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BACKGROUND/OBJECTIVES: The orally administered para-amino benzoic acid (PABA) is known to have near 100% excretion in urine and is used as a measure of 24-h urine collection completeness (referred to as PABAcheck). The purpose was to examine the effect of including urine collections deemed incomplete based on PABAcheck in a dietary measurement error study. SUBJECTS/METHODS: The Observing Protein and Energy Nutrition (OPEN) study was conducted in 1999-2000 and included 484 men and women aged 40-69 years. A food frequency questionnaire and 24-h dietary recalls were evaluated using recovery biomarkers that included urinary nitrogen and potassium from two 24-h urine collections. Statistical modeling determined the measurement error properties of dietary assessment instruments. In the original analyses, PABAcheck was used as a measure of complete urine collection; incomplete collections were either excluded or adjusted to acceptable levels. The OPEN data were reanalyzed including all urine collections and by using criteria based on self-reported missing voids to assess the differences. RESULTS: Means and coefficients of variation for biomarker-based protein and potassium intakes, and measurement error model-based correlations and attenuation factors were similar regardless of whether PABAcheck or missed voids were considered. CONCLUSION: PABAcheck may not be required in large population-based biomarker studies. However, until there are more analyses evaluating the necessity of a PABAcheck, it is recommended that PABA be given to all participants, but not necessarily analyzed. Then, PABAcheck could be used selectively as a marker of completeness among the collections in which low levels of biomarker are detected or for which noncompliance is suspected. European Journal of Clinical Nutrition (2013) 67, 863-867; doi: 10.1038/ejcn.2013.62; published online 13 March 2013 Keywords: diet; nutrition assessment; urine specimen collection; statistical bias, INTRODUCTION Objective biomarkers are useful to validate self-reported dietary assessment instruments. However, only a few biomarkers, defined as 'recovery' biomarkers, have a consistent relationship with the amount consumed at the [...]

To improve our temporal understanding of the effects of standard CRT in HNCs through analysis of a rich longitudinal database of validated multi-dimensional PROs among HNC patients receiving CRT. All patients receiving CRT for HNCs beginning 12/29/2014 to current were asked to complete a palette of validated PROs before, during and at each follow-up (FU) visit on a secure tablet writing directly to our database (approved by institutional IRB). At baseline and during FU visits, the Sydney Swallow Questionnaire (SSQ), the MD Anderson Dysphagia (MDADI), the FACT-HN QoL and the University of Michigan Xerostomia Questionnaire (XQ) were administered. During CRT, the SSQ, XQ and the Skindex questionnaire were administered weekly. Patient demographics and performance status, HN sub-site, HPV status, use of induction and/or concurrent chemotherapy, and absolute lymphocyte count (ALC) were also collected. Analysis was restricted to treatment start dates between 12/29/2014 and 12/26/2019 due to institutional data safety requirements. We limit this presentation to the SSQ, which reflects patient perceived swallow effectiveness (normal range = 0-250), and the XQ (normalized range = 0 to 100) to understand the post-CRT burden of swallow and xerostomia toxicities. Statistical analyses were conducted in STATA version 18. Longitudinal SSQ and XQ data were available for 420 of 432 patients (97.2% response), yielding a total of 4248 observations with a median duration of 22.2 months (0 to 84 months) from the start of treatment. Peak XQ and SSQ scores were 42 and 624, with the peak values reported an average of 13 months and 6.5 months after treatment initiation, respectively. XQ nadir (14) and SSQ nadir (173) were reported after an average of 14.6 and 8.8 months, respectively. In multivariate linear regression, greater peak SSQ scores were associated with HPV-negative pathology, African American race, and decreasing absolute lymphocyte count. A 1-point decrease in absolute lymphocyte count (ALC) was associated with a 252-point increase in peak SSQ (p<0.05). Additional analysis by HN sub-site and time trends will be reported. Self-reported SSQ and XQ in HNC patients demonstrated swallow dysfunction and xerostomia to be low with normal swallow possible within 12 months. The relationship between decreased ALC due to treatment and SSQ warrants further investigation. [ABSTRACT FROM AUTHOR]

