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Results for “measurement error”

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PubMedDate not supplied

Measurement error in air pollution exposure assessment.

The exposure of an individual to an air pollutant can be assessed indirectly, with a "microenvironmental" approach, or directly with a personal sampler. Both methods of assessment are subject to measurement error, which can cause considerable bias in estimates of health effects. If the exposure estimates are unbiased and the measurement error is nondifferential, the bias in a linear model can be corrected when the variance of the measurement error is known. Unless the measurement error is quite large, estimates of health effects based on individual exposures appear to be more accurate than those based on ambient levels.

Air Pollutants↗

PubMed2002

Measurement error affects risk estimates for recruitment to the Hudson River stock of striped bass.

We examined the consequences of ignoring the distinction between measurement error and natural variability in an assessment of risk to the Hudson River stock of striped bass posed by entrainment at the Bowline Point, Indian Point, and Roseton power plants. Risk was defined as the probability that recruitment of age-1+ striped bass would decline by 80% or more, relative to the equilibrium value, at least once during the time periods examined (1, 5, 10, and 15 years). Measurement error, estimated using two abundance indices from independent beach seine surveys conducted on the Hudson River, accounted for 50% of the variability in one index and 56% of the variability in the other. If a measurement error of 50% was ignored and all of the variability in abundance was attributed to natural causes, the risk that recruitment of age-1+ striped bass would decline by 80% or more after 15 years was 0.308 at the current level of entrainment mortality (11%). However, the risk decreased almost tenfold (0.032) if a measurement error of 50% was considered. The change in risk attributable to decreasing the entrainment mortality rate from 11 to 0% was very small (0.009) and similar in magnitude to the change in risk associated with an action proposed in Amendment #5 to the Interstate Fishery Management Plan for Atlantic striped bass (0.006)--an increase in the instantaneous fishing mortality rate from 0.33 to 0.4. The proposed increase in fishing mortality was not considered an adverse environmental impact, which suggests that potentially costly efforts to reduce entrainment mortality on the Hudson River stock of striped bass are not warranted.

Age Factors↗

PubMed1989

[Coverage error measurement of the Canadian census].

"This paper describes the methods used by Statistics Canada to measure coverage errors in the Canadian Census." Consideration is given to data collection and analysis and to estimation techniques. (SUMMARY IN ENG AND SPA)

Americas↗

PubMedDate not supplied

Error measures for objective assessment of scene segmentation algorithms.

Scene segmentation is an important element in pattern recognition problems. Previous efforts to evaluate and compare scene segmentation procedures have been largely subjective. Quantitative error measures would facilitate objective comparison of scene segmentation algorithms. A theoretical discussion leading to a new generalized quantitative error measure, G2, based on comparison of both pixel class proportions and spatial distributions of "true" and test segmentations, is presented. This error measure was tested on 14 manual segmentations and 40 gynecologic cytology specimens segmented with five different scene segmentation techniques. Results indicate that G2 seems to have the desirable properties of correlation with human observation, categorization of error allowing for weighting, invariance with picture size and ease of computation necessary for a useful scene segmentation error measure.

Autoanalysis↗

PubMed1997

The correction of risk estimates for measurement error.

PURPOSE: The methods available for the correction of risk estimates for measurement errors are reviewed. The assumptions and design implications of each of the following six methods are noted: linear imputation, absolute limits, maximum likelihood, latent class, discriminant analysis and Gibbs sampling. METHODS: All methods, with the exception of the absolute limits approach, require either repeated determinations on the same subjects with use of the methods that are prone to error or a validation study, in which the measurement is performed for a number of persons with use of both the error-prone method and a more accurate method regarded as a "gold standard". RESULTS: The maximum likelihood, latent class and absolute limits methods are most suitable for purely discrete risk factors. The linear imputation methods and the closely related discrimination analysis method are suitable for continuous risk factors which, together with the errors of measurement, are usually assumed to be normally distributed. CONCLUSIONS: The Gibbs sampling approach is, in principle, useful for both discrete and continuous risk factors and measurement errors, although its use does mandate that the user specify models and dependencies that may be very complex. Also, the Bayesian approach implicit in the use of Gibbs sampling is difficult to apply to the design of the case-control study.

