scholarly journals Combining biomarker and self-reported dietary intake data: A review of the state of the art and an exposition of concepts

2019 ◽  
Vol 29 (2) ◽  
pp. 617-635 ◽  
Author(s):  
Isobel Claire Gormley ◽  
Yuxin Bai ◽  
Lorraine Brennan
Keyword(s):  
Measurement Error ◽  
Dietary Intake ◽  
Measurement Errors ◽  
State Of The Art ◽  
Calibration Method ◽  
Simulation Studies ◽  
Dietary Biomarkers ◽  
Dietary Intake Assessment ◽  
Reported Data ◽  
Further Development

Classical approaches to assessing dietary intake are associated with measurement error. In an effort to address inherent measurement error in dietary self-reported data there is increased interest in the use of dietary biomarkers as objective measures of intake. Furthermore, there is a growing consensus of the need to combine dietary biomarker data with self-reported data. A review of state of the art techniques employed when combining biomarker and self-reported data is conducted. Two predominant methods, the calibration method and the method of triads, emerge as relevant techniques used when combining biomarker and self-reported data to account for measurement errors in dietary intake assessment. Both methods crucially assume measurement error independence. To expose and understand the performance of these methods in a range of realistic settings, their underpinning statistical concepts are unified and delineated, and thorough simulation studies are conducted. Results show that violation of the methods' assumptions negatively impacts resulting inference but that this impact is mitigated when the variation of the biomarker around the true intake is small. Thus there is much scope for the further development of biomarkers and models in tandem to achieve the ultimate goal of accurately assessing dietary intake.

scholarly journals Bayesian Estimation Analysis of Bernoulli Measurement Error Model for Longitudinal Data

2020 ◽  
Vol 10 (4) ◽  
pp. 160-166
Author(s):  
Dewang Li ◽  
◽  
Meilan Qiu ◽  
Zhongyi Ke
Keyword(s):  
Measurement Error ◽  
Longitudinal Data ◽  
Bayesian Statistics ◽  
Measurement Errors ◽  
Bayesian Method ◽  
Error Model ◽  
Simulation Studies ◽  
Unknown Parameters ◽  
Measurement Error Model ◽  
Mh Algorithm

The Bayesian method is used to study the inference of the semi-parametric measurement error model (MEs) with longitudinal data. A semi-parametric Bayesian method combined with fracture prior and Gibbs sampling combined with Metropolis-Hastings (MH) algorithm is applied and applied to the simulation observation from the posterior distribution, and the combined Bayesian statistics of unknown parameters and measurement errors are obtained. We obtained Bayesian estimates of the parameters and covariates of the measurement error model. Under three different priori assumptions, four simulation studies illustrate the effectiveness and utility of the proposed method.

scholarly journals Plasma alkylresorcinol concentrations, biomarkers of whole-grain wheat and rye intake, in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort

2014 ◽  
Vol 111 (10) ◽  
pp. 1881-1890 ◽  
Author(s):  
Cecilie Kyrø ◽  
Anja Olsen ◽  
H. B(as). Bueno-de-Mesquita ◽  
Guri Skeie ◽  
Steffen Loft ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Geometric Mean ◽  
Plasma Concentrations ◽  
European Countries ◽  
Whole Grain ◽  
Dietary Biomarkers ◽  
Mediterranean Countries ◽  
The Mediterranean ◽  
Dietary Intake Assessment ◽  
Prospective Investigation

Whole-grain intake has been reported to be associated with a lower risk of several lifestyle-related diseases such as type 2 diabetes, CVD and some types of cancers. As measurement errors in self-reported whole-grain intake assessments can be substantial, dietary biomarkers are relevant to be used as complementary tools for dietary intake assessment. Alkylresorcinols (AR) are phenolic lipids found almost exclusively in whole-grain wheat and rye products among the commonly consumed foods and are considered as valid biomarkers of the intake of these products. In the present study, we analysed the plasma concentrations of five AR homologues in 2845 participants from ten European countries from a nested case–control study in the European Prospective Investigation into Cancer and Nutrition. High concentrations of plasma total AR were found in participants from Scandinavia and Central Europe and lower concentrations in those from the Mediterranean countries. The geometric mean plasma total AR concentrations were between 35 and 41 nmol/l in samples drawn from fasting participants in the Central European and Scandinavian countries and below 23 nmol/l in those of participants from the Mediterranean countries. The whole-grain source (wheat or rye) could be determined using the ratio of two of the homologues. The main source was wheat in Greece, Italy, the Netherlands and the UK, whereas rye was also consumed in considerable amounts in Germany, Denmark and Sweden. The present study demonstrates a considerable variation in the plasma concentrations of total AR and concentrations of AR homologues across ten European countries, reflecting both quantitative and qualitative differences in the intake of whole-grain wheat and rye.

