses were confirmed; correlations: RMDQ (r = 0.35), PI-NRS (r = 0.25), PCS (r = 0.41) and TSK (r = 0.38). Internal consistency was acceptable (alpha = 0.82) and test–retest repeatability was excellent (ICC (2,1) = 0.88, 95% CI: 0.83, 0.92). The MDC95 corresponded to 6.7 scale points. Conclusion: The FreBAQ-I was found to be a unidimensional, valid, and reliable outcome measure in Italians with chronic LBP. Its application is advised for clinical and research use within the Italian speaking community. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the veracity of self‐reports of month‐level health insurance coverage in the Current Population Survey Annual Social and Economic Supplement (CPS). Data Sources and Study Setting: The CHIME (Comparing Health Insurance Measurement Error) study used health insurance enrollment records from a large regional Midwest insurer as sample for primary data collection in spring 2015. Study Design: A sample of individuals enrolled in a range of public and private coverage types (including Medicaid and marketplace) was administered the CPS health insurance module, which included questions about month‐level coverage, by type, over a 17–18‐month time span. Survey data was then matched to enrollment records covering that same time frame, and concordance between the records and self‐reports was assessed. Data Collection/Extraction Methods: Sample was drawn by the insurer's informatics specialists and Census Bureau interviewers conducted the survey. Following data collection, updated enrollment records were matched to the survey data to produce a person‐level file of coverage by type at the month‐level. Principal Findings: For 91% of the overall sample, coverage status and type were reported accurately for at least 75% of observed months. Results varied somewhat by stability of coverage. Among those who were continuously covered throughout the 17–18 month observation period (which comprised 64% of the overall sample), that level of reporting accuracy was observed for 94% of the sample; for those who had censored spells (34% of the overall sample), the figure was 87%; and among those with gaps and/or changes according to the records (2% of the overall sample), for 82% of the group at least 75% of months were reported accurately. Conclusions: Findings suggest that reporting accuracy of month‐level coverage in the CPS is high and that the survey could become a valuable new data source for studying the dynamics of coverage, including the Medicaid unwinding. [ABSTRACT FROM AUTHOR]

Background: Tracking gait and balance impairment in time is paramount in the care of older neurological patients. The Minimal Detectable Change (MDC), built upon the Standard Error of the Measurement (SEM), is the smallest modification of a measure exceeding the measurement error. Here, a novel method based on linear mixed-effects models (LMMs) is applied to estimate the standard error of the measurement from data collected before and after rehabilitation and calculate the MDC of gait and balance measures. Methods: One hundred nine older adults with a gait impairment due to neurological disease (66 stroke patients) completed two assessment sessions before and after inpatient rehabilitation. In each session, two trials of the 10-meter walking test and the Timed Up and Go (TUG) test, instrumented with inertial sensors, have been collected. The 95% MDC was calculated for the gait speed, TUG test duration (TTD) and other measures from the TUG test, including the angular velocity peak (ωpeak) in the TUG test's turning phase. Random intercepts and slopes LMMs with sessions as fixed effects were used to estimate SEM. LMMs assumptions (residuals normality and homoscedasticity) were checked, and the predictor variable ln-transformed if needed. Results: The MDC of gait speed was 0.13 m/s. The TTD MDC, ln-transformed and then expressed as a percentage of the baseline value to meet LMMs' assumptions, was 15%, i.e. TTD should be < 85% of the baseline value to conclude the patient's improvement. ωpeak MDC, also ln-transformed and expressed as the baseline percentage change, was 25%. Conclusions: LMMs allowed calculating the MDC of gait and balance measures even if the test-retest steady-state assumption did not hold. The MDC of gait speed, TTD and ωpeak from the TUG test with an inertial sensor have been provided. These indices allow monitoring of the gait and balance impairment, which is central for patients with an increased falling risk, such as neurological old persons. Trial registration: NA. [ABSTRACT FROM AUTHOR]