Random Measurement Error Research Articles

Associations between dietary patterns and cardiovascular disease risk in Canadian adults: a comparison of partial least squares, reduced rank regression and the simplified dietary pattern technique.

ObjectivesWe aimed to compare two data reduction techniques, partial least squares (PLS) and reduced rank regression (RRR), in identifying dietary patterns associated with a high cardiovascular disease (CVD) risk in Canadian adults, and to construct PLS- and RRR-based simplified dietary patterns as well as evaluating associations between derived patterns and CVD risk.
 ApproachData were collected from 24-hour dietary recalls of adult respondents in two cycles of the nationally representative Canadian Community Health Survey (CCHS)-Nutrition: CCHS 2004 linked to health administrative databases (n = 12,313) and CCHS 2015 (n = 14,020). Using 39 food groups, PLS and RRR were applied for the identification of an energy-dense (ED), high-saturated-fat (HSF) and low-fiber-density (LFD) dietary pattern. Associations of derived dietary patterns with lifestyle characteristics and CVD incidence and mortality were examined using weighted multivariate regression and weighted multivariable-adjusted Cox-proportional hazard models, respectively. Random and systematic measurement errors were adjusted for in all statistical analyses.
 ResultsPLS and RRR identified highly similar ED, HSF, LFD dietary patterns with common high positive loadings for fast food, carbonated drinks, salty snacks and solid fats, and high negative loadings for fruit, dark green vegetables, red and orange vegetables, other vegetables, whole grains, legumes and soy (≥|0.17|). Food groups with the highest loadings were summed to form simplified pattern scores. Although the dietary patterns were not significantly associated with CVD risk, they were positively associated with 402 kcal/d higher energy intake (P-trends <0.05) and higher obesity risk [PLS (OR: 2.09; 95% CI: 1.62, 2.7) and RRR (OR: 1.76; 95% CI: 1.44, 2.17)] (P-trends <0.0001) in the fourth quartiles as compared to the first.
 ConclusionPLS and RRR were shown to be equally effective for derivation of a high-CVD-risk dietary pattern among Canadian adults. This research highlights the importance of leveraging linked data to inform public health nutrition policies. Further research is warranted on the role of major dietary components in cardiovascular health.

Hiding or Pleasing: Spousal Disagreement Among Ugandan Maize Farmers

To gain a better understanding of intrahousehold bargaining processes, surveys increasingly collect data from co-heads individually, especially on decision-making, asset ownership and labour contributions. However, answers provided by co-heads to the same set of questions often differ substantially. Recent research suggests that while some of this disagreement is due to random measurement error and cognitive bias, part also reflects non-overlapping information sets. We document differences in answers between male and female co-heads in monogamous smallholder maize-farming households in Uganda. We first confirm that not all disagreement can be explained by measurement error or bias. Using a field experiment, we then test if disagreement is due to information asymmetry between male and female co-heads. We also test an alternative explanation where discord is attributed to co-heads’ tendency to respond in line with prevailing gender norms and social customs. While the interventions did seem to reduce discord in survey response about decision-making, we do not find that information asymmetry nor reporting in line with gender norms and customs are the primary drivers of disagreement.

МІНІМІЗАЦІЯ ВИПАДКОВОЇ ПОХИБКИ ЦИФРОВОГО МЕТОДУ ВИМІРЮВАННЯ АМПЛІТУДИ СИНУСОЇДАЛЬНОГО СИГНАЛУ

The errors of measuring the amplitude of sinusoidal signals by the method based on calculating the sum of the ratios of digital samples of instantaneous signal values to the sines of the phase angles corresponding to the sampling moments are investigated. A mathematical expression for determining the total random measurement error using the values of the parameters of the error components is obtained. It is shown that there is a minimum value of the total random measurement error as a function of the number of instantaneous signal values taken into account. The method of reducing the level of random error to the desired level is given. The method can be used to create metrological support for measuring the parameters of electrical signals at low and infralow frequencies, including industrial frequency. Ref. 7, fig. 4, table.