Dietary measurement error creates serious challenges to reliably discovering new diet-disease associations in nutritional cohort studies. Such error causes substantial underestimation of relative risks and reduction of statistical power for detecting associations. On the basis of data from the Observing Protein and Energy Nutrition Study, we recommend the following approaches to deal with these problems. Regarding data analysis of cohort studies using food-frequency questionnaires, we recommend 1) using energy adjustment for relative risk estimation; 2) reporting estimates adjusted for measurement error along with the usual relative risk estimates, whenever possible (this requires data from a relevant, preferably internal, validation study in which participants report intakes using both the main instrument and a more detailed reference instrument such as a 24-hour recall or multiple-day food record); 3) performing statistical adjustment of relative risks, based on such validation data, if they exist, using univariate (only for energy-adjusted intakes such as densities or residuals) or multivariate regression calibration. We note that whereas unadjusted relative risk estimates are biased toward the null value, statistical significance tests of unadjusted relative risk estimates are approximately valid. Regarding study design, we recommend increasing the sample size to remedy loss of power; however, it is important to understand that this will often be an incomplete solution because the attenuated signal may be too small to distinguish from unmeasured confounding in the model relating disease to reported intake. Future work should be devoted to alleviating the problem of signal attenuation, possibly through the use of improved self-report instruments or by combining dietary biomarkers with self-report instruments. [ABSTRACT FROM AUTHOR]

Objective: To develop a method to validate an FFQ for reported intake of episodically consumed foods when the reference instrument measures short-term intake, and to apply the method in a large prospective cohort.Design: The FFQ was evaluated in a sub-study of cohort participants who, in addition to the questionnaire, were asked to complete two non-consecutive 24 h dietary recalls (24HR). FFQ-reported intakes of twenty-nine food groups were analysed using a two-part measurement error model that allows for non-consumption on a given day, using 24HR as a reference instrument under the assumption that 24HR is unbiased for true intake at the individual level.Setting: The National Institutes of Health-AARP Diet and Health Study, a cohort of 567 169 participants living in the USA and aged 50-71 years at baseline in 1995.Subjects: A sub-study of the cohort consisting of 2055 participants.Results: Estimated correlations of true and FFQ-reported energy-adjusted intakes were 0·5 or greater for most of the twenty-nine food groups evaluated, and estimated attenuation factors (a measure of bias in estimated diet-disease associations) were 0·4 or greater for most food groups.Conclusions: The proposed methodology extends the class of foods and nutrients for which an FFQ can be evaluated in studies with short-term reference instruments. Although violations of the assumption that the 24HR is unbiased could be inflating some of the observed correlations and attenuation factors, results suggest that the FFQ is suitable for testing many, but not all, diet-disease hypotheses in a cohort of this size. [ABSTRACT FROM AUTHOR]

The authors compared dietary pattern methods-cluster analysis, factor analysis, and index analysis-with colorectal cancer risk in the National Institutes of Health (NIH)-AARP Diet and Health Study (n = 492,306). Data from a 124-item food frequency questionnaire (1995-1996) were used to identify 4 clusters for men (3 clusters for women), 3 factors, and 4 indexes. Comparisons were made with adjusted relative risks and 95% confidence intervals, distributions of individuals in clusters by quintile of factor and index scores, and health behavior characteristics. During 5 years of follow-up through 2000, 3,110 colorectal cancer cases were ascertained. In men, the vegetables and fruits cluster, the fruits and vegetables factor, the fat-reduced/diet foods factor, and all indexes were associated with reduced risk; the meat and potatoes factor was associated with increased risk. In women, reduced risk was found with the Healthy Eating Index-2005 and increased risk with the meat and potatoes factor. For men, beneficial health characteristics were seen with all fruit/vegetable patterns, diet foods patterns, and indexes, while poorer health characteristics were found with meat patterns. For women, findings were similar except that poorer health characteristics were seen with diet foods patterns. Similarities were found across methods, suggesting basic qualities of healthy diets. Nonetheless, findings vary because each method answers a different question. [ABSTRACT FROM AUTHOR]

