Presence Of Measurement Errors Research Articles

Longitudinal MRI data analysis in presence of measurement error but absence of replicates

ABSTRACTLongitudinal data analysis has found immense importance in biomedical fields to assess relationships between an outcome and its explanatory variables over time. However, this analysis is unreliable in presence of measurement errors in response data because the errors confound the effect of any signal caused by process changes. This confounding can be easily resolved by estimating and isolating the measurement errors using replicated measurements; i.e., multiple measurements in a small (stationary) time interval. However, in many medical applications, such as in magnetic resonance imaging (MRI), taking replicated measurements is not possible due to cost and/or risk considerations. This makes measurement error estimation and data analysis very challenging. In this article, we propose a novel method for the analysis of unreplicated longitudinal data under the presence of measurement errors. We formulate the problem using mixed-effect regression and develop a new EM-Variogram technique to estimate regression coefficients as well as variance components. The proposed approach decouples the confounded observed variance into the process and measurement system variances, and helps construct precise confidence intervals, leading to a more powerful statistical hypothesis test for the model parameters. We validate the proposed method using simulation and also apply it to a longitudinal MRI data for patients with neurodegenerative diseases. The results show improved statistical power in measuring their hippocampal volume loss, and a quicker degeneration detection. We also demonstrate the robustness of the proposed method with respect to missing values, a common issue in longitudinal data.

Estimation of Causal Effect Measures in the Presence of Measurement Error in Confounders

The odds ratio, risk ratio, and the risk difference are important measures for assessing comparative effectiveness of available treatment plans in epidemiological studies. Estimation of these measures, however, is often challenged by the presence of error-contaminated confounders. In this article, by adapting two correction methods for measurement error effects applicable to the noncausal context, we propose valid methods which consistently estimate the causal odds ratio, causal risk ratio, and the causal risk difference for settings with error-prone confounders. Furthermore, we develop a bootstrap-based procedure to construct estimators with improved asymptotic efficiency. Numerical studies are conducted to assess the performance of the proposed methods.

Separating variability in healthcare practice patterns from random error.

Improving the quality of care that patients receive is a major focus of clinical research, particularly in the setting of cardiovascular hospitalization. Quality improvement studies seek to estimate and visualize the degree of variability in dichotomous treatment patterns and outcomes across different providers, whereby naive techniques either over-estimate or under-estimate the actual degree of variation. Various statistical methods have been proposed for similar applications including (1) the Gaussian hierarchical model, (2) the semi-parametric Bayesian hierarchical model with a Dirichlet process prior and (3) the non-parametric empirical Bayes approach of smoothing by roughening. Alternatively, we propose that a recently developed method for density estimation in the presence of measurement error, moment-adjusted imputation, can be adapted for this problem. The methods are compared by an extensive simulation study. In the present context, we find that the Bayesian methods are sensitive to the choice of prior and tuning parameters, whereas moment-adjusted imputation performs well with modest sample size requirements. The alternative approaches are applied to identify disparities in the receipt of early physician follow-up after myocardial infarction across 225 hospitals in the CRUSADE registry.

Estimation of population mean for a sensitive variable in the presence of measurement error

Survey researchers are well aware that non-sampling errors are likely to occur if individuals are asked to reveal information about personal or socially undesirable behavior. Getting accurate responses to the so-called sensitive questions is an age-old problem in survey research. The idea of randomized response technique (RRT) was given by Warner (1965), to provide privacy to respondents in such surveys. In this paper, a generalized estimator of population mean for sensitive study variable is proposed. A simulation study is conducted to assess the performance of the proposed estimator in the presence and absence of measurement error.