Bayes Theorem↗

PubMed2006

Measurement error of DXA: interpretation of fat and lean mass changes in obese and non-obese children.

Information on reproducibility of dual-energy X-ray absorptiometry (DXA) measurements is essential because DXA is frequently used by clinicians and researchers to assess body composition changes. We estimated measurement error and absolute and relative smallest detectable differences (SDDs) for fat, lean, and bone mass in children. The SDD is the change necessary to be confident that the change is not a consequence of measurement error. Duplicate whole body DXA (Hologic QDR 4500A, Hologic Inc., Waltham, MA) scans were obtained on 32 obese and 34 non-obese children ages 6-19 yr. Absolute (kg) and relative (coefficient of variation) measurement error and SDD were calculated. Absolute SDDs for fat and lean were higher for obese (1.39 and 1.30 kg, respectively) than for non-obese children (0.42 and 0.47 kg, respectively). The %SDD for fat was lower for obese (3.58%) than non-obese children (5.24%), but for lean the %SDD was higher for obese (2.60%) than non-obese children (1.32%). The SDDs for bone mass were similar for obese and non-obese children. An obese child must lose or gain more absolute fat and lean mass than a non-obese child to be confident that the change is not a reflection of measurement error. Overall, SDD values for fat, lean, and bone mass are low.

Absorptiometry, Photon↗

PubMed1997

Effect of measurement error on energy-adjustment models in nutritional epidemiology.

The use and interpretation of energy-adjustment regression models in nutritional epidemiology has been vigorously debated recently. There has been little discussion, however, regarding the effect of dietary measurement error on the performance of such models. Contrary to conventional assumptions invoked in the standard treatment of the effect of measurement error in regression analysis, reporting errors in dietary studies are usually biased, correlated with true nutrient intakes and with each other, heteroscedastic, and nonnormally distributed. Methods developed in this paper allow for this more complex error structure and are therefore more appropriate for dietary data. For practical illustration, these methods are applied to data from the Women's Health Trial Vanguard Study. The results demonstrate considerable shrinkage in the magnitude of the estimated main exposure effect in energy-adjustment models due to attenuation of the true effect and contamination from the effect of an adjusting covariate. In most cases, this shrinkage causes a sharply reduced statistical power of the corresponding significance test in comparison with measurement without error. These results emphasize the need to understand the measurement error properties of dietary instruments through validation/calibration studies and, where possible, to correct for the impact of measurement error when applying energy-adjustment models.

Aged↗

PubMed2002

A regression model for longitudinal change in the presence of measurement error.

PURPOSE: The analysis of change in measured variables has become quite popular in studies where data are collected repeatedly over time. The authors describe some of the potential pitfalls in the analysis of change when the variable for change is measured with error. They show that regression analysis is often biased, possibly leading to erroneous results. METHODS: A simple method to correct for measurement error bias in regression models that model change is presented. RESULTS AND CONCLUSIONS: The two examples illustrate how measurement error can adversely affect an analysis. The bias-corrected approach yields valid results.

Aged↗

PubMed1984

Fluctuating dental asymmetry and measurement error.

Differences between the dimensions of left and right molar teeth are analyzed in the context of measurement error while considering the power of the F-Ratio. Molar teeth from two Nubian cemeteries, 24-S-46 and 21-R-2, excavated at Kulubnarti, Sudan, were initially measured, and then remeasured to obtain data for assessing fluctuating dental asymmetry and measurement error. As has been demonstrated by other investigators, the power of the F-Ratio for detecting small, but real differences, that might exist between antimeric dimensions when sample sizes are small, as they usually are with archeological materials, limits interpretation. In addition, an analysis of measurement error indicates that such error can have a confounding effect upon interpretations of the degree of fluctuating dental asymmetry that exists even in very large samples that exceed the size of typical archeological samples.

Humans↗

PubMed2001

Measurement error correction in nutritional epidemiology based on individual foods, with application to the relation of diet to breast cancer.

