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Results for “measurement error”

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PubMed1992

The effect of awareness of measurement error on physical therapists' confidence in their decisions.

This study examined whether physical therapists understand the meaning of measurement error and whether information about measurement error affects their decisions. One of four versions of two physical therapy problems was mailed to 500 randomly selected physical therapists. Therapists were asked to define reliability and error of measurement, to estimate the error of measurement of two assessments, and to make decisions about an intervention based on specific measurements. They were also asked to rate their confidence in those decisions. Problems varied on the presence or absence of measurement information and on the difference between an observed measurement and a criterion measurement against which the observed measurement must be compared to make a decision. The response rate was 62%; respondents represented a typical profile of practicing physical therapists. The therapists understood reliability, but they did not correctly describe the relationship between reliability and error of measurement. Their estimates of the error of measurement of the two assessments were reasonable for only one procedure. The presence or absence of measurement information and difference between observed and criterion measurements affected their confidence, albeit inappropriately, in only one problem. Confidence was not affected by the therapists' level of experience, type of reading, formal study, or degree earned. Therapists responded to the two problems differently. The problems involved different measures, roles, utilities, and structures. The process of decision making does not generalize to all decision types. Measurement principles and strategies of use in decision making must be emphasized in physical therapy curricula so that physical therapists can consider the quality of their assessment data in making clinical decisions.

Anthropometry↗

PubMed2003

Evaluation of refractive error measurements of the Wavescan Wavefront system and the Tracey Wavefront aberrometer.

PURPOSE: To evaluate the accuracy and repeatability of the WaveScan WavePrint system and the Tracey wavefront aberrometer in measuring refractive errors in phakic eyes. SETTING: Cullen Eye Institute, Baylor College of Medicine, Houston, Texas, USA. METHODS: Using subjective manifest refraction (MR) as the standard, the spherical equivalent (SE), sphere, and cylinder were compared to values measured by WaveScan and Tracey devices in virgin eyes and eyes that had had corneal refractive surgery. Astigmatism was evaluated using vector analysis. The accuracy of the WaveScan and Tracey devices was assessed by 95% limits of agreement (95% LA), and repeatability was analyzed by 2 standard deviations (SDs) and intraclass correlation coefficients (ICCs). RESULTS: The mean differences in SE, sphere, and cylinder between MR and WaveScan were -0.26 diopter (D), -0.12 D, and -0.28 D, respectively, and between MR and Tracey, -0.21 D, -0.01 D, and -0.40 D, respectively. The 95% LA for SE, sphere, and cylinder were -1.09 to 0.57 D, -1.14 to 0.89 D, and -0.95 to 0.40 D, respectively, for WaveScan and -1.37 to 0.95 D, -1.27 to 1.26 D, and -1.16 to 0.35 D, respectively, for Tracey. Vector analysis revealed mean differences of -0.47 +0.07 x 9 degrees between MR and WaveScan and of -0.53 +0.27 x 12 between MR and Tracey. The 2 SDs for SE, sphere, and cylinder were 0.26 D, 0.29 D, and 0.16 D, respectively, for WaveScan and 0.31 D, 0.36 D, and 0.33 D, respectively, for Tracey. The ICCs for SE, sphere, and cylinder were 0.993, 0.992, and 0.902, respectively, for WaveScan and 0.994, 0.992, and 0.764, respectively, for Tracey. The Tracey device measured all eyes evaluated; the WaveScan could not measure 14% of normal eyes and 50% of post laser in situ keratomileusis eyes. CONCLUSIONS: Using MR as the standard, refractive errors measured by the WaveScan and Tracey devices were reliable and reproducible. However, the Tracey device was more robust in its ability to obtain measurements in normal and postoperative eyes.

Adult↗

PubMed1977

The effects of measurement errors on some multivariate procedures.

The effects of random errors of measurement on a variety of basic multivariate procedures are discussed. For some procedures, the effect is one of attenuation (i.e., simply reducing the estimated strength of association). For others, the effect may be to alter the apparent direction of association. Some proposed solutions to the problems caused by errors of measurement are presented, and areas requiring further research are pointed out.

California↗

PubMed1998

Measurement error as a source of QT dispersion: a computerised analysis.