Singularity Analysis for the Existing Closed-Form Solutions of the Hand-Eye Calibration

In this paper, a class of singular phenomenon is first found for the existing closed-form solutions of the hand–eye calibration problem with the form ${{{A}}_{i}{{X}}={{X}}{{ B}}_{i}}$ when the angles corresponding to rotational parts of ${{A}}_{i}$ and ${{B}}_{i}$ are near or equal to $\pi $ radian. A universal observability index is put forward to detect when this singularity would undoubtedly occur. For avoiding this singularity, a novel analytical solution based on a new cost function is proposed to estimate the hand–eye matrix in the presence of measurement errors. Simulation and experimental results can illustrate the feasibility and benefits of the proposed observability index and the singular-free closed-form solution. In addition, the other kind of singular phenomenon is also discovered for the existing closed-form solution, where the orientation of the unknown hand–eye matrix is parameterized by modified Rodrigues parameters. Therefore, in order to obtain the non-singular analytical solution based on modified Rodrigues parameters, a novel additional rotation theory is introduced and verified by the hand–eye calibration of a novel surgical robot.

Wavelet methods for erratic regression means in the presence of measurement error

Wavelet methods for erratic regression means in the presence of measurement error

The memory of volatility

The focus of the volatility literature on forecasting and the predominance of theconceptually simpler HAR model over long memory stochastic volatility models has led to the factthat the actual degree of memory estimates has rarely been considered. Estimates in the literaturerange roughly between 0.4 and 0.6 - that is from the higher stationary to the lower non-stationaryregion. This difference, however, has important practical implications - such as the existence or nonexistenceof the fourth moment of the return distribution. Inference on the memory order is complicatedby the presence of measurement error in realized volatility and the potential of spurious long memory.In this paper we provide a comprehensive analysis of the memory in variances of international stockindices and exchange rates. On the one hand, we find that the variance of exchange rates is subject tospurious long memory and the true memory parameter is in the higher stationary range. Stock indexvariances, on the other hand, are free of low frequency contaminations and the memory is in the lowernon-stationary range. These results are obtained using state of the art local Whittle methods that allowconsistent estimation in presence of perturbations or low frequency contaminations.

The performance of EWMA median and CUSUM median control charts for a normal process with measurement errors

AbstractMeasurement error is often occurred in statistical process control. The effect of a linearly covariate error model on the exponentially weighted moving average (EWMA) median and cumulative sum (CUSUM) median charts is investigated. The results indicate that the EWMA median and CUSUM median charts are significantly affected in the presence of measurement errors. We compared the performance of the EWMA median and CUSUM median charts by using Markov chain method in the average run length and the standard deviation of the run length. We concluded that the CUSUM median chart for small shifts and the EWMA median chart for larger shifts are recommended. Two examples are provided to illustrate the application of the EWMA and CUSUM median charts with measurement errors.

Generalized estimator of population mean by using conventional and non-conventional measures in the presence of measurement errors

ABSTRACTIn survey research, it is assumed that reported response by the individual is correct. However, given the issues of prestige bias, self-respect, respondent's reported data often produces estimated values which are highly deviated from the true values. This causes measurement error (ME) to be present in the sample estimates. In this article, the estimation of population mean in the presence of measurement error using information on a single auxiliary variable is studied. A generalized estimator of population mean is proposed. The class of estimators is obtained by using some conventional and non-conventional measures. Simulation and numerical study is also conducted to assess the performance of estimators in the presence and absence of measurement error.

CoCoLasso for high-dimensional error-in-variables regression

Much theoretical and applied work has been devoted to high-dimensional regression with clean data. However, we often face corrupted data in many applications where missing data and measurement errors cannot be ignored. Loh and Wainwright [Ann. Statist. 40 (2012) 1637–1664] proposed a nonconvex modification of the Lasso for doing high-dimensional regression with noisy and missing data. It is generally agreed that the virtues of convexity contribute fundamentally the success and popularity of the Lasso. In light of this, we propose a new method named CoCoLasso that is convex and can handle a general class of corrupted datasets. We establish the estimation error bounds of CoCoLasso and its asymptotic sign-consistent selection property. We further elucidate how the standard cross validation techniques can be misleading in presence of measurement error and develop a novel calibrated cross-validation technique by using the basic idea in CoCoLasso. The calibrated cross-validation has its own importance. We demonstrate the superior performance of our method over the nonconvex approach by simulation studies.