Nutrient intake is often measured with error by commonly used dietary instruments such as the food frequency questionnaire (FFQ) or 24-hour recall. More accurate assessments of true intake are obtained by using weighed diet records, in which subjects record what they eat on a real-time basis, but these records are expensive to administer. Validation studies are often performed to relate "gold standard" intake to intake according to surrogate instruments and to correct relative risk estimates obtained in the main study for measurement error. Most measurement error correction methods use validation study data at the nutrient level. However, subjects almost always report intake at the food rather than the nutrient level. In addition, the validity of measurement of different foods can vary considerably; it is relatively high for some foods (e.g., beverages) but relatively low for others (e.g., meats, vegetables). This differential validity could be incorporated into measurement error methods and potentially improve on nutrient-based measurement error methods. In this paper, the authors discuss correction methods for food-based measurement error and apply them to study the relation between FFQ intake in 1980 and incident breast cancer in 1980-1994 among approximately 89,000 women in the Nurses' Health Study, in whom approximately 3,000 incident breast cancers were observed.

Adult↗

PubMed2003

Alcohol consumption, beverage prices and measurement error.

OBJECTIVE: Alcohol price data collected by the American Chamber of Commerce Researchers Association (ACCRA) have been widely used in studies of alcohol consumption and related behaviors. A number of problems with these data suggest that they contain substantial measurement error, which biases conventional statistical estimators toward a finding of little or no effect of prices on behavior. We test for measurement error, assess the magnitude of the bias and provide an alternative estimator that is likely to be superior. METHOD: The study utilizes data on per capita alcohol consumption across U.S. states and the years 1982-1997. State and federal alcohol taxes are used as instrumental variables for prices. RESULTS: Formal tests strongly confim the hypothesis of measurement error. Instrumental variable estimates of the price elasticity of demand range from -0.53 to -1.24. These estimates are substantially larger in absolute value than ordinary least squares estimates, which sometimes are not significantly different from zero or even positive. CONCLUSIONS: The ACCRA price data are substantially contaminated with measurement error, but using state and federal taxes as instrumental variables mitigates the problem.

Adolescent↗

PubMed2003

Consequences of exposure measurement error for confounder identification in environmental epidemiology.

Non-differential measurement error in the exposure variable is known to attenuate the dose-response relationship. The amount of attenuation introduced in a given situation is not only a function of the precision of the exposure measurement but also depends on the conditional variance of the true exposure given the other independent variables. In addition, confounder effects may also be affected by the exposure measurement error. These difficulties in statistical model development are illustrated by examples from a epidemiological study performed in the Faroe Islands to investigate the adverse health effects of prenatal mercury exposure.

Bias↗

PubMed1993

Conditional independence models for epidemiological studies with covariate measurement error.

We construct a unifying representation of the structure of measurement error problems with particular reference to situations commonly encountered in epidemiological studies, and outline how estimation of the parameters of interest can be carried out in a Bayesian framework using Gibbs sampling. We show how this approach can be implemented for designs involving continuous measurement errors assessed through a validation substudy, and discuss our results on simulated data.

Analysis of Variance↗

PubMed2006

Measurement error of lumbar total disc replacement range of motion.

STUDY DESIGN: A retrospective review of lumbar total disc replacement (TDR) radiographs. OBJECTIVE: To determine the error and variability in measuring TDR radiographic range of motion (ROM). SUMMARY OF BACKGROUND DATA: Motion preservation is the driving force behind lumbar TDR technology. In the recent literature, sagittal radiographic TDR ROM as low as 2 degrees has been reported. In these studies, ROM was determined by using the Cobb method to measure TDR sagittal alignment angles in flexion-extension lateral radiographs. However, previous studies in the spinal deformity literature have shown that the Cobb method is very susceptible to measurement error. METHODS: There were 5 observers, including 2 attending orthopedic spine surgeons, 1 spine fellow, 1 fifth-year resident, and 1 fourth-year resident, who measured the ROM of 50 ProDisc II (Synthes Spine Solutions, New York, NY) TDRs on standard flexion-extension lumbar spine radiograph sets. Repeated measurements were made on 2 occasions using the Cobb method. Measurement variability was calculated using 3 statistical methods. RESULTS: The 3 statistical methods resulted in extremely similar values for TDR ROM observer variability. Overall, the intraobserver variability of TDR ROM measurement was +/-4.6 degrees, and interobserver variability was +/-5.2 degrees . CONCLUSIONS: To be 95% certain that an implanted TDR prosthesis has any sagittal motion, a ROM of at least 4.6 degrees must be observed, which is the upper limit of intraobserver measurement variability for a TDR with a true ROM of 0 degrees. To be 95% certain that a change in TDR ROM has occurred between 2 measurements by the same observer, a change in ROM of at least 9.6 degrees must be observed (the entire range of +/-4.6 degrees intraobserver variability). ROM measurement variability should be considered when evaluating the success or failure of motion preservation in lumbar TDR.