Estimating the population distribution of the usual intake of a nutrient relative to that of another nutrient requires determination of individual-level ratios. If intake data are available on a per-day basis, as with 24-h dietary recalls, those ratios can be determined in 1 of 2 ways: as the usual ratio of intakes or the ratio of usual intakes. Each of these ratios has its own meaning and determination; the ratio of usual intakes is conceptually consistent with determinations obtained from FFQ data. We present a method for estimating the ratio of usual intakes that uses bivariate modeling of the 2 nutrient intakes in question. Application of the method to the NHANES data for the years 2001-2004 yielded estimated distributions for percent of usual energy intake from total fat, percent of usual energy intake from saturated fat, and usual sodium intake per 1000 kcal (4184 kJ) of usual energy intake. Distributions for both the total population and for age-gender subgroups were estimated. Approximately 60% of adults (>19 y) had a usual total fat intake that was within the recommended range of 20-35% of total energy, but only approximately 34% had a usual saturated fat intake <10% of total energy. The results changed only minimally when the other definition of usual intake, the usual ratio of intakes, was adopted. [ABSTRACT FROM AUTHOR]

Objective: We evaluated the performance of the food-frequency questionnaire (FFQ) administered to participants in the US NIH-AARP (National Institutes of Health-American Association of Retired Persons) Diet and Health Study, a cohort of 566 404 persons living in the USA and aged 50-71 years at baseline in 1995.Design: The 124-item FFQ was evaluated within a measurement error model using two non-consecutive 24-hour dietary recalls (24HRs) as the reference.Setting: Participants were from six states (California, Florida, Pennsylvania, New Jersey, North Carolina and Louisiana) and two metropolitan areas (Atlanta, Georgia and Detroit, Michigan).Subjects: A subgroup of the cohort consisting of 2053 individuals.Results: For the 26 nutrient constituents examined, estimated correlations with true intake (not energy-adjusted) ranged from 0.22 to 0.67, and attenuation factors ranged from 0.15 to 0.49. When adjusted for reported energy intake, performance improved; estimated correlations with true intake ranged from 0.36 to 0.76, and attenuation factors ranged from 0.24 to 0.68. These results compare favourably with those from other large prospective studies. However, previous biomarker-based studies suggest that, due to correlation of errors in FFQs and self-report reference instruments such as the 24HR, the correlations and attenuation factors observed in most calibration studies, including ours, tend to overestimate FFQ performance.Conclusion: The performance of the FFQ in the NIH-AARP Diet and Health Study, in conjunction with the study's large sample size and wide range of dietary intake, is likely to allow detection of moderate (> or =1.8) relative risks between many energy-adjusted nutrients and common cancers. [ABSTRACT FROM AUTHOR]

Objectives: We describe the methods used to develop and score a 17-item 'screener' designed to estimate intake of fruit and vegetables, percentage energy from fat and fibre. The ability of this screener and a food-frequency questionnaire (FFQ) to measure these exposures is evaluated.Design: Using US national food consumption data, stepwise multiple regression was used to identify the foods to be included on the instrument; multiple regression analysis was used to develop scoring algorithms. The performance of the screener was evaluated in three different studies. Estimates of intakes measured by the screener and the FFQ were compared with true usual intake based on a measurement error model.Setting: US adult population.Subjects: For development of instrument, n=9323 adults. For testing of instrument, adult men and women in three studies completing multiple 24-hour dietary recalls, FFQ and screeners, n=484, 462 and 416, respectively.Results: Median recalled intakes for examined exposures were generally estimated closely by the screener. In the various validation studies, the correlations between screener estimates and estimated true intake were 0.5-0.8. In general, the performances of the screener and the full FFQ were similar; estimates of attenuation were lower for screeners than for full FFQs.Conclusions: When coupled with appropriate reference data, the screener approach described may yield useful estimates of intake, for both surveillance and epidemiological purposes. [ABSTRACT FROM AUTHOR]