OBJECTIVE: To establish a general method to estimate the measuring error in QT dispersion (QTD) determination, and to assess this error using a computer program for automated measurement of QTD. SUBJECTS: Measurements were done on 1220 standard simultaneous 12 lead electrocardiograms. DESIGN: The computer program was validated against two observers on a random subset of 100 electrocardiograms. Simple laws of physics require that at least five of the six extremity leads have the same QT duration. This allows the direct assessment of the error in measuring QTD derived from five extremity leads (QTD5). It also enables ST-T amplitude dependent distributions of measurement error in determining QT duration to be established. These QT error distributions were then used to estimate the error in measuring QTD from all 12 leads (QTD12). MAIN OUTCOME MEASURES: Mean and standard deviation of error in measuring QTD duration, QTD5, and QTD12. RESULTS: Performance of the program was comparable to that of observers. Errors in measuring QT duration (measured QT minus reference QT) fell from a mean (SD) of 6.9 (17.1) ms for ST-T amplitudes < 50 microV to -1.4 (6.3) ms for amplitudes > 350 microV. Measurement errors of QTD5 and QTD12 were 20.4 (11.5) ms and 29.4 (14.9) ms. CONCLUSIONS: The fact that no QTD can exist between five of the six extremity leads provides a means of estimating QTD measurement error. Measuring error of QT duration is dependent on ST-T amplitude. QTD measurement error is large compared with typical QTD values reported.

Databases, Factual↗

PubMed2004

Two dimensions of measurement error: classical and Berkson error in residential radon exposure assessment.

Measurement error in exposure assessment is unavoidable. Statistical methods to correct for such errors rely upon a valid error model, particularly regarding the classification of classical and Berkson error, the structure and the size of the error. We provide a detailed list of sources of error in residential radon exposure assessment, stressing the importance of (a) the differentiation between classical and Berkson error and (b) the clear definitions of predictors and operationally defined predictors using the example of two German case-control studies on lung cancer and residential radon exposure. We give intuitive measures of error size and present evidence on both the error size and the multiplicative structure of the error from three data sets with repeated measurements of radon concentration. We conclude that modern exposure assessment should not only aim to be as accurate and precise as possible, but should also provide a model of the remaining measurement errors with clear differentiation of classical and Berkson components.

Bias↗

PubMed1997

[Evaluation of possible measuring errors from overlaying pressure components in invasive blood pressure recording with external transducers].

Invasive arterial blood pressure measurement using external transducers is a routine measure in intensive care medicine and anaesthesiology. Despite the frequency of its use, measuring errors may occur that can strongly affect the significance of the results. The error with the greatest influence on blood pressure measurement arises from an inadequate dynamic behaviour of the measuring chain, and has been investigated in numerous publications. Measuring errors due to hydrostatic and dynamic pressure have not been considered to date. The present investigation describes an analysis of such measuring errors and possible ways of avoiding them. A side-hole catheter permits pressure measurements to be made without the measuring error caused by dynamic pressure behaviour. However, both analytical and experimental studies revealed that, since the velocities involved are small, this error is negligible in blood pressure measurements, and the increased cost of such catheters is therefore not justified. Measuring error due to hydrostatic pressure can be eliminated by using a zero point compensator. Such a compensator that permits vertical changes in the position of the patient without the need for manual resetting of the zero point has now been developed. However, the clinical test showed that the advantages offered by the zero point compensator again do not justify the increased cost. Furthermore, lateral changes in the patient's position during its use can lead to erroneous measurements.

Artifacts↗

PubMed1984

Scene-segmentation algorithm development using error measures.

Development of scene-segmentation algorithms has generally been an ad hoc process. This paper presents a systematic technique for developing these algorithms using error-measure minimization. If scene segmentation is regarded as a problem of pixel classification whereby each pixel of a scene is assigned to a particular object class, development of a scene-segmentation algorithm becomes primarily a process of feature selection. In this study, four methods of feature selection were used to develop segmentation techniques for cervical cytology images: (1) random selection, (2) manual selection (best features in the subjective judgment of the investigator), (3) eigenvector selection (ranking features according to the largest contribution to each eigenvector of the feature covariance matrix) and (4) selection using the scene-segmentation error measure A2. Four features were selected by each method from a universe of 35 features consisting of gray level, color, texture and special pixel neighborhood features in 40 cervical cytology images . Evaluation of the results was done with a composite of the scene-segmentation error measure A2, which depends on the percentage of scenes with measurable error, the agreement of pixel class proportions, the agreement of number of objects for each pixel class and the distance of each misclassified pixel to the nearest pixel of the misclassified class. Results indicate that random and eigenvector feature selection were the poorest methods, manual feature selection somewhat better and error-measure feature selection best. The error-measure feature selection method provides a useful, systematic method of developing and evaluating scene-segmentation algorithms.

Computers↗

PubMed2004

Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models.