Errors in Measuring the Distance to an Obstacle by Technical Vision Means and in Forecasting Braking Distance in Driverless Train Control Systems. World of Transport and Transportation

Technical vision systems are sources of information about an obstacle on the track in the case of driverless train control. Based on the information received, the traffic control system decides to turn on the braking mode to prevent a colliosni with an obstacle. In accordance with international and domestic expertise and standard ratings, it is necessary to ensure the probability of a dangerous failure, in this case, the probability of hitting an obstacle, not more than 10-8 with a confidence probability of 0,95 according to SIL-4 ([Russian state standard] GOST-R61508). Considering the presence of an error in measuring the distance to an obstacle by the technical vision system and an error in calculating the stopping distance, it is required to determine the coordinate of the braking start point when an object is detected on the track in such a way as to ensure that the train stops before the obstacle with a probability determined in accordance with SIL-4.A feature of the problem being solved for estimating the errors in measuring the distance to an obstacle and calculating the stopping distance implies the need to determine the estimates of their maximum values and to develop an algorithm for using these estimates in such a way that the collision probability does not exceed the normalised value.A technique is described for determining the maximum value of the error in measuring the distance to the obstacle, the probability of exceeding which is quite small (from 10-2 to 10-6). A proposed algorithm for multiple measurements of the distance to an obstacle allows choosing the minimum measurement result for deciding on the start of braking, which ensures meeting standard indicator of a probability of a train colliding with an obstacle according to SIL-4. A method for estimating the error in calculating the stopping distance has been developed, which, together with the algorithm of multiple measurements by the technical vision system of the distance to the obstacle, provides the standard indicator according to SIL-4. The need for the second channel of technical vision due to the presence of curves along the route is shown. The necessity of using algorithms for multiple measurements to an obstacle through the second channel located outside the train is also substantiated. It is noted that the methods described in this article for choosing the maximum values of random errors in measurements and calculations, the values of which can be exceeded with a very low probability, can be used to solve various applied problems of traffic control in transportation processes.

An Error Prediction Model for Construction Bulk Measurements Using a Customized Low-Cost UAS-LIDAR System

Small unmanned aerial systems (UAS) have been increasingly popular in surveying and mapping tasks. While photogrammetry has been the primary UAS sensing technology in other industries, construction activities can also benefit from accurate surveying measurements from airborne LIDAR. This paper discusses a custom-designed low-cost UAS-based LIDAR system that can effectively measure construction excavation and bulk piles. The system is designed with open interfaces that can be easily upgraded and expanded. An error model was developed to predict the horizontal and vertical errors of single point geo-registration for a generic UAS-LIDAR. This model was validated for the proposed UAS-LIDAR system using calibration targets and real-world measurements from different scenarios. The results indicated random errors from LIDAR at approximately 0.1 m and systematic errors at or below centimeter level. Additional pre-processing of the raw point cloud can further reduce the random errors in LIDAR measurements of bulk piles.

Determination of alpha factors for monitoring of aeration systems with the ex situ off-gas method: experience from practical application and estimation of measurement uncertainty

Performance of aeration systems in wastewater treatment plants (WWTP) under process conditions can be monitored with off-gas tests. The ex situ off-gas method transfers activated sludge from an adjacent aeration tank into aerated columns to determine oxygen transfer parameters (e.g., the α-factor). This method is an alternative to in situ off-gas testing with hoods at the tank surface; however, its application and measurement uncertainty have not been examined yet. We outline our experience from long-term off-gas testing with two pilot-scale test reactors (8.3 m3 volume). Global variance-based sensitivity analysis using Sobol’ indices revealed oxygen concentration in off-gas and dissolved oxygen as the most important input quantities to determine α-factors accurately. Measurement uncertainty of other instruments was negligible. These findings are transferable to in situ off-gas hoods because the methods are similar. Random measurement error of α-factors was estimated with uncertainty analysis and comparison measurements to a relative standard deviation of about ± 2.8% for our ex situ pilot setup. Diffuser fouling, biofilm growth, or sensor drift caused systematic errors avoidable by maintenance. Additional mixing of bubble column due to sludge inflow into ex situ tanks led to a systematic overestimation of α-factors at lower airflow rates. Hence, the ex situ off-gas method is not suitable to determine α-factors for the design of aeration systems but offers unique possibilities for research of oxygen transfer dynamics and development of aeration equipment because ex situ columns can be operated independently from a full-scale activated sludge tank.

Robust binary fringe generation method with defocus adaptability

Current binary defocus technology mainly focuses on fringe generation suitable for specific frequencies without considering fringe adaptability for defocus-degree variation, which decreases the measuring accuracy for this scenario. To achieve a high-quality measurement, we propose a robust binary fringe generation method to minimize the phase error caused by changes in defocus and the random error in measurement. In the method, we establish a complete phase error model and construct a novel objective function, to the best of our knowledge, to optimize the binarization threshold of each pixel. Through derivation of the threshold gradient calculation formula, we quickly obtain optimal binary fringes that can adapt to different fringe pitches and various degrees of defocus. The experimental results verify that the proposed method can generate robust binary fringes adaptive to different fringe pitches and defocus degree variation, and thus achieve high-quality 3D measurements.

Biomarker-Calibrated Red and Combined Red and Processed Meat Intakes with Chronic Disease Risk in a Cohort of Postmenopausal Women

The associations of red and processed meat with chronic disease risk remain to be clarified, in part because of measurement error in self-reported diet. We sought to develop metabolomics-based biomarkers for red and processed meat, and to evaluate associations of biomarker-calibrated meat intake with chronic disease risk among postmenopausal women. Study participants were women who were members of the Women's Health Initiative (WHI) study cohorts. These participants were postmenopausal women aged 50-79 y when enrolled during 1993-1998 at 40 US clinical centers with embedded human feeding and nutrition biomarker studies. Literature reports of metabolomics correlates of meat consumption were used to develop meat intake biomarkers from serum and 24-h urine metabolites in a 153-participant feeding study (2010-2014). Resulting biomarkers were used in a 450-participant biomarker study (2007-2009) to develop linear regression calibration equations that adjust FFQ intakes for random and systematic measurement error. Biomarker-calibrated meat intakes were associated with cardiovascular disease, cancer, and diabetes incidence among 81,954 WHI participants (1993-2020). Biomarkers and calibration equations meeting prespecified criteria were developed for consumption of red meat and red plus processed meat combined, but not for processed meat consumption. Following control for nondietary confounding factors, hazard ratios were calculated for a 40% increment above the red meat median intake for coronary artery disease (HR: 1.10; 95% CI: 1.07, 1.14), heart failure (HR: 1.26; 95% CI: 1.20, 1.33), breast cancer (HR: 1.10; 95% CI: 1.07, 1.13) for, total invasive cancer (HR: 1.07; 95% CI: 1.05, 1.09), and diabetes (HR: 1.37; 95% CI: 1.34, 1.39). HRs for red plus processed meat intake were similar. HRs were close to the null, and mostly nonsignificant following additional control for dietary potential confounding factors, including calibrated total energy consumption. A relatively high-meat dietary pattern is associated with somewhat higher chronic disease risks. These elevations appear to be largely attributable to the dietary pattern, rather than to consumption of red or processed meat per se.

Psychometric Comparisons of Three Depression Measures for Patients With Stroke.

