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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. POTOMAC: Prediction model on impact of lymphOpenia kineTics on survival Outcomes in H&N cancer via MAChine learning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective To describe the data-based development of a short dietary assessment instrument, a 16-item screener; and to evaluate the performance of the screener, comparing its performance with a complete 120-item food frequency questionnaire (FFQ) in assessing percentage energy from fat intake. Design A subsample (n=404) of participants in the National Institutes of Health-AARP Diet and Health Study, who had completed an FFQ and two 24-hour dietary recalls, also completed the fat screener. Percentage energy from fat from the screener and from the FFQ were compared with estimated true usual intake using a measurement error model. Results For men, the mean percentage energy from fat estimates for the different methods were: recalls, 30.1%, screener, 29.9%; FFQ, 30.4%. For women, the results were: recalls, 31.3%, screener, 28.4%, FFQ, 30.0%. Estimated correlations between true intake and screener were 0.64 and 0.58 for men and women, respectively, and between true intake and FFQ were 0.67 for men and 0.72 for women. Estimated attenuation coefficients for the screener were 1.29 (men) and 0.98 (women) and for the FFQ were 0.56 (men) and 0.57 (women). Conclusions The percentage energy from fat screener, when used in conjunction with external reference data, may be useful to compare mean intakes of fat for different population subgroups, and to examine relationships between fat intake and other factors.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jada.2006.07.003 Byline: Janet A. Tooze, Douglas Midthune, Kevin W. Dodd, Laurence S. Freedman, Susan M. Krebs-Smith, Amy F. Subar, Patricia M. Guenther, Raymond J. Carroll, Victor Kipnis Abstract: We propose a new statistical method that uses information from two 24-hour recalls to estimate usual intake of episodically consumed foods.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jada.2006.07.002 Byline: Amy F. Subar, Kevin W. Dodd, Patricia M. Guenther, Victor Kipnis, Douglas Midthune, Margaret McDowell, Janet A. Tooze, Laurence S. Freedman, Susan M. Krebs-Smith Abstract: Twenty-four-hour recalls capture rich information on food consumption, but suffer from inadequately measuring usual intakes of episodically consumed foods. We explore using food frequency questionnaire (FFQ) data as covariates in a statistical model to estimate individual usual intakes of episodically consumed foods and their distributions and describe the development of the Food Propensity Questionnaire, an FFQ introduced in the 2003-2004 National Health and Nutrition Examination Survey.

Although 24-hour recalls are frequently used in dietary assessment, intake on a single day is a poor estimator of long-term usual intake. Statistical modeling mitigates this limitation more effectively than averaging multiple 24-hour recalls per respondent. In this article, we describe the statistical theory that underlies the four major modeling methods developed to date, then review the strengths and limitations of each method. We focus on the problem of estimating the distribution of usual intake for a population from 24-hour recall data, giving special attention to the problems inherent in modeling usual intake for foods or food groups that a proportion of the population does not consume every day (ie, episodically consumed foods). All four statistical methods share a common framework. Differences between the methods arise from different assumptions about the measurement characteristics of 24-hour recalls and from the fact that more recently developed methods build upon their predecessor(s). These differences can result in estimated usual intake distributions that differ from one another. We also demonstrate the need for an improved method for estimating usual intake distributions for episodically consumed foods.

Objective We propose a new statistical method that uses information from two 24-hour recalls to estimate usual intake of episodically consumed foods. Statistical analyses performed The method developed at the National Cancer Institute (NCI) accommodates the large number of nonconsumption days that occur with foods by separating the probability of consumption from the consumption-day amount, using a two-part model. Covariates, such as sex, age, race, or information from a food frequency questionnaire, may supplement the information from two or more 24-hour recalls using correlated mixed model regression. The model allows for correlation between the probability of consuming a food on a single day and the consumption-day amount. Percentiles of the distribution of usual intake are computed from the estimated model parameters. Results The Eating at America's Table Study data are used to illustrate the method to estimate the distribution of usual intake for whole grains and dark-green vegetables for men and women and the distribution of usual intakes of whole grains by educational level among men. A simulation study indicates that the NCI method leads to substantial improvement over existing methods for estimating the distribution of usual intake of foods. Conclusions The NCI method provides distinct advantages over previously proposed methods by accounting for the correlation between probability of consumption and amount consumed and by incorporating covariate information. Researchers interested in estimating the distribution of usual intakes of foods for a population or subpopulation are advised to work with a statistician and incorporate the NCI method in analyses.

Objective Twenty-four-hour recalls capture rich information on food consumption, but suffer from inadequately measuring usual intakes of episodically consumed foods. We explore using food frequency questionnaire (FFQ) data as covariates in a statistical model to estimate individual usual intakes of episodically consumed foods and their distributions and describe the development of the Food Propensity Questionnaire, an FFQ introduced in the 2003-2004 National Health and Nutrition Examination Survey. Design We analyzed data from 965 adult participants in the Eating at America's Table Study who completed four 24-hour recalls and an FFQ. We assessed whether or not increasing FFQ-reported frequency was associated with both number of 24-hour recall consumption days and amounts reported. Results For 52 of 56 food groups (93%), and 218 of 230 individual foods (95%), there were significant monotonically increasing relationships between FFQ frequency and 24-hour recall probability of consumption. For 47 of 56 food groups (84%) and 55 of 230 (24%) individual foods, there were significant positive correlations between FFQ frequencies and consumption-day mean intake. Conclusions We found strong and consistent relationships between reported FFQ frequency of food and food-group consumption and probability of consumption on 24-hour recalls. This supports the premise that frequency data may offer important covariate information in supplementing multiple recalls for estimating usual intake of food groups.

