Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros.

We consider measurement error models for two variables observed repeatedly and subject to measurement error. One variable is continuous, while the other variable is a mixture of continuous and zero measurements. This second variable has two sources of zeros. The first source is episodic zeros, wherein some of the measurements for an individual may be zero and others positive. The second source is hard zeros, i.e., some individuals will always report zero. An example is the consumption of alcohol from alcoholic beverages: some individuals consume alcoholic beverages episodically, while others never consume alcoholic beverages. However, with a small number of repeat measurements from individuals, it is not possible to determine those who are episodic zeros and those who are hard zeros. We develop a new measurement error model for this problem, and use Bayesian methods to fit it. Simulations and data analyses are used to illustrate our methods. Extensions to parametric models and survival analysis are discussed briefly.

Can sodium and potassium measured in timed voids be used as reference instruments for validating self-report instruments? Results from a urine calibration study.

BACKGROUND: Sodium and potassium measured in 24-h urine collections are often used as reference measurements to validate self-reported dietary intake instruments. OBJECTIVES: To evaluate whether collection and analysis of a limited number of urine voids at specified times during the day ("timed voids") can provide alternative reference measurements, and to identify their optimal number and timing. METHODS: We used data from a urine calibration study among 441 adults aged 18-39 y. Participants collected each urine void in a separate container for 24 h and recorded the collection time. For the same day, they reported dietary intake using a 24-h recall. Urinary sodium and potassium were analyzed in a 24-h composite sample and in 4 timed voids (morning, afternoon, evening, and overnight). Linear regression models were used to develop equations predicting log-transformed 24-h urinary sodium or potassium levels using each of the 4 single timed voids, 6 pairs, and 4 triples. The equations also included age, sex, race, BMI (kg/m2), and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-h levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-h dietary recall. These estimates were compared. RESULTS: Optimal combinations found were as follows: single voids-evening; paired voids-afternoon + overnight (sodium) and morning + evening (potassium); and triple voids-morning + evening + overnight (sodium) and morning + afternoon + evening (potassium). Predicted 24-h urinary levels estimated 24-h recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-h urinary levels. To recover lost precision, it was estimated that sample sizes need to be increased by ∼2.6-2.7 times for a single void, 1.7-2.1 times for paired voids, and 1.5-1.6 times for triple voids. CONCLUSIONS: Our results provide the basis for further development of new reference biomarkers based on timed voids. CLINICAL TRIAL REGISTRY: clinicaltrials.gov as NCT01631240.

Developing the POTOMAC Model: A Novel Prediction Model to Study the Impact of Lymphopenia Kinetics on Survival Outcomes in Head and Neck Cancer Via an Ensemble Tree-Based Machine Learning Approach.

PURPOSE: Lymphopenia is associated with poor survival outcomes in head and neck squamous cell carcinoma (HNSCC), yet there is no consensus on whether we should limit lymphopenia risks during treatment. To fully elucidate the prognostic role of baseline versus treatment-related lymphopenia, a robust analysis is necessary to investigate the relative importance of various lymphopenia metrics (LMs) in predicting survival outcomes. METHODS: In this prospective cohort study, 363 patients were eligible for analysis (patients with newly diagnosed, nonmetastatic HNSCC treated with neck radiation with or without chemotherapy in 2015-2019). Data were acquired on 28 covariates: seven baseline, five disease, seven treatment, and nine LMs, including static and time-varying features for absolute lymphocyte count (ALC), neutrophil-to-lymphocyte ratio, and immature granulocytes (IGs). IGs were included, given their hypothesized role in inhibiting lymphocyte function. Overall, there were 4.0% missing data. Median follow-up was 2.9 years. We developed a model (POTOMAC) to predict survival outcomes using a random survival forest (RSF) procedure. RSF uses an ensemble approach to reduce the risk of overfitting and provides internal validation of the model using data that are not used in model development. The ability to predict survival risk was assessed using the AUC for the predicted risk score. RESULTS: POTOMAC predicted 2-year survival with AUCs at 0.78 for overall survival (primary end point) and 0.73 for progression-free survival (secondary end point). Top modifiable risk factors included radiation dose and max ALC decrease. Top baseline risk factors included age, Charlson Comorbidity Index, Karnofsky Performance Score, and baseline IGs. Top-ranking LMs had superior prognostic performance when compared with human papillomavirus status, chemotherapy type, and dose (up to 2, 8, and 65 times higher in variable importance score). CONCLUSION: POTOMAC provides important insights into potential approaches to reduce mortality in patients with HNSCC treated by chemoradiation but needs to be validated in future studies.