Psychometric examinations for patients with stroke remain insufficient. The Center for Epidemiologic Studies Depression Scale (CES-D), Beck Depression Inventory-II (BDI-II), and Geriatric Depression Scale (GDS) are promising outcome measures. To examine and compare the reliability and validity of three depression measures in a sample of patients with stroke. Repeated-measures design. A hospital in southern Taiwan. Fifty-nine outpatients, who completed three depression measures. Cronbach's α and intraclass correlation coefficients (ICCs) were used to examine the internal consistency and test-retest reliability, respectively, of the three measures. An independent-samples t test was conducted to compare two groups of patients with different levels of disability to investigate discriminative validity. Pearson's rs were calculated among the three measures to examine concurrent validity. The three measures had good internal consistency (α = .85-.92) and sufficient test-retest reliability (ICC = .84-.91). The minimal detectable change (percentage of minimal detectable change) was 10.6 (63.3%), 13.5 (98.3%), and 5.8 (49.9%) for the CES-D, BDI-II, and GDS, respectively. There was a statistically significant difference between the two groups in CES-D score (p = .032) and no significant differences on the other two measures (p = .095-.187). The correlations among the three measures ranged from .79 to .89. All three depression measures had sound internal consistency, test-retest reliability, and concurrent validity in patients with stroke. What This Article Adds: Of the three measures, the CES-D had better discriminative validity, and the GDS demonstrated greater reliability and smaller random measurement error in patients with stroke.

A method to improve accuracy of definition and promptness of forecasting of spacecraft motion parameters

The subject of the presented paper is the methods used to define spacecraft motion parameters. The goal of the paper is to develop approaches to improve accuracy of definition and promptness of forecasting of motion parameters. The tasks are 1) to determine the aspects influencing the result accuracy at defining initial conditions of the spacecraft motion, 2) to reveal the drawbacks of traditional methods and 3) to suggest some possible ways that can be used to improve accuracy and promptness at defining the spacecraft motion parameters. During the research it is revealed that the result accuracy at defining the initial conditions of the spacecraft motion is influenced by three aspects – a random component caused by presence of random errors in trajectory measurements, a dynamic component due to dynamic errors of the used model of the spacecraft motion and the error defined by the size of convergence region of the minimization method that is used to solve the multi-point boundary problem. To receive the least inaccuracy at defining the spacecraft motion parameters it is necessary to process some optimal quantity of measuring information which should ensure sufficient compensation of random errors and at the same time prevent the dynamic error from influencing considerably. At it it should be taken into account that the amount of the measured information should not be less than it is necessary to ensure convergence of the algorithm used to define those motion parameters. The main results are as follows. For solving the task of increasing accuracy of spacecraft motion parameters definition the paper suggests a new method based on minimization with definition of initial approximation region. The suggested method implements a non-local approach to minimization of the goal function, which ensures better as compared to traditional methods convergence. To increase the promptness of spacecraft motion forecasting it is suggested to use the mathematical apparatus of differential transformations. It allows decreasing the computational expenditures 3 to 4 times as compared to the regular ballistic and navigational algorithms while the given accuracy is preserved. On the basis of the performed research the following conclusions can be made. To increase the accuracy of the spacecraft motion parameters definition it is necessary to optimize the required number of measurement orbit passes, for that it is appropriate to use the Nelder-Mead search method. At implementing the advanced coordinate methods of the spacecraft control, to increase the promptness of the spacecraft motion forecasting, it is necessary to considerably decrease the computational expenditures for the motion parameters definition. To achieve that it is reasonable to use the mathematical apparatus of the differential transformations.

The reliability of students’ earnings expectations

The elicitation of subjective expectations in surveys has gained significant interest in economics. Students’ earnings expectations have been widely used to model school and occupational choices, but quantification of the reliability of this variable has not yet been done. To what extent are earnings expectations affected by random measurement error? To assess this issue, in this paper we use different waves of a survey carried out in a Spanish university eliciting the earnings expectations of economics students. A test-retest method is applied with different time spans between the first wave and subsequent repetitions of the survey: two weeks and two and a half months later. A significant number of students declared large differences in earnings expectations, even in the case where the time span between surveys was short. Apart from the reliability, we also provide an estimate of the measurement error variance. With this information we show how a sensitivity analysis can be performed in external earnings expectations data research.

