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Measurement Error Correction (measurement + error_correction)

Distribution by Scientific Domains

Mathematics and Statistics	100%

Selected Abstracts

A Flexible Approach to Measurement Error Correction in Case,Control Studies

BIOMETRICS, Issue 4 2008
A. Guolo
Summary We investigate the use of prospective likelihood methods to analyze retrospective case,control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case,control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case,control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case,control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer. [source]

Occupational exposure to methyl tertiary butyl ether in relation to key health symptom prevalence: the effect of measurement error correction

ENVIRONMETRICS, Issue 6 2003
Aparna P. Keshaviah
Abstract In 1995, White et al. reported that methyl tertiary butyl ether (MTBE), an oxygenate added to gasoline, was significantly associated with key health symptoms, including headaches, eye irritation, and burning of the nose and throat, among 44 people occupationally exposed to the compound and for whom serum MTBE measurements were available (odds ratio (OR),=,8.9, 95% CI,=,[1.2, 75.6]). However, these serum MTBE measurements were available for only 29 per cent of the 150 subjects enrolled. Around the same time, Mannino et al. conducted a similar study among individuals occupationally exposed to low levels of MTBE and did not find a significant association between exposure to MTBE and the presence of one or more key health symptoms among the 264 study participants (OR,=,0.60, 95% CI,=,[0.3, 1.21]). In this article, we evaluate the effect of MTBE on the prevalence of key health symptoms by applying a regression calibration method to White et al.'s and Mannino et al.'s data. Unlike White et al., who classified exposure using actual MTBE levels among a subset of the participants, and Mannino et al., who classified exposure based on job category among all participants, we use all of the available data to obtain an estimate of the effect of MTBE in units of serum concentration, adjusted for measurement error due to using job category instead of measured exposure. After adjusting for age, gender and smoking status, MTBE exposure was found to be significantly associated with a 50 per cent increase in the prevalence of one or more key health symptoms per order of magnitude increase in blood concentration on the log10 scale, using data from the 409 study participants with complete information on the covariates (95% CI,=,[1.00, 2.25]). Simulation results indicated that under conditions similar to those observed in these data, the estimator is unbiased and has a coverage probability close to the nominal value. The methodology illustrated in this article is advantageous because all of the available data were used in the analysis, obtaining a more precise estimate of exposure effect on health outcome, and the estimate is adjusted for measurement error due to using job category instead of measured exposure. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes

BIOMETRICS, Issue 4 2009
Victor Kipnis
Summary Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006,,Journal of the American Dietetic Association,106, 1575,1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food's usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America's Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context. [source]

Haplotype-Based Regression Analysis and Inference of Case,Control Studies with Unphased Genotypes and Measurement Errors in Environmental Exposures

BIOMETRICS, Issue 3 2008
Iryna Lobach
Summary It is widely believed that risks of many complex diseases are determined by genetic susceptibilities, environmental exposures, and their interaction. Chatterjee and Carroll (2005, Biometrika92, 399,418) developed an efficient retrospective maximum-likelihood method for analysis of case,control studies that exploits an assumption of gene,environment independence and leaves the distribution of the environmental covariates to be completely nonparametric. Spinka, Carroll, and Chatterjee (2005, Genetic Epidemiology29, 108,127) extended this approach to studies where certain types of genetic information, such as haplotype phases, may be missing on some subjects. We further extend this approach to situations when some of the environmental exposures are measured with error. Using a polychotomous logistic regression model, we allow disease status to have K+ 1 levels. We propose use of a pseudolikelihood and a related EM algorithm for parameter estimation. We prove consistency and derive the resulting asymptotic covariance matrix of parameter estimates when the variance of the measurement error is known and when it is estimated using replications. Inferences with measurement error corrections are complicated by the fact that the Wald test often behaves poorly in the presence of large amounts of measurement error. The likelihood-ratio (LR) techniques are known to be a good alternative. However, the LR tests are not technically correct in this setting because the likelihood function is based on an incorrect model, i.e., a prospective model in a retrospective sampling scheme. We corrected standard asymptotic results to account for the fact that the LR test is based on a likelihood-type function. The performance of the proposed method is illustrated using simulation studies emphasizing the case when genetic information is in the form of haplotypes and missing data arises from haplotype-phase ambiguity. An application of our method is illustrated using a population-based case,control study of the association between calcium intake and the risk of colorectal adenoma. [source]