Adult↗

PubMed2007

Interval estimation for rank correlation coefficients based on the probit transformation with extension to measurement error correction of correlated ranked data.

The Spearman (rho(s)) and Kendall (tau) rank correlation coefficient are routinely used as measures of association between non-normally distributed random variables. However, confidence limits for rho(s) are only available under the assumption of bivariate normality and for tau under the assumption of asymptotic normality of tau. In this paper, we introduce another approach for obtaining confidence limits for rho(s) or tau based on the arcsin transformation of sample probit score correlations. This approach is shown to be applicable for an arbitrary bivariate distribution. The arcsin-based estimators for rho(s) and tau (denoted by rho(s,a), tau(a)) are shown to have asymptotic relative efficiency (ARE) of 9/pi2 compared with the usual estimators rho(s) and tau when rho(s) and tau are, respectively, 0. In some nutritional applications, the Spearman rank correlation between nutrient intake as assessed by a reference instrument versus nutrient intake as assessed by a surrogate instrument is used as a measure of validity of the surrogate instrument. However, if only a single replicate (or a few replicates) are available for the reference instrument, then the estimated Spearman rank correlation will be downwardly biased due to measurement error. In this paper, we use the probit transformation as a tool for specifying an ANOVA-type model for replicate ranked data resulting in a point and interval estimate of a measurement error corrected rank correlation. This extends previous work by Rosner and Willett for obtaining point and interval estimates of measurement error corrected Pearson correlations.

Confidence Intervals↗

PubMed2006

Probe-level measurement error improves accuracy in detecting differential gene expression.

MOTIVATION: Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes. RESULTS: We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression. AVAILABILITY: The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.

Algorithms↗

PubMed1992

Measurement errors in epidemiological studies of human papillomavirus and cervical cancer.

Measurement errors have been an important concern in studies of human papillomavirus (HPV) and anogenital cancers. Misclassification of HPV infection status is a possible explanation for incoherent findings in previous epidemiological studies purporting to show an etiological role for HPV in cervical cancer. Even low levels of misclassification of HPV infection can cause severe underestimation of HPV prevalence in field surveys, bias the association between HPV and sexual activity, and impair the ability to control statistically the relation between sexual activity and neoplasia by viral status. The present report focuses on aspects that have not been elaborated in two previous studies (Kaldor, 1989; Franco, 1991). Emphasis is given to effects of measurement errors of HPV infection status in biasing the association with cervical neoplasia under more complex scenarios, e.g., differential misclassification between cases and controls in a case-control study, and non-differential misclassification of both viral infection and cervical neoplasia in cross-sectional cytology surveys and cohort studies. Some simple numeric formulae are given that allow the correction of prevalence rates and epidemiological measures of effect, such as the odds ratio and the relative risk, under the latter conditions of misclassification constraint. These formulae have been used to correct estimates from recent epidemiological studies using hypothetical misclassification scenarios in order to obtain clues on the magnitude of the underlying relationship between HPV and cervical cancer.

Bias↗

PubMed1994

Biological variability and measurement error variability in ocular biometry in samoyed dogs.

A-scan ultrasonography was performed in 40 healthy Samoyeds aged 2-5 years. Mydriasis and cycloplegia were induced in all dogs and 25 of the dogs were also sedated prior to the ultrasound examination. Five consecutive A-scans were taken on each eye and the intrasubject variance (measurement error) and true intersubject variance (true biological variation) were computed. The true biological variation was found to be of the same magnitude in both sedated and unsedated dogs, whereas the measurement errors were considerably larger in the group of unsedated dogs compared to the sedated ones. Magnitudes of the measurement errors for the anterior chamber depth and length of the vitreous body in the unsedated group were unacceptable. The sizes of the measurement errors in the sedated dogs were all within an acceptable range.

Animals↗