Constrained Bayesian optimization and spatio-temporal surveillance for sensor network design in the presence of measurement errors

ABSTRACT The optimal placement of sensors is studied to construct a surveillance sensor network for a complicated stochastic system with random measurement errors. The problem is formulated as a joint problem of constrained black-box optimization for the fast detection of an anomaly event and spatio-temporal change-point detection for a low false alarm rate. An algorithm is proposed called Confidence-Set based Constrained Bayesian Optimization (CSCBO) that models performance measures as Gaussian Processes (GPs) and provides a flexible and easy-to-implement framework for handling noisy black-box constraints. As the decision variables of this problem are high-dimensional binary variables, the Wasserstein similarity metric is introduced as a distance measure among different solutions to capture the similarity among solutions properly. Finally, a newly proposed detection statistic for spatio-temporal surveillance is combined with CSCBO to identify the optimal sensor placement while controlling the false alarm rate. The combined procedure is applied to the Altamaha River.

A New Three-Dimensional Empirical Reconstruction Model Using a Stochastic Optimization Method

Motivated by MMS mission observations near magnetic reconnection sites, we have developed a new empirical reconstruction (ER) model of the three-dimensional (3D) magnetic field and the associated plasma currents. Our approach combines both the measurements from a constellation of satellites and a set of physics-based equations as physical constraints to build spatially smooth distributions. This ER model directly minimizes the loss function that characterizes the model-measurement differences and the model departures from linear or nonlinear physical constraints using an efficient stochastic optimization method by which the effects of random measurement errors can be effectively included. Depending on the availability of the measured parameters and the adopted physical constraints on the reconstructed fields, the ER model could be either slightly over-determined or under-determined, yielding nearly identical reconstructed fields when solved by the stochastic optimization method. As a result, the ER model remains valid and operational even if the input measurements are incomplete. Two sets of new indices associated respectively with the model-measurement differences and the model departures are introduced to objectively measure the accuracy and quality of the reconstructed fields. While applying the reconstruction model to observations of an electron diffusion region (EDR) observed by NASA’s Magnetospheric Multiscale (MMS) mission, we examine the relative contributions of the errors in the plasma current density arising from random measurement errors and linear approximations made in application of the curlometer technique. It was found that the errors in the plasma current density calculated directly from the measured magnetic fields using a linear approximation were mostly contributed from the nonlinear configuration of the 3D magnetic fields.

Estimation of Angles of Attack and Sideslip of Unmanned Aerial Vehicle in the Absence of Aerodynamic Angle Sensors

A method for estimating aerodynamic angles in the absence of appropriate sensors is proposed, using measurements of three projections of flight speed carried out by the navigation system and the values of the orientation angles. The relevance of the problem being solved is determined by the fact that on unmanned aerial vehicles (UAVs) sensors of aerodynamic angles, that is, angles of attack and slip, are often not installed due to restrictions on dimensions and mass. The proposed method is based on the joint use of mathematical models of aircraft motion, known from flight dynamics, and the theory of parametric identification of dynamic systems. The key factor ensuring the accuracy of the proposed method is the use of very accurate measurements of three UAV velocity projections performed by a satellite navigation system or an inertial navigation system with satellite correction. To account for the influence of wind, parametric identification of three projections of wind speed is provided. Another feature of the method is that instead of the missing aerodynamic angle sensors, it is proposed to use information about the aerodynamic coefficients of the lifting and lateral forces of the UAV. If these coefficients are known with errors, their values are also specified by identification methods. The dimension of the identification problem turns out to be low in the range of small and medium angles of attack when the aerodynamic dependencies are linear. The results of testing the proposed method based on simulation data on the flight test bench of a modern training aircraft for nine different flight modes under conditions of simulating random errors of onboard measurements corresponding to the flight experiment are presented.

Measurement error of tracer-based velocimetry in single-phase turbulent flows with inhomogeneous refractive indices

Inhomogeneous refractive index fields lead to errors in optical flow velocity measurements. Former respective studies are mostly in quasi two-dimensional flows, and attribute the measurement errors to spatial gradients in the refractive index field, while less attention has been paid to flows with three-dimensional refractive index fields which usually change in space and in time. In this study, ray tracing simulations were carried out in a three-dimensional flow, which is from a direct numerical simulation of single-phase turbulent mixing of two fluids. Given the data of the numerical simulation as reference, the ray tracing simulation is used to quantify the measurement errors of the flow velocity and flow acceleration for tracer-based velocimetry, i.e. particle tracking velocimetry in this study. The errors of both flow velocity and flow acceleration are attributed to the spatial and the spatio-temporal gradients of the refractive indices, respectively, which are closely inherited from flow characteristics. While the dominant type of error depends on the studied flow, the main measurement error for the considered turbulent mixing flow is caused by the random error. When the maximum spatial difference of the refractive indices is about 10−6, the relative random measurement error is about 1 % in velocity and about 200 % in acceleration, respectively. When the maximum index difference is about 10−2 (water), the relative random measurement errors of velocity and acceleration are 2000 % and 105 %, respectively, for the flow considered in this study.

Haemodynamic changes during radical nephrectomy with inferior vena cava thrombectomy: A pilot study.

Haemodynamic changes during radical nephrectomy with inferior vena cava thrombectomy: A pilot study.

Test–retest reliabilities and minimal detectable changes of 5 versions of the Alzheimer’s Disease Assessment Scale-Cognitive Subscale in people with dementia

Purpose To compare the test–retest reliability and minimal detectable change (MDC) of the commonly used versions of the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) (the ADAS-Cog-11 (11 items), ADAS-Cog-3 (three items), ADAS-Cog-5-Subset (five items), ADAS-Cog-6-Subset (six items), and ADAS-Rasch (11 items)) in people with dementia. Materials and methods A repeated-assessments design (2 weeks apart) was used to examine the ADAS-Cog-11, ADAS-Cog-3, ADAS-Cog-5-Subset, ADAS-Cog-6-Subset, and ADAS-Rasch. Participants with dementia were recruited from one hospital, one elder care center, and two day-care centers using convenience sampling. Results Fifty-two participants finished the assessments twice in two weeks. All versions showed high intraclass correlation coefficients (ICCs) (0.82–0.96), minimal standardized response means (–0.07 to 0.08) and low to acceptable MDC% (9.2–28.6%). The ADAS-Rasch had the highest ICC (0.96) and the lowest MDC%. The ADAS-Cog-3 had an ICC lower than 0.90 (0.82) and the highest MDC% (28.6%). Conclusions The ADAS-Rasch seems to be the most reliable version of the ADAS-Cog for group- and individual-level comparisons. The ADAS-Cog-3 may be a better choice for researchers for group-level comparisons because it requires fewer items to achieve acceptable reliability. The ADAS-Cog-11, ADAS-Cog-5-Subset, ADAS-Cog-6-Subset, and ADAS-Rasch could be considered for clinical usage for individual-level comparisons. Implications for rehabilitation The ADAS-Rasch is the most reliable version of the ADAS-Cog for group- and individual-level comparisons due to its excellent test–retest reliability, lowest random measurement error and absence of a practice effect. The ADAS-Cog-5-Subset and ADAS-Cog-6-Subset might be good substitutes for the ADAS-Rasch in clinical settings because of their comparable reliability features and superior administration efficiency.

