Janet A. Tooze

A mixed‐effects model approach for estimating the distribution of usual intake of nutrients: The NCI method

It is of interest to estimate the distribution of usual nutrient intake for a population from repeat 24-h dietary recall assessments. A mixed effects model and quantile estimation procedure, developed at the National Cancer Institute (NCI), may be used for this purpose. The model incorporates a Box-Cox parameter and covariates to estimate usual daily intake of nutrients; model parameters are estimated via quasi-Newton optimization of a likelihood approximated by the adaptive Gaussian quadrature. The parameter estimates are used in a Monte Carlo approach to generate empirical quantiles; standard errors are estimated by bootstrap. The NCI method is illustrated and compared with current estimation methods, including the individual mean and the semi-parametric method developed at the Iowa State University (ISU), using data from a random sample and computer simulations. Both the NCI and ISU methods for nutrients are superior to the distribution of individual means. For simple (no covariate) models, quantile estimates are similar between the NCI and ISU methods. The bootstrap approach used by the NCI method to estimate standard errors of quantiles appears preferable to Taylor linearization. One major advantage of the NCI method is its ability to provide estimates for subpopulations through the incorporation of covariates into the model. The NCI method may be used for estimating the distribution of usual nutrient intake for populations and subpopulations as part of a unified framework of estimation of usual intake of dietary constituents.

Morbidity and mortality in long-term survivors of Hodgkin lymphoma: a report from the Childhood Cancer Survivor Study

The contribution of specific cancer therapies, comorbid medical conditions, and host factors to mortality risk after pediatric Hodgkin lymphoma (HL) is unclear. We assessed leading morbidities, overall and cause-specific mortality, and mortality risks among 2742 survivors of HL in the Childhood Cancer Survivor Study, a multi-institutional retrospective cohort study of survivors diagnosed from 1970 to 1986. Excess absolute risk for leading causes of death and cumulative incidence and standardized incidence ratios of key medical morbidities were calculated. Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of risks for overall and cause-specific mortality. Substantial excess absolute risk of mortality per 10,000 person-years was identified: overall 95.5; death due to HL 38.3, second malignant neoplasms 23.9, and cardiovascular disease 13.1. Risks for overall mortality included radiation dose ≥ 3000 rad ( ≥ 30 Gy; supra-diaphragm: HR, 3.8; 95% CI, 1.1-12.6; infradiaphragm + supradiaphragm: HR, 7.8; 95% CI, 2.4-25.1), exposure to anthracycline (HR, 2.6; 95% CI, 1.6-4.3) or alkylating agents (HR, 1.7; 95% CI, 1.2-2.5), non-breast second malignant neoplasm (HR, 2.6; 95% CI 1.4-5.1), or a serious cardiovascular condition (HR, 4.4; 95% CI 2.7-7.3). Excess mortality from second neoplasms and cardiovascular disease vary by sex and persist > 20 years of follow-up in childhood HL survivors.

Addressing Current Criticism Regarding the Value of Self-Report Dietary Data

Recent reports have asserted that, because of energy underreporting, dietary self-report data suffer from measurement error so great that findings that rely on them are of no value. This commentary considers the amassed evidence that shows that self-report dietary intake data can successfully be used to inform dietary guidance and public health policy. Topics discussed include what is known and what can be done about the measurement error inherent in data collected by using self-report dietary assessment instruments and the extent and magnitude of underreporting energy compared with other nutrients and food groups. Also discussed is the overall impact of energy underreporting on dietary surveillance and nutritional epidemiology. In conclusion, 7 specific recommendations for collecting, analyzing, and interpreting self-report dietary data are provided: (1) continue to collect self-report dietary intake data because they contain valuable, rich, and critical information about foods and beverages consumed by populations that can be used to inform nutrition policy and assess diet-disease associations; (2) do not use self-reported energy intake as a measure of true energy intake; (3) do use self-reported energy intake for energy adjustment of other self-reported dietary constituents to improve risk estimation in studies of diet-health associations; (4) acknowledge the limitations of self-report dietary data and analyze and interpret them appropriately; (5) design studies and conduct analyses that allow adjustment for measurement error; (6) design new epidemiologic studies to collect dