scholarly journals Measurement error modeling and nutritional epidemiology association analyses

2011 ◽  
pp. n/a-n/a
Author(s):  
Ross L. Prentice ◽  
Ying Huang
Keyword(s):  
Measurement Error ◽  
Error Modeling ◽  
Nutritional Epidemiology ◽  
Association Analyses

Characterization of a PMU-based method for transmission line parameters estimation with systematic measurement error modeling

2021 ◽  
Author(s):  
Carlo Muscas ◽  
Paolo Attilio Pegoraro ◽  
Carlo Sitzia ◽  
Antonio Vincenzo Solinas ◽  
Sara Sulis ◽  
...  
Keyword(s):  
Measurement Error ◽  
Transmission Line ◽  
Error Modeling ◽  
Parameters Estimation ◽  
Systematic Measurement ◽  
Systematic Measurement Error

scholarly journals Impact of Exposure Measurement Error in Nutritional Epidemiology

2008 ◽  
Vol 100 (23) ◽  
pp. 1658-1659 ◽  
Author(s):  
Victor Kipnis ◽  
Laurence S. Freedman
Keyword(s):  
Measurement Error ◽  
Nutritional Epidemiology ◽  
Exposure Measurement ◽  
Exposure Measurement Error

scholarly journals Structured Measurement Error in Nutritional Epidemiology

2007 ◽  
Vol 102 (479) ◽  
pp. 856-866 ◽  
Author(s):  
Brent A Johnson ◽  
Amy H Herring ◽  
Joseph G Ibrahim ◽  
Anna Maria Siega-Riz
Keyword(s):  
Measurement Error ◽  
Nutritional Epidemiology

Seemingly Unrelated Measurement Error Models, with Application to Nutritional Epidemiology

Biometrics ◽  
2005 ◽  
Vol 62 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Raymond J. Carroll ◽  
Douglas Midthune ◽  
Laurence S. Freedman ◽  
Victor Kipnis
Keyword(s):  
Measurement Error ◽  
Nutritional Epidemiology ◽  
Error Models ◽  
Measurement Error Models

scholarly journals Rarefaction, alpha diversity, and statistics

2017 ◽  
Author(s):  
Amy Willis
Keyword(s):  
Measurement Error ◽  
Microbial Diversity ◽  
Microbial Ecology ◽  
Ecosystem Function ◽  
Unknown Parameter ◽  
Alpha Diversity ◽  
Fundamental Question ◽  
Error Modeling ◽  
Extensive Literature ◽  
Statistical Methodology

AbstractUnderstanding the drivers of microbial diversity is a fundamental question in microbial ecology. Extensive literature discusses different methods for describing microbial diversity and documenting its effects on ecosystem function. However, it is widely believed that diversity depends on the number of reads that are sequenced. I discuss a statistical perspective on diversity, framing the diversity of an environment as an unknown parameter, and discussing the bias and variance of plug-in and rarefied estimates. I argue that by failing to account for both bias and variance, we invalidate analysis of alpha diversity. I describe the state of the statistical literature for addressing these problems, and suggest that measurement error modeling can address issues with variance, but bias corrections need to be utilized as well. I encourage microbial ecologists to avoid motivating their investigations with alpha diversity analyses that do not use valid statistical methodology.

scholarly journals Estimation of Eosinophil Cells in Cord Blood with References Based on Blood in Adults via Bayesian Measurement Error Modeling

2019 ◽  
Author(s):  
Yu Jiang ◽  
Hongmei Zhang ◽  
Shan V Andrews ◽  
Hasan Arshad ◽  
Susan Ewart ◽  
...  
Keyword(s):  
Measurement Error ◽  
Cord Blood ◽  
Blood Cells ◽  
White Blood Cells ◽  
Error Modeling ◽  
Supplementary Information ◽  
Estimation Accuracy ◽  
Bioconductor Package ◽  
High Quality ◽  
Eosinophil Cell

Abstract Motivation Eosinophils are phagocytic white blood cells with a variety of roles in the immune system. In situations where actual counts are not available, high quality approximations of their cell proportions using indirect markers are critical. Results We develop a Bayesian measurement error model to estimate proportions of eosinophils in cord blood, using the cord blood DNA methylation profiles, based on markers of eosinophil cell heterogeneity in blood of adults. The proposed method can be directly extended to other cells across different reference panels. We demonstrate the method’s estimation accuracy using B cells and show that the findings support the proposed approach. The method has been incorporated into the estimateCellCounts function in the minfi package to estimate eosinophil cells proportions in cord blood. Availability estimateCellCounts function is implemented and available in Bioconductor package minfi. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Semiparametric Bayesian measurement error modeling

2010 ◽  
Vol 101 (3) ◽  
pp. 512-524 ◽  
Author(s):  
María P. Casanova ◽  
Pilar Iglesias ◽  
Heleno Bolfarine ◽  
Victor H. Salinas ◽  
Alexis Peña
Keyword(s):  
Measurement Error ◽  
Error Modeling

scholarly journals Biomarkers in nutritional epidemiology

2002 ◽  
Vol 5 (6a) ◽  
pp. 821-827 ◽  
Author(s):  
Sheila A Bingham
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Disease Risk ◽  
Dietary Assessment ◽  
Nutritional Epidemiology ◽  
Significant Development ◽  
Gene Environment ◽  
Human Volunteers ◽  
Individual Disease ◽  
Scientific Hypotheses

AbstractObjective:To illustrate biomarkers of diet that can be used to validate estimates of dietary intake in the study of gene–environment interactions in complex diseases.Design:Prospective cohort studies, studies of biomarkers where diet is carefully controlled.Setting:Free–living individuals, volunteers in metabolic suites.Subjects:Male and female human volunteers.Results:Recent studies using biomarkers have demonstrated substantial differences in the extent of measurement error from those derived by comparison with other methods of dietary assessment. The interaction between nutritional and genetic factors has so far largely gone uninvestigated, but can be studied in epidemiological trials that include collections of biological material. Large sample sizes are required to study interactions, and these are made larger in the presence of measurement errors.Conclusions:Diet is of key importance in affecting the risk of most chronic diseases in man. Nutritional epidemiology provides the only direct approach to the quantification of risks. The introduction of biomarkers to calibrate the measurement error in dietary reports, and as additional measures of exposure, is a significant development in the effort to improve estimates of the magnitude of the contribution of diet in affecting individual disease risk within populations. The extent of measurement error has important implications for correction for regression dilution and for sample size. The collection of biological samples to improve and validate estimates of exposure, enhance the pursuit of scientific hypotheses, and enable gene–nutrient interactions to be studied, should become the routine in nutritional epidemiology.

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