Error analysis and calibration of micro-vibration test platform developed for advanced pointing instrument

This paper builds a micro vibration test platform which is capable to obtain image shift data from an advanced pointing instrument with 1000 Hz sampling frequency and three seconds sampling time. According to the image shift error analysis, system measurement errors brought up by CCDs (Charge-Coupled Device) in form of pixel gray value skewing and those brought up by suspension apparatus locating in frequency band lower than 5 Hz, are able to be eliminated by gray value offset and high-pass filter algorithm respectively. Random measurement errors caused by electronic and mechanical noises are the main factors affecting the measurement accuracy; they obey normal distribution with standard deviation lower than 0.0116 and 0.0170 pixels respectively. The image shift measure accuracy of the test platform is calibrated better than 0.0618 pixels, which meets the demand of 0.08 pixels. The test platform is considered to work well on micro vibration impact assessment with ideal measurement accuracy.

Assessment of Low Back Pain: Reliability and Minimal Detectable Change of the Brief Pain Inventory.

The Brief Pain Inventory (BPI) is one of the most widely used measures to assess pain and related impacts among patients with low back pain (LBP). However, its test-retest reliability and minimal detectable change (MDC) have rarely been examined in patients with LBP, interfering with its utility. To investigate the test-retest reliability and MDC of the BPI among patients with LBP. Repeated assessments design with a 1-wk interval. Department of Physical Medicine and Rehabilitation in a hospital in Taiwan. Fifty-four patients with stable LBP conditions. The BPI has two subscales-Intensity and Interference-that assess pain intensity and pain interference, respectively. Their test-retest reliability was examined using the intraclass correlation coefficient (ICC), and MDCs were calculated. The ICCs of the Intensity and Interference subscales were .62 and .76, respectively. The MDC values for the two subscales were 2.57 and 2.34, respectively. For the four Intensity items, the average-pain score had a higher ICC (.60) than scores on the other items (worst, least, and current pain, which had ICCs of about .40). The results suggest that although the BPI is a commonly used measure of pain intensity and pain interference among patients with LBP, caution should be exercised in interpreting the Intensity subscale score and its item scores. What This Article Adds: The BPI is widely used to assess pain and related impacts on daily occupation and functioning among patients with LBP. This study provides information regarding its test-retest reliability. Moreover, the MDC values provide clinicians and researchers with the thresholds for determining real improvement (beyond random measurement error).

Biomarker-Based Methods and Study Designs to Calibrate Dietary Intake for Assessing Diet–Disease Associations

BackgroundDietary biomarkers measured in biospecimens can play an important role in correcting for random and systematic measurement error in self-reported nutrient intake when assessing diet–disease associations. To date, high-quality biomarkers for calibrating self-reported dietary intake have only been developed for a few nutrients. ObjectivesTo investigate new study designs and regression calibration approaches for calibrating self-reported nutrient intake for use in disease association analyses. MethodsWe studied 3 regression calibration approaches: 1) an existing approach built on a calibration cohort assuming the existence of an objective biomarker (i.e., biomarker with random independent measurement error), 2) a proposed approach using a biomarker development cohort, and 3) a proposed 2-stage approach using both cohorts. We conducted simulation studies to compare the performance of different study designs/methods for estimating diet–disease associations and applied suitable methods to examine the association of sodium and potassium intake with cardiovascular disease (CVD) risk in Women's Health Initiative cohorts. ResultsSimulation studies showed that the first approach can lead to biased association estimation when the objective biomarker assumption is violated; the second and third proposed approaches obviate the need for such an objective biomarker. Precision for estimating the association depends critically on sample size of the biomarker development cohort and the strength of the self-reported nutrient intake. Analyses based on the second and third approaches support previously reported significant findings using the first approach about associations of the ratio of sodium to potassium intake with CVD risk while providing efficiency gain for some outcomes. ConclusionsSelf-reported dietary intake needs to be calibrated for measurement error correction in diet–disease association analyses. When there are no existing objective biomarkers that can be used for calibration purpose, controlled feeding studies can be used to develop new biomarkers for use in calibration or can be used to calibrate self-reported dietary intake directly.