Orienting the causal relationship between imprecisely measured traits using GWAS summary data.

Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration.

BackgroundNegative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome.MethodsWe investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations.ResultsOur results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study.ConclusionsMeasurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present.

Aircraft parameter estimation using Hybrid Neuro Fuzzy and Artificial Bee Colony optimization (HNFABC) algorithm

In this paper, method of aerodynamic parameter estimation of aircraft, in the presence of process and measurement noise, have been introduced with the help of relevant experiments carried out on simulated as well as real aircraft data. A merger between two recently proposed artificial intelligence based techniques one of which simulates the intelligent foraging behavior of honey bee swarm viz. Artificial Bee Colony (ABC) optimization along with well-known Adaptive Neuro-Fuzzy System (ANFIS) that simulates the working of a unit of brain viz. neuron combined with the knowledge-based decision making capability of fuzzy system have been shown to be a promisingly new approach to the problem of aerodynamic modeling and parameter estimation for both aerodynamically stable aircraft in the presence of measurement error (sensor noise). Obtained results have been compared with benchmark estimation methods viz. Least Square, Filter Error Method. Corroborating results and comparisons have been furnished highlighting the efficacy of proposed algorithm. Furthermore, it has been shown that the proposed hybrid estimation algorithm can have realizable application in extracting stability and control variables utilizing kinematics of stable aircraft even in absence of adequate information content in the data history.

Measuring cosmic shear and birefringence using resolved radio sources

We develop a new method of extracting simultaneous measurements of weak lensing shear and a local rotation of the plane of polarization using observations of resolved radio sources. We show that the direction of polarization is statistically linked with that of the gradient of the total intensity field, and this provides the basis of our method. Using a number of sources spread over the sky, this method allows constraints to be placed on cosmic shear and birefringence, and it can be applied to any resolved radio sources for which such a correlation exists. Assuming that the rotation and shear are constant across the source, we use this relationship to construct a quadratic estimator and investigate its properties using simulated observations. We develop a calibration scheme using simulations based on the observed images to mitigate a bias which occurs in the presence of measurement errors and an astrophysical scatter on the polarization. The method is applied directly to archival data of radio galaxies where we measure a mean rotation signal of $\left<\omega\right>=-2.03^{\circ}\pm0.75^{\circ}$ and an average shear compatible with zero using 30 reliable sources. This level of constraint on an overall rotation is comparable with current leading constraints from CMB experiments and is expected to increase by at least an order of magnitude with future high precision radio surveys, such as those performed by the SKA. We also measure the shear and rotation two-point correlation functions and estimate the number of sources required to detect shear and rotation correlations in future surveys.

Adjustment and testing comparison of absolute gravimeters in November 2013

&lt;span style="color: #000000;"&gt;This paper is focused on a comparison measurement processing of absolute gravimeters in 2013. The comparison deals with a number of various types of absolute gravimeters and includes also an absolute gravimeter from Geodetic observatory Pecný. Comparative measurements are performed to detect systematic errors of gravimeters. A result of processing is most likely value of a gravity and a systematic error of individual devices. Measured values are input to a adjustment with condition a sum of systematic errors is zero. A part of this process is also verification following output: (i) value of a posteriori standard deviation, (ii) size of corrections and (iii) statistical significance of systematic errors. The results of adjustment are 15 gravity values on the reference places and 25 systematic errors of measuring instruments. Result shows that the presence of systematic errors in measurements is not statistically provable because the systematic errors are similarly sized as their standard deviation.&lt;/span&gt;

A generalized estimator for finite population mean in the presence of measurement errors in stratified random sampling

ABSTRACTA generalized estimator is introduced for finite population mean in stratified random sampling when observations are contaminated with measurement errors. Many special cases of the proposed estimator are possible. The bias and mean square error of the proposed family of estimators are derived. The performance of the proposed estimator is evaluated both theoretically and empirically in the presence and absence of measurement error.