Line transect sampling is one of the most widely used methods for animal abundance assessment. Standard estimation methods assume certain detection on the transect, no animal movement, and no measurement errors. Failure of the assumptions can cause substantial bias. In this work, the effect of error measurement on line transect estimators is investigated. Based on considerations of the process generating the errors, a multiplicative error model is presented and a simple way of correcting estimates based on knowledge of the error distribution is proposed. Using beta models for the error distribution, the effect of errors and of the proposed correction is assessed by simulation. Adequate confidence intervals for the corrected estimates are obtained using a bootstrap variance estimate for the correction and the delta method. As noted by Chen (1998, Biometrics 54, 899-908), even unbiased estimators of the distances might lead to biased density estimators, depending on the actual error distribution. In contrast with the findings of Chen, who used an additive model, unbiased estimation of distances, given a multiplicative model, lead to overestimation of density. Some error distributions result in observed distance distributions that make efficient estimation impossible, by removing the shoulder present in the original detection function. This indicates the need to improve field methods to reduce measurement error. An application of the new methods to a real data set is presented.

Animals↗

PubMed1998

Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists.

OBJECTIVES: This paper describes 2 statistical methods designed to correct for bias from exposure measurement error in point and interval estimates of relative risk. METHODS: The first method takes the usual point and interval estimates of the log relative risk obtained from logistic regression and corrects them for nondifferential measurement error using an exposure measurement error model estimated from validation data. The second, likelihood-based method fits an arbitrary measurement error model suitable for the data at hand and then derives the model for the outcome of interest. RESULTS: Data from Valanis and colleagues' study of the health effects of antineoplastics exposure among hospital pharmacists were used to estimate the prevalence ratio of fever in the previous 3 months from this exposure. For an interdecile increase in weekly number of drugs mixed, the prevalence ratio, adjusted for confounding, changed from 1.06 to 1.17 (95% confidence interval [CI] = 1.04, 1.26) after correction for exposure measurement error. CONCLUSIONS: Exposure measurement error is often an important source of bias in public health research. Methods are available to correct such biases.

Antineoplastic Agents↗

PubMed2000

A simulation study of measurement error correction methods in logistic regression.

Measurement error models in logistic regression have received considerable theoretical interest over the past 10-15 years. In this paper, we present the results of a simulation study that compares four estimation methods: the so-called regression calibration method, probit maximum likelihood as an approximation to the logistic maximum likelihood, the exact maximum likelihood method based on a logistic model, and the naive estimator, which is the result of simply ignoring the fact that some of the explanatory variables are measured with error. We have compared the behavior of these methods in a simple, additive measurement error model. We show that, in this situation, the regression calibration method is a very good alternative to more mathematically sophisticated methods.

Calibration↗

PubMed2000

Exposure measurement error in time-series studies of air pollution: concepts and consequences.

Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them.

Air Pollution↗

PubMed2005

Determining sexual dimorphism in frog measurement data: integration of statistical significance, measurement error, effect size and biological significance.

Several analytic techniques have been used to determine sexual dimorphism in vertebrate morphological measurement data with no emergent consensus on which technique is superior. A further confounding problem for frog data is the existence of considerable measurement error. To determine dimorphism, we examine a single hypothesis (Ho = equal means) for two groups (females and males). We demonstrate that frog measurement data meet assumptions for clearly defined statistical hypothesis testing with statistical linear models rather than those of exploratory multivariate techniques such as principal components, correlation or correspondence analysis. In order to distinguish biological from statistical significance of hypotheses, we propose a new protocol that incorporates measurement error and effect size. Measurement error is evaluated with a novel measurement error index. Effect size, widely used in the behavioral sciences and in meta-analysis studies in biology, proves to be the most useful single metric to evaluate whether statistically significant results are biologically meaningful. Definitions for a range of small, medium, and large effect sizes specifically for frog measurement data are provided. Examples with measurement data for species of the frog genus Leptodactylus are presented. The new protocol is recommended not only to evaluate sexual dimorphism for frog data but for any animal measurement data for which the measurement error index and observed or a priori effect sizes can be calculated.

Analysis of Variance↗

PubMed2005

On corrected score approach for proportional hazards model with covariate measurement error.