Issues in Implementing Regression Calibration Analyses.

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

Urinary Sucrose and Fructose From Spot Urine May Be Used as a Predictive Biomarker of Total Sugar Intake-Findings From a Controlled Feeding Study.

BACKGROUND: Recently, we confirmed 24-h urinary sucrose plus fructose (24 uSF) as a predictive biomarker of total sugar intake. However, the collection of 24-h urine samples has limited feasibility in population studies. OBJECTIVE: We investigated the utility of the urinary sucrose plus fructose (uSF) biomarker measured in spot urine as a measure of 24 uSF biomarker and total sugar intake. METHODS: Hundred participants, 18-70 y of age, from the Phoenix Metropolitan Area completed a 15-d feeding study. For 2 of the 8 collected 24-h urine samples, each spot urine sample was collected in a separate container. We considered 4 timed voids of the day [morning (AM) void: first void 08:30-12:30; afternoon (PM) void: first void 12:31-17:30; evening (EVE) void: first void 17:31-12:00; and next-day (ND) void: first void 04:00-12:00]. We investigated the performance of uSF from 1 void, and uSF combined from 2 and 3 voids as a measure of 24 uSF and sugar intake. RESULTS: The biomarker averaged from PM/EVE void strongly correlated with 24 uSF (partial r = 0.75). The 24 uSF predicted from the PM/EVE combination was significantly associated with observed sugar intake and was selected for building the calibrated biomarker equation (marginal R2 = 0.36). Spot urine-based calibrated biomarker, ie, biomarker-estimated sugar intake was moderately correlated with the 15-d mean-observed sugar intake (r = 0.50). CONCLUSIONS: uSF measured from a PM and EVE void may be used to generate biomarker-based sugar intake estimate when collecting 24-h urine samples is not feasible, pending external validation.

Evaluating a Model of Added Sugar Intake Based on Amino Acid Carbon Isotope Ratios in a Controlled Feeding Study of U.S. Adults.

Previous studies suggest that amino acid carbon stable isotope ratios (CIRAAs) may serve as biomarkers of added sugar (AS) intake, but this has not been tested in a demographically diverse population. We conducted a 15-day feeding study of U.S. adults, recruited across sex, age, and BMI groups. Participants consumed personalized diets that resembled habitual intake, assessed using two consecutive 7-day food records. We measured serum (n = 99) CIRAAs collected at the end of the feeding period and determined correlations with diet. We used forward selection to model AS intake using participant characteristics and 15 CIRAAs. This model was internally validated using bootstrap optimism correction. Median (25th, 75th percentile) AS intake was 65.2 g/day (44.7, 81.4) and 9.5% (7.2%, 12.4%) of energy. The CIR of alanine had the highest, although modest, correlation with AS intake (r = 0.32, p = 0.001). Serum CIRAAs were more highly correlated with animal food intakes, especially the ratio of animal to total protein. The AS model included sex, body weight and 6 CIRAAs. This model had modest explanatory power (multiple R2 = 0.38), and the optimism-corrected R2 was lower (R2 = 0.15). Further investigations in populations with wider ranges of AS intake are warranted.

Establishing 24-Hour Urinary Sucrose Plus Fructose as a Predictive Biomarker for Total Sugars Intake.

BACKGROUND: Twenty-four-hour urinary sucrose and fructose (24uSF) has been studied as a biomarker of total sugars intake in two feeding studies conducted in the United Kingdom (UK) and Arizona (AZ). We compare the biomarker performance in these populations, testing whether it meets the criteria for a predictive biomarker. METHODS: The UK and AZ feeding studies included 13 and 98 participants, respectively, aged 18 to 70 years, consuming their usual diet under controlled conditions. Linear mixed models relating 24uSF to total sugars and personal characteristics were developed in each study and compared. The AZ calibrated biomarker equation was applied to generate biomarker-estimated total sugars intake in UK participants. Stability of the model across AZ study subpopulations was also examined. RESULTS: Model coefficients were similar between the two studies [e.g., log(total sugars): UK 0.99, AZ 1.03, P = 0.67], as was the ratio of calibrated biomarker person-specific bias to between-person variance (UK 0.32, AZ 0.25, P = 0.68). The AZ equation estimated UK log(total sugar intakes) with mean squared prediction error of 0.27, similar to the AZ study estimate (0.28). Within the AZ study, the regression coefficients of log(total sugars) were similar across age, gender, and body mass index subpopulations. CONCLUSIONS: Similar model coefficients in the two studies and good prediction of UK sugar intakes by the AZ equation suggest that 24uSF meets the criteria for a predictive biomarker. Testing the biomarker performance in other populations is advisable. IMPACT: Applications of the 24uSF biomarker will enable improved assessment of the role of sugars intake in risk of chronic disease, including cancer. See related commentary by Prentice, p. 1151.

Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error.

Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45-86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.

An evaluation of the serum carbon isotope ratio as a candidate predictive biomarker of the dietary animal protein ratio (animal protein/total protein) in a 15-day controlled feeding study of US adults.

BACKGROUND: The serum natural abundance carbon isotope ratio (CIR) was recently identified as a candidate biomarker of animal protein intake in postmenopausal women. Such a biomarker would help clarify the relation between dietary protein source (plant or animal) and chronic disease risk. OBJECTIVES: We aimed to evaluate the performance of the serum CIR as a biomarker of dietary protein source in a controlled feeding study of men and women of diverse age and BMI. METHODS: We conducted a 15-d feeding study of 100 adults (age: 18-70 y, 55% women) in Phoenix, AZ. Participants were provided individualized diets that approximated habitual food intakes. Serum was collected at the end of the feeding period for biomarker measurements. RESULTS: Median [IQR] animal protein intake was 67 g/d [55-88 g/d], which was 64% of total protein. The serum CIR was positively correlated with animal protein and inversely correlated with plant protein intake, leading to a strong correlation (r2 = 0.76) with the dietary animal protein ratio (APR; animal/total protein). Regressing serum CIR on the APR, serum nitrogen isotope ratio (NIR), gender, age, and body weight generated an R2 of 0.78. Following the measurement error model for predictive biomarkers, the resulting regression equation was then inverted to develop a calibrated biomarker equation for APR. Added sugars ratio (added/total sugars intake) and corn intakes also influenced the serum CIR but to a much lesser degree than the APR; variations in these intakes had only small effects on biomarker-estimated APR. CONCLUSIONS: Based on our findings in this US cohort of mixed sex and age, we propose the serum CIR alongside NIR as a predictive dietary biomarker of the APR. We anticipate using this biomarker to generate calibrated estimates based on self-reported intake and ultimately to obtain more precise disease risk estimates according to dietary protein source.

Investigating the performance of 24-h urinary sucrose and fructose as a biomarker of total sugars intake in US participants - a controlled feeding study.

BACKGROUND: Developing approaches for the objective assessment of sugars intake in population research is crucial for generating reliable disease risk estimates, and evidence-based dietary guidelines. Twenty-four-hour urinary sucrose and fructose (24uSF) was developed as a predictive biomarker of total sugars intake based on 3 UK feeding studies, yet its performance as a biomarker of total sugars among US participants is unknown. OBJECTIVES: To investigate the performance of 24uSF as a biomarker of sugars intake among US participants, and to characterize its use. METHODS: Ninety-eight participants, aged 18-70 y, consumed their usual diet under controlled conditions of a feeding study for 15 d, and collected 8 nonconsecutive 24-h urines measured for sucrose and fructose. RESULTS: A linear mixed model regressing log 24uSF biomarker on log total sugars intake along with other covariates explained 56% of the biomarker variance. Total sugars intake was the strongest predictor in the model (Marginal R2 = 0.52; P <0.0001), followed by sex (P = 0.0002) and log age (P = 0.002). The equation was then inverted to solve for total sugars intake, thus generating a calibrated biomarker equation. Calibration of the biomarker produced mean biomarker-based log total sugars of 4.79 (SD = 0.59), which was similar to the observed log 15-d mean total sugars intake of 4.69 (0.35). The correlation between calibrated biomarker and usual total sugars intake was 0.59 for the calibrated biomarker based on a single biomarker measurement, and 0.76 based on 4 biomarker repeats spaced far apart. CONCLUSIONS: In this controlled feeding study, total sugars intake was the main determinant of 24uSF confirming its utility as a biomarker of total sugars in this population. Next steps will include validation of stability assumptions of the biomarker calibration equation proposed here, which will allow its use as an instrument for dietary validation and measurement error correction in diet-disease association studies.