Benefits from Computerized Adaptive Testing as Seen in Simulation Studies

1999 ◽  
Vol 15 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Lutz F. Hornke
Keyword(s):  
Measurement Error ◽  
Computerized Adaptive Testing ◽  
Test Procedure ◽  
Adaptive Testing ◽  
Parameter Estimates ◽  
Simulation Studies ◽  
Computerized Adaptive Test ◽  
Item Banks ◽  
Item Parameters ◽  
General Reliability

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.

Features of accumulation of amounts of measurement error

2017 ◽  
Vol 928 (10) ◽  
pp. 58-63 ◽  
Author(s):  
V.I. Salnikov
Keyword(s):  
Measurement Error ◽  
Confidence Interval ◽  
Normal Distribution ◽  
Confidence Level ◽  
Measurement Errors ◽  
Truncated Normal Distribution ◽  
Truncated Normal ◽  
The Law ◽  
Normal Law

The initial subject for study are consistent sums of the measurement errors. It is assumed that the latter are subject to the normal law, but with the limitation on the value of the marginal error Δpred = 2m. It is known that each amount ni corresponding to a confidence interval, which provides the value of the sum, is equal to zero. The paradox is that the probability of such an event is zero; therefore, it is impossible to determine the value ni of where the sum becomes zero. The article proposes to consider the event consisting in the fact that some amount of error will change value within 2m limits with a confidence level of 0,954. Within the group all the sums have a limit error. These tolerances are proposed to use for the discrepancies in geodesy instead of 2m*SQL(ni). The concept of “the law of the truncated normal distribution with Δpred = 2m” is suggested to be introduced.

scholarly journals Dietary Intake Assessment of Pre-Packed Graviera Cheese in Greece and Nutritional Characterization Using the Nutri-Score Front of Pack Label Scheme

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 295
Author(s):  
Evangelia Katsouri ◽  
Emmanuella Magriplis ◽  
Antonis Zampelas ◽  
Eleftherios H. Drosinos ◽  
George-John Nychas
Keyword(s):  
Dietary Intake ◽  
Salt Intake ◽  
Saturated Fat ◽  
Nutrient Contents ◽  
Intake Assessment ◽  
Dark Orange ◽  
Nutrients Intake ◽  
Total Fat ◽  
Dark Green ◽  
Dietary Intake Assessment

Gravieras are ‘gruyere’ type hard cheeses with a variety of different products and the second highest consumption in Greece. In this study, we present a dietary intake assessment and a nutritional characterization of pre-packed graviera products sold in the Greek market using Nutri-Score Front of Pack Label (FoPL). The nutrient contents of 92 pre-packed graviera products were combined with daily individual consumption data extracted from the Hellenic National Nutrition Health Survey (n = 93), attempting to evaluate the contribution of graviera’s consumption to the Greek diet. The analysis of nutrients’ intake as a Reference Intake (RI) percentage ranked saturated fat first on the nutrients’ intake list, with RI percentage ranging from 36.1 to 109.2% for the 95th percentile of consumption. The respective % RI for energy, total fat, carbohydrates, sugars, proteins and salt ranged from 12.7–20.7%, 21.6–50.4%, 0–3.1%, 0–6.1%, 37–57.1% and 6.3–42%. Nutri-Score classified 1% of the products to C—light orange class, 62% to D—orange and 37% to E—dark orange, while no products were classified to A—dark green or B—green classes. The comparison between the Nutri-Score classification and the nutrients’ intake assessment, also separately conducted within the classes, showed a higher salt intake after the consumption of products classified as D—orange and E—dark orange.

scholarly journals ON THE UNIFORM CONVERGENCE OF DECONVOLUTION ESTIMATORS FROM REPEATED MEASUREMENTS

2021 ◽  
pp. 1-22
Author(s):  
Daisuke Kurisu ◽  
Taisuke Otsu
Keyword(s):  
Multivariate Analysis ◽  
Measurement Error ◽  
Uniform Convergence ◽  
Measurement Errors ◽  
Convergence Rates ◽  
Free Variable ◽  
Error Model ◽  
Repeated Measurements ◽  
Empirical Characteristic Function ◽  
Measurement Error Model

This paper studies the uniform convergence rates of Li and Vuong’s (1998, Journal of Multivariate Analysis 65, 139–165; hereafter LV) nonparametric deconvolution estimator and its regularized version by Comte and Kappus (2015, Journal of Multivariate Analysis 140, 31–46) for the classical measurement error model, where repeated noisy measurements on the error-free variable of interest are available. In contrast to LV, our assumptions allow unbounded supports for the error-free variable and measurement errors. Compared to Bonhomme and Robin (2010, Review of Economic Studies 77, 491–533) specialized to the measurement error model, our assumptions do not require existence of the moment generating functions of the square and product of repeated measurements. Furthermore, by utilizing a maximal inequality for the multivariate normalized empirical characteristic function process, we derive uniform convergence rates that are faster than the ones derived in these papers under such weaker conditions.