A study examined body-mass index (BMI) in relation to the risk of death from any cause in more than 500,000 people who were 50 to 71 years of age. Results revealed an increased risk of death for the highest and lowest categories of BMI among both men and women, in all racial or ethnic groups, and at all ages, and that excess body weight during midlife, including overweight, was related to an increased risk of death.

Objectives To describe the implementation of the Multifactor Screener in the 2000 National Health Interview Survey (NHIS); to provide intake estimates for fruits and vegetables, fiber, and percentage of energy from fat by various demographic and behavioral characteristics; and to discuss the strengths and weaknesses of the method. Design/Subjects The 2000 NHIS was conducted in 38,632 households in a clustered sample representative of the 48 contiguous states in the United States. The Cancer Control Module was administered to adults (18 years and older) and included 17 dietary intake questions. Analyses After excluding individuals with missing data or unlikely values on the diet questions, we computed individual intake of servings of fruits and vegetables, percentage of energy from fat, and grams of fiber. We estimated median intakes and distributions of intakes using adjusted variance estimates. We present bivariate relationships between diet and demographics and diet and behavioral characteristics in almost 30,000 adults in the United States. Results In general, intakes of these dietary factors were closer to recommendations among well-educated individuals, those engaged in other healthful behaviors, and underweight and normal weight individuals. Latinos had higher intakes of fruits and vegetables (men: 6 servings; women: 4.8 servings) and fiber (men: 23 g; women: 17 g), and generally a lower percentage of energy intake from fat (men: 33.7%; women: 32.1%) than did non-Latino whites (men: 5.4 servings; women: 4.5 servings; men: 19 g; women: 14 g; men: 33.9%; women: 32.0%) and non-Latino blacks (men: 5.4 servings; women: 4.4 servings; men: 19 g; women: 13 g; men: 34.7%; women: 33.5%). The strengths and limitations of the short dietary assessment method are discussed. Conclusions The Multifactor Screener in NHIS, when used in conjunction with external reference data, provides reasonable estimates for three dietary factors and suggests relationships between intakes and other characteristics that are consistent with other data. Thus, these NHIS estimates could provide useful national benchmarks for local surveys using the same instrument. J Am Diet Assoc. 2005;105:352-363.

We reexamined the current practice in estimating the distribution of usual dietary nutrient intakes from population surveys when using self-report dietary instruments, particularly the 24-h recall (24HR), in light of the new data from the Observing Protein and Energy Nutrition Study. In this study, reference biomarkers for energy (doubly labeled water) and protein [urinary nitrogen (UN)], together with multiple FFQs and 24HRs, were administered to 484 healthy volunteers. By using the reference biomarkers to estimate the distributions for energy and protein, the data confirmed previous reports that FFQs generally do not give an accurate impression of the distribution of usual dietary intake. The traditional method applied to 24HRs performed poorly because of underestimating the mean and overestimating the SD of the usual energy and protein intake distributions, and, although the National Research Council and the Iowa State University methods generally give better estimates of the shape of the distribution, they did not improve the estimates of the mean (10-15% underestimation for energy and 6-7% underestimation for protein). Results for urinary potassium, a putative biomarker for potassium intake, and reported potassium intake did not display this underestimation and may reflect either differential underreporting of foods or inadequacy of the potassium biomarker. A large controlled feeding study is required to validate conclusively the potassium biomarker. For energy intake, adjusting its 24HR-based distribution by using the UN biomarker appeared to capture the usual intake distribution quite accurately. Incorporating UN assessments into nutritional surveys, therefore, deserves serious consideration. KEY WORDS: * biological markers * energy intake * nutrition assessment * questionnaires * reference values * underreporting * surveillance

Objective The aim of this study was to examine whether estimated net acid excretion (NAE) from two dietary assessment instruments can predict urine pH in healthy volunteers. Design The Observing Protein and Energy Nutrition (OPEN) study, which was designed to assess dietary measurement error, included men and women aged 40 to 69 years. Participants were asked to complete two food frequency questionnaires (FFQs) and two 24-hour recalls at approximately three-month intervals, and to return two 24-hour urine samples during the same period. NAE was estimated using a previously described equation. The estimates obtained from the different dietary assessment measures were correlated with actual urine pH. Subjects/setting Four hundred eighty-four subjects were enrolled in the OPEN study. All participants were included in this study on urine pH, given that they had all completed at least one dietary assessment and one urine sample. The study took place in Montgomery County, MD, between September 1999 and March 2000. Results Correlation coefficients between NAE estimates using the FFQ data and the average of two urine pH measures were -0.34 and -0.39 for women and men, respectively. Removing within-person variance in the urine pH levels resulted in a deattenuated overall correlation of -0.50 for the FFQ. Correlations between the 24-hour recall estimates of NAE were -0.41 and -0.32 for women and men, respectively. For the 24-hour recalls, the deattenuated overall correlation was -0.50. Application/conclusions Our findings suggest that FFQs provide adequate dietary data to estimate renal NAE and could be used as a surrogate for urinary pH levels in large epidemiologic studies. J Am Diet Assoc. 2003; 103: 1001-1007., In animal and human studies, low pH of the urine has been directly related to elevated levels of arylamine-DNA adducts in the bladder urothelium (1-3). Experimental data show that low [...]