Fatigue, anxiety, and quality of life in breast cancer patients compared to non-cancer controls: a nationwide longitudinal analysis.

PURPOSE: Fatigue and anxiety are common and significant symptoms reported by cancer patients. Few studies have examined the trajectory of multidimensional fatigue and anxiety, the relationships between them and with quality of life. METHODS: Breast cancer patients (n = 580) from community oncology clinics and age-matched controls (n = 364) completed fatigue and anxiety questionnaires prior to chemotherapy (A1), at chemotherapy completion (A2), and six months post-chemotherapy (A3). Linear mixed models (LMM) compared trajectories of fatigue /anxiety over time in patients and controls and estimated their relationship with quality of life. Models adjusted for age, education, race, BMI, marital status, menopausal status, and sleep symptoms. RESULTS: Patients reported greater fatigue and anxiety compared to controls at all time points (p's < 0.001, 35% clinically meaningful anxiety at baseline). From A1 to A2 patients experienced a significant increase in fatigue (ß = 8.3 95%CI 6.6,10.0) which returned to A1 values at A3 but remained greater than controls' (p < 0.001). General, mental, and physical fatigue subscales increased from A1 to A2 remaining significantly higher than A1 at A3 (p < 0.001). Anxiety improved over time (A1 to A3 ß = - 4.3 95%CI -2.6,-3.3) but remained higher than controls at A3 (p < 0.001). Among patients, fatigue and anxiety significantly predicted one another and quality of life. Menopausal status, higher BMI, mastectomy, and sleep problems also significantly predicted change in fatigue. CONCLUSION: Breast cancer patients experience significant fatigue and anxiety up to six months post-chemotherapy that is associated with worse quality of life. Future interventions should simultaneously address anxiety and fatigue, focusing on mental and physical fatigue subdomains.

Performance and Feasibility of Recalls Completed Using the Automated Self-Administered 24-Hour Dietary Assessment Tool in Relation to Other Self-Report Tools and Biomarkers in the Interactive Diet and Activity Tracking in AARP (IDATA) Study.

BACKGROUND: Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a self-administered web-based tool designed to collect detailed dietary data at low cost in observational studies. OBJECTIVE: The objectives of this study were to describe, overall and by demographic groups, the performance and feasibility of ASA24-2011 recalls and compare Healthy Eating Index-2015 (HEI-2015) total and component scores to 4-day food records (4DFRs) and food frequency questionnaires (FFQs). DESIGN: Over 12 months, participants completed up to 6 ASA24 recalls, 2 web-based FFQs, and 2 unweighed paper-and-pencil 4DFRs. Up to 3 attempts were made to obtain each ASA24 recall. Participants were administered doubly-labeled water to provide a measure of total energy expenditure and collected two 24-hour urine samples to assess concentrations of nitrogen, sodium, and potassium. PARTICIPANTS/SETTING: From January through September 2012, 1,110 adult members of AARP, 50 to 74 years of age, were recruited from the Pittsburgh, PA, area to participate in the Interactive Diet and Activity Tracking in AARP (IDATA) study. After excluding 33 participants who had not completed any dietary assessments, 531 men and 546 women remained. MAIN OUTCOME MEASURES: Response rates, nutrient intakes compared to recovery biomarkers across each ASA24 administration day, and HEI-2015 total and component scores were measured. STATISTICAL ANALYSES PERFORMED: Means, medians, standard deviations, interquartile ranges, and HEI-2015 total and component scores computed using a multivariate measurement error model are presented. RESULTS: Ninety-one percent of men and 86% of women completed 3 ASA24 recalls. Approximately three-quarters completed 5 or more, higher than the completion rates for 2 4DFRs and 2 FFQs. Approximately, three-quarters of men and 70% of women completed ASA24 on the first attempt; 1 in 5 completed it on the second. Completion rates varied slightly by age and body mass index. Median time to complete ASA24-2011 (current version: ASA24-2020) declined with subsequent recalls from 55 to 41 minutes in men and from 58 to 42 minutes in women and was lowest in those younger than 60 years. Mean nutrient intakes were similar across recalls. For each recording day, energy intakes estimated by ASA24 were lower than energy expenditure. Reported intakes for protein, potassium, and sodium were closer to recovery biomarkers for women, but not for men. Geometric means of reported intakes of these nutrients did not systematically vary across ASA24 administrations, but differences between reported intakes and biomarkers differed by nutrient. Of 100 possible points, HEI-2015 total scores were nearly identical for 4DFRs and ASA24 recalls and higher for FFQs (men: 61, 60, and 68; women: 64, 64, and 72, respectively). CONCLUSIONS: ASA24, a freely available dietary assessment tool for use in large-scale nutrition research, was found to be highly feasible. Similar to previously reported data for nutrient intakes, HEI-2015 total and component scores for ASA24 recalls were comparable to those for 4DFRs, but not FFQs. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03268577 (http://www.clinicaltrials.gov).