Partial-Nodes-Based State Estimation for Complex Networks With Unbounded Distributed Delays.

In this brief, the new problem of partial-nodes-based (PNB) state estimation problem is investigated for a class of complex network with unbounded distributed delays and energy-bounded measurement noises. The main novelty lies in that the states of the complex network are estimated through measurement outputs of a fraction of the network nodes. Such fraction of the nodes is determined by either the practical availability or the computational necessity. The PNB state estimator is designed such that the error dynamics of the network state estimation is exponentially ultimately bounded in the presence of measurement errors. Sufficient conditions are established to ensure the existence of the PNB state estimators and then the explicit expression of the gain matrices of such estimators is characterized. When the network measurements are free of noises, the main results specialize to the case of exponential stability for error dynamics. Numerical examples are presented to verify the theoretical results.

Robust gene selection methods using weighting schemes for microarray data analysis

BackgroundA common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates.ResultsWe have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays.ConclusionsThe results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

Modeling Congruence with Latent Moderated Structural Equations: A Simulation Study

Polynomial regression analysis (PRA) has been used as an alternative to traditional methods in congruence research. However, it relies on the assumption that predictors are measured without error. The presence of measurement error may significantly affect the unbiasedness and precision of the analysis. In the current study, we introduce a method that addresses this concern by integrating PRA with structure equation modeling (SEM), termed latent moderated structural equations (LMS). We conducted a simulation study to compare the accuracy of PRA and LMS in parameter estimation and investigated a number of factors that influenced the performance of the two methods, including the reliability of predictor and outcome measures, population effect sizes of linear and nonlinear effects, population correlation between predictors, number of indicators per latent variable, and sample size. We demonstrated that, compared to PRA, LMS parameter estimates were accurate and stable, even when reliability was low or when latent correlation between linear predictors was high. When reliability was moderate to high, LMS models could be properly estimated with relatively small sample sizes. We recommend that researchers use LMS when examining congruence effects and we provide sample Mplus codes to facilitate the application of LMS in congruence research.

Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease

Background and ObjectiveAnemia is a common comorbidity in patients with chronic kidney disease (CKD) and is frequently associated with decreased physical component of quality of life, as well as adverse cardiovascular events. Current treatment methods for renal anemia are mostly population-based approaches treating individual patients with a one-size-fits-all model. However, FDA recommendations stipulate individualized anemia treatment with precise control of the hemoglobin concentration and minimal drug utilization. In accordance with these recommendations, this work presents an individualized drug dosing approach to anemia management by leveraging the theory of optimal control. MethodsA Multiple Receding Horizon Control (MRHC) approach based on the RBF-Galerkin optimization method is proposed for individualized anemia management in CKD patients. Recently developed by the authors, the RBF-Galerkin method uses the radial basis function approximation along with the Galerkin error projection to solve constrained optimal control problems numerically. The proposed approach is applied to generate optimal dosing recommendations for individual patients. ResultsPerformance of the proposed approach (MRHC) is compared in silico to that of a population-based anemia management protocol and an individualized multiple model predictive control method for two case scenarios: hemoglobin measurement with and without observational errors. In silico comparison indicates that hemoglobin concentration with MRHC method has less variation among the methods, especially in presence of measurement errors. In addition, the average achieved hemoglobin level from the MRHC is significantly closer to the target hemoglobin than that of the other two methods, according to the analysis of variance (ANOVA) statistical test. Furthermore, drug dosages recommended by the MRHC are more stable and accurate and reach the steady-state value notably faster than those generated by the other two methods. ConclusionsThe proposed method is highly efficient for the control of hemoglobin level, yet provides accurate dosage adjustments in the treatment of CKD anemia.