In the presence of covariate measurement error with the proportional hazards model, several functional modeling methods have been proposed. These include the conditional score estimator (Tsiatis and Davidian, 2001, Biometrika 88, 447-458), the parametric correction estimator (Nakamura, 1992, Biometrics 48, 829-838), and the nonparametric correction estimator (Huang and Wang, 2000, Journal of the American Statistical Association 95, 1209-1219) in the order of weaker assumptions on the error. Although they are all consistent, each suffers from potential difficulties with small samples and substantial measurement error. In this article, upon noting that the conditional score and parametric correction estimators are asymptotically equivalent in the case of normal error, we investigate their relative finite sample performance and discover that the former is superior. This finding motivates a general refinement approach to parametric and nonparametric correction methods. The refined correction estimators are asymptotically equivalent to their standard counterparts, but have improved numerical properties and perform better when the standard estimates do not exist or are outliers. Simulation results and application to an HIV clinical trial are presented.

Acquired Immunodeficiency Syndrome↗

PubMed1997

Effects of biomarker measurement error on epidemiological studies.

This chapter presents an overview of how measurement error in a biomarker affects epidemiological studies which use the biomarker. To estimate the effects of biomarker error, one must first measure the error using an appropriate validity or reliability study design and using appropriate parameters, i.e. parameters that are informative about the effects of measurement error on the 'parent' epidemiological study. These measures of the biomarker measurement error from the validity or reliability study can then be applied to what is known about the association under study in the parent study, in order to estimate the effects of the biomarker error on the results of the epidemiological study.

Bias↗

PubMed1998

Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine.

Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.

Bias↗

PubMed1989

Analysis of case-control data with covariate measurement error: application to diet and colon cancer.

We propose a method for estimating odds ratios from case-control data in which covariates are subject to measurement error. The measurement error may contain both a random component and a systematic difference between cases and controls (recall bias). A multivariate normal discriminant analysis model is assumed. If the distribution of measurement error is known, then a simple correction to naive (biased) estimates of odds ratios from logistic regression of disease on fallible measurements of covariates removes bias. The same correction yields confidence intervals and significance tests. We apply the proposed methods to data from a case-control study of colon cancer and diet.

Analysis of Variance↗

PubMed1995

Effects of correlated and uncorrelated measurement error on linear regression and correlation in medical method comparison studies.

It is well known that when uncorrelated measurement error affects both variables in linear regression, there is attenuation of the correlation coefficient and regression slope. The effect of correlated measurement error, however, has received little attention. In medical method comparison studies, such error correlation results from the presence of other, unknown explanatory variables that affect the results of the new test method and the reference test method to which it is being compared. The contribution of correlated measurement error to the observed correlation coefficient can be accounted for by the expression rho t1t2 = rho1 rho2 + rho E1E2 (1-rho2(1))1/2(1-rho 2(2))1/2 where rho t1t2 is the observed correlation between tests 1 and 2, rho 1 and rho 2 are the correlation with true values for tests 1 and 2, respectively, and rho E1E2 is the correlation between the test errors. The first term describes the attenuation due to uncorrelated error, the second term describes the effect of correlated error. A positive correlation between the measurement errors reduces the attenuation of observed correlation and slope, but, when the reference method is excellent, the effect is very small. For poorer reference tests whose correlations with true values are less than 0.9, however, error correlation may result in a slope and correlation coefficient that differ importantly from the values obtained with either uncorrelated error or with no reference test error. Negatively correlated measurement errors magnify the attenuation of slope and correlation. One might suspect the presence of correlated error when the observed regression slope is close to or exceeds 1 and the reference test is known to have suboptimal reliability. This paper provides several clinical examples of potentially correlated diagnostic methods.

Cardiac Output↗

PubMed1998

Measurement error in dietary data: implications for the epidemiologic study of the diet-disease relationship.

OBJECTIVES: To examine the effect of measurement error in dietary data on the relationship between diet and body mass index (BMI). To correct for the effect of measurement error on diet-BMI association by using replicate measurements of diet. The effect of measurement error on diet--BMI relationship was simulated, and its implications are discussed. DESIGN: Prospective study design. SETTING: The first and second China Health and Nutrition Survey conducted in 1989 and 1991, respectively. SUBJECTS: Three thousand, four hundred and seventy-nine adults age 20-45 y at the 1989 survey. METHODS: Statistical methods were used to demonstrate the effect of measurement error in dietary data on the diet-BMI association. RESULTS: By using the average of three replicate 24 h dietary recalls, the attenuation of diet-BMI association was reduced substantially. The regression coefficients of fat and energy intakes differed markedly from those computed by using only single measurement of diet. CONCLUSIONS: Measurement error in dietary data may significantly attenuate the diet-disease association. Where appropriate, specific emphasis may be needed to address the problem of measurement error in the study of diet-disease relationship.

Adult↗