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics.

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Epidemiologic analyses with error-prone exposures: review of current practice and recommendations.

PURPOSE: Variables in observational studies are commonly subject to measurement error, but the impact of such errors is frequently ignored. As part of the STRengthening Analytical Thinking for Observational Studies Initiative, a task group on measurement error and misclassification seeks to describe the current practice for acknowledging and addressing measurement error. METHODS: Task group on measurement error and misclassification conducted a literature survey of four types of research studies that are typically impacted by exposure measurement error: (1) dietary intake cohort studies, (2) dietary intake population surveys, (3) physical activity cohort studies, and (4) air pollution cohort studies. RESULTS: The survey revealed that while researchers were generally aware that measurement error affected their studies, very few adjusted their analysis for the error. Most articles provided incomplete discussion of the potential effects of measurement error on their results. Regression calibration was the most widely used method of adjustment. CONCLUSIONS: Methods to correct for measurement error are available but require additional data regarding the error structure. There is a great need to incorporate such data collection within study designs and improve the analytical approach. Increased efforts by investigators, editors, and reviewers are needed to improve presentation of research when data are subject to error.

Combining a Food Frequency Questionnaire With 24-Hour Recalls to Increase the Precision of Estimation of Usual Dietary Intakes-Evidence From the Validation Studies Pooling Project.

Improving estimates of individuals' dietary intakes is key to obtaining more reliable evidence for diet-health relationships from nutritional cohort studies. One approach to improvement is combining information from different self-report instruments. Previous work evaluated the gains obtained from combining information from a food frequency questionnaire (FFQ) and multiple 24-hour recalls (24HRs), based on assuming that 24HRs provide unbiased measures of individual intakes. Here we evaluate the same approach of combining instruments but base it on the better assumption that recovery biomarkers provide unbiased measures of individual intakes. Our analysis uses data from the 5 large validation studies included in the Validation Studies Pooling Project: the Observing Protein and Energy Nutrition Study (1999-2000), the Automated Multiple-Pass Method validation study (2002-2004), the Energetics Study (2006-2009), the Nutrition Biomarker Study (2004-2005), and the Nutrition and Physical Activity Assessment Study (2007-2009). The data included intakes of energy, protein, potassium, and sodium. Under a time-varying usual-intake model analysis, the combination of an FFQ with 4 24HRs improved correlations with true intake for predicted protein density, potassium density, and sodium density (range, 0.39-0.61) in comparison with use of a single FFQ (range, 0.34-0.50). Absolute increases in correlation ranged from 0.02 to 0.26, depending on nutrient and sex, with an average increase of 0.14. Based on unbiased recovery biomarker evaluation for these nutrients, we confirm that combining an FFQ with multiple 24HRs modestly improves the accuracy of estimates of individual intakes.

Associations of Biomarker-Calibrated Intake of Total Sugars With the Risk of Type 2 Diabetes and Cardiovascular Disease in the Women's Health Initiative Observational Study.

The inconsistent findings from epidemiologic studies relating total sugars (TS) consumption to cardiovascular disease (CVD) or type 2 diabetes (T2D) risk may be partly due to measurement error in self-reported intake. Using regression calibration equations developed based on the predictive biomarker for TS and recovery biomarker for energy, we examined the association of TS with T2D and CVD risk, before and after dietary calibration, in 82,254 postmenopausal women participating in the Women's Health Initiative Observational Study. After up to 16 years of follow-up (1993-2010), 6,621 T2D and 5,802 CVD incident cases were identified. The hazard ratio for T2D per 20% increase in calibrated TS was 0.94 (95% confidence interval (CI): 0.77, 1.15) in multivariable energy substitution, and 1.00 (95% CI: 0.85, 1.18) in energy partition models. Multivariable hazard ratios for total CVD were 0.97 (95% CI: 0.87, 1.09) from energy substitution, and 0.91 (95% CI: 0.80, 1.04) from energy partition models. Uncalibrated TS generated a statistically significant inverse association with T2D and total CVD risk in multivariable energy substitution and energy partition models. The lack of conclusive findings from our calibrated analyses may be due to the low explanatory power of the calibration equations for TS, which could have led to incomplete deattenuation of the risk estimates.

Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers.

Background: A limited number of studies have evaluated self-reported dietary intakes against objective recovery biomarkers. Objective: The aim was to compare dietary intakes of multiple Automated Self-Administered 24-h recalls (ASA24s), 4-d food records (4DFRs), and food-frequency questionnaires (FFQs) against recovery biomarkers and to estimate the prevalence of under- and overreporting. Design: Over 12 mo, 530 men and 545 women, aged 50-74 y, were asked to complete 6 ASA24s (2011 version), 2 unweighed 4DFRs, 2 FFQs, two 24-h urine collections (biomarkers for protein, potassium, and sodium intakes), and 1 administration of doubly labeled water (biomarker for energy intake). Absolute and density-based energy-adjusted nutrient intakes were calculated. The prevalence of under- and overreporting of self-report against biomarkers was estimated. Results: Ninety-two percent of men and 87% of women completed ≥3 ASA24s (mean ASA24s completed: 5.4 and 5.1 for men and women, respectively). Absolute intakes of energy, protein, potassium, and sodium assessed by all self-reported instruments were systematically lower than those from recovery biomarkers, with underreporting greater for energy than for other nutrients. On average, compared with the energy biomarker, intake was underestimated by 15-17% on ASA24s, 18-21% on 4DFRs, and 29-34% on FFQs. Underreporting was more prevalent on FFQs than on ASA24s and 4DFRs and among obese individuals. Mean protein and sodium densities on ASA24s, 4DFRs, and FFQs were similar to biomarker values, but potassium density on FFQs was 26-40% higher, leading to a substantial increase in the prevalence of overreporting compared with absolute potassium intake. Conclusions: Although misreporting is present in all self-report dietary assessment tools, multiple ASA24s and a 4DFR provided the best estimates of absolute dietary intakes for these few nutrients and outperformed FFQs. Energy adjustment improved estimates from FFQs for protein and sodium but not for potassium. The ASA24, which now can be used to collect both recalls and records, is a feasible means to collect dietary data for nutrition research.

Categorizing a continuous predictor subject to measurement error.

Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categorical model and interpret the categorical parameters. We address the question: with measurement error and categorization, how can we do what epidemiologists want, namely to estimate the parameters of the categorical model that would have been estimated if the true predictor was observed? We develop a general methodology for such an analysis, and illustrate it in linear and logistic regression. Simulation studies are presented and the methodology is applied to a nutrition data set. Discussion of alternative approaches is also included.

Evaluation of the 24-Hour Recall as a Reference Instrument for Calibrating Other Self-Report Instruments in Nutritional Cohort Studies: Evidence From the Validation Studies Pooling Project.

Calibrating dietary self-report instruments is recommended as a way to adjust for measurement error when estimating diet-disease associations. Because biomarkers available for calibration are limited, most investigators use self-reports (e.g., 24-hour recalls (24HRs)) as the reference instrument. We evaluated the performance of 24HRs as reference instruments for calibrating food frequency questionnaires (FFQs), using data from the Validation Studies Pooling Project, comprising 5 large validation studies using recovery biomarkers. Using 24HRs as reference instruments, we estimated attenuation factors, correlations with truth, and calibration equations for FFQ-reported intakes of energy and for protein, potassium, and sodium and their densities, and we compared them with values derived using biomarkers. Based on 24HRs, FFQ attenuation factors were substantially overestimated for energy and sodium intakes, less for protein and potassium, and minimally for nutrient densities. FFQ correlations with truth, based on 24HRs, were substantially overestimated for all dietary components. Calibration equations did not capture dependencies on body mass index. We also compared predicted bias in estimated relative risks adjusted using 24HRs as reference instruments with bias when making no adjustment. In disease models with energy and 1 or more nutrient intakes, predicted bias in estimated nutrient relative risks was reduced on average, but bias in the energy risk coefficient was unchanged.