scholarly journals A Benchmark and Evaluation of Non-Rigid Structure from Motion

2020 ◽  
Author(s):  
Sebastian Hoppe Nesgaard Jensen ◽  
Mads Emil Brix Doest ◽  
Henrik Aanæs ◽  
Alessio Del Bue
Keyword(s):  
Computer Vision ◽  
Structure From Motion ◽  
State Of The Art ◽  
The State ◽  
Quality Data ◽  
Data Set ◽  
Rigid Structure ◽  
Public Data ◽  
3D Information ◽  
Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

Guidelines for choosing volume equations in the presence of measurement error in height

2000 ◽  
Vol 30 (2) ◽  
pp. 306-310 ◽  
Author(s):  
M S Williams ◽  
H T Schreuder
Keyword(s):  
Measurement Error ◽  
Tree Species ◽  
Measurement Errors ◽  
Tree Diameter ◽  
Total Height ◽  
Volume Equations

Assuming volume equations with multiplicative errors, we derive simple conditions for determining when measurement error in total height is large enough that only using tree diameter, rather than both diameter and height, is more reliable for predicting tree volumes. Based on data for different tree species of excurrent form, we conclude that measurement errors up to ±40% of the true height can be tolerated before inclusion of estimated height in volume prediction is no longer warranted.

scholarly journals An improved computational method to assess pituitary responsiveness to secretagogue stimuli

2002 ◽  
pp. 323-332 ◽  
Author(s):  
A Sartorio ◽  
G De Nicolao ◽  
D Liberati
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Pituitary Hormones ◽  
Computational Method ◽  
Kinetics Parameters ◽  
Thyrotrophin Releasing Hormone ◽  
Deconvolution Analysis ◽  
Releasing Hormone ◽  
Sampling Protocols ◽  
Peak Value

OBJECTIVE: The quantitative assessment of gland responsiveness to exogenous stimuli is typically carried out using the peak value of the hormone concentrations in plasma, the area under its curve (AUC), or through deconvolution analysis. However, none of these methods is satisfactory, due to either sensitivity to measurement errors or various sources of bias. The objective was to introduce and validate an easy-to-compute responsiveness index, robust in the face of measurement errors and interindividual variability of kinetics parameters. DESIGN: The new method has been tested on responsiveness tests for the six pituitary hormones (using GH-releasing hormone, thyrotrophin-releasing hormone, gonadotrophin-releasing hormone and corticotrophin-releasing hormone as secretagogues), for a total of 174 tests. Hormone concentrations were assayed in six to eight samples between -30 min and 120 min from the stimulus. METHODS: An easy-to-compute direct formula has been worked out to assess the 'stimulated AUC', that is the part of the AUC of the response curve depending on the stimulus, as opposed to pre- and post-stimulus spontaneous secretion. The weights of the formula have been reported for the six pituitary hormones and some popular sampling protocols. RESULTS AND CONCLUSIONS: The new index is less sensitive to measurement error than the peak value. Moreover, it provides results that cannot be obtained from a simple scaling of either the peak value or the standard AUC. Future studies are needed to show whether the reduced sensitivity to measurement error and the proportionality to the amount of released hormone render the stimulated AUC indeed a valid alternative to the peak value for the diagnosis of the different pathophysiological states, such as, for instance, GH deficits.

Use of simulation–extrapolation estimation in catch–effort analyses

1999 ◽  
Vol 56 (7) ◽  
pp. 1234-1240
Author(s):  
W R Gould ◽  
L A Stefanski ◽  
K H Pollock
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Mean Squared Error ◽  
Estimation Procedure ◽  
Bias Reduction ◽  
Estimation Methods ◽  
Effort Estimation ◽  
Stat Assoc ◽  
General Terms ◽  
Naive Estimator

All catch-effort estimation methods implicitly assume catch and effort are known quantities, whereas in many cases, they have been estimated and are subject to error. We evaluate the application of a simulation-based estimation procedure for measurement error models (J.R. Cook and L.A. Stefanski. 1994. J. Am. Stat. Assoc. 89: 1314-1328) in catch-effort studies. The technique involves a simulation component and an extrapolation step, hence the name SIMEX estimation. We describe SIMEX estimation in general terms and illustrate its use with applications to real and simulated catch and effort data. Correcting for measurement error with SIMEX estimation resulted in population size and catchability coefficient estimates that were substantially less than naive estimates, which ignored measurement errors in some cases. In a simulation of the procedure, we compared estimators from SIMEX with "naive" estimators that ignore measurement errors in catch and effort to determine the ability of SIMEX to produce bias-corrected estimates. The SIMEX estimators were less biased than the naive estimators but in some cases were also more variable. Despite the bias reduction, the SIMEX estimator had a larger mean squared error than the naive estimator for one of two artificial populations studied. However, our results suggest the SIMEX estimator may outperform the naive estimator in terms of bias and precision for larger populations.

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