This paper describes the Observing Protein and Energy Nutrition (OPEN) Study, conducted from September 1999 to March 2000. The purpose of the study was to assess dietary measurement error using two self-reported dietary instruments-the food frequency questionnaire (FFQ) and the 24-hour dietary recall (24HR)-and unbiased biomarkers of energy and protein intakes: doubly labeled water and urinary nitrogen. Participants were 484 men and women aged 40-69 years from Montgomery County, Maryland. Nine percent of men and 7% of women were defined as underreporters of both energy and protein intake on 24HRs; for FFQs, the comparable values were 35% for men and 23% for women. On average, men underreported energy intake compared with total energy expenditure by 12-14% on 24HRs and 31-36% on FFQs and underreported protein intake compared with a protein biomarker by 11-12% on 24HRs and 30-34% on FFQs. Women underreported energy intake on 24HRs by 16-20% and on FFQs by 34-38% and underreported protein intake by 11-15% on 24HRs and 27-32% on FFQs. There was little underreporting of the percentage of energy from protein for men or women. These findings have important implications for nutritional epidemiology and dietary surveillance. [ABSTRACT FROM AUTHOR]

BACKGROUND: Measurement of fruit and vegetable intake is important in the surveillance of populations and in epidemiologic studies that examine the relations between diet and disease. Some situations require the use of brief dietary assessment tools. OBJECTIVE: Our objective was to evaluate the performance of 2 brief dietary assessment instruments, a 7-item standard screener and a new 16-item screener, and a complete food-frequency questionnaire (FFQ) in measuring total fruit and vegetable consumption. DESIGN: About 800 men and women from the National Institutes of Health-AARP Diet and Health Study completed an FFQ, 1 of the 2 screeners, and two 24-h dietary recalls. Fruit and vegetable intakes as measured by each screener and the FFQ were compared with estimated true usual intake by using a measurement-error model. RESULTS: Median daily servings of fruit and vegetables were underestimated by both screeners. The estimated agreement between true intake and the screener was higher for the new screener than for the standard screener and was higher for women than for men. The estimated agreement between true intake and the FFQ was higher than that for both screeners. Attenuation coefficients for the FFQ and screeners were comparable. CONCLUSIONS: For estimating median intakes of fruit and vegetables and the prevalence of recommended intakes being met, the use of screeners without appropriate adjustment is suboptimal. For estimating relative risks in the relations between fruit and vegetable intake and disease, screeners and this FFQ are similar in performance. Copyright © 2000 American Society for Clinical Nutrition [ABSTRACT FROM AUTHOR]

Data collected at 4 Behavioral Change Consortium sites were used to assess social desirability bias in self-reports derived from a dietary fat screener (PFat), a dietary fruit and vegetable screener (FVS), and a 1-item question on fruit and vegetable intake. Comparisons were made with mean intakes derived from up to 3 24-h recall interviews at baseline and follow-up (at 12 mo in 3 sites, 6 mo in the fourth). A social-desirability-related underestimate in fat intake on the PFat relative to the 24HR (percentage energy as fat) was evident in women [baseline b = -0.56 (P = 0.005); follow-up b = -0.62 (P < 0.001)]. There was an overestimate in FVS-derived fruit and vegetable consumption (servings/week) in men enrolled in any intervention at follow-up (b = 0.39, P = 0.05) vs. baseline (b = 0.04, P = 0.75). The 1-item fruit and vegetable question was associated with an overestimate at baseline in men according to SD score (b = 0.14, P = 0.02), especially men with less than college education (b = 0.23, P = 0.01). Women with less than college education expressed a similar bias at follow-up (b = 0.13, P = 0.02). Differences in the magnitude of bias according to gender, type of instrument used, and randomization condition are comparable to what has been seen for other instruments and have important implications for both measuring change in studies of diet and health outcomes and for developing methods to control for such biases. [ABSTRACT FROM AUTHOR]

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., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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/m 2 ), 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., (Copyright © 2024 American Society for Nutrition. All rights reserved.)

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.

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., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2023. This work is written by (a) US Government employee(s) and is in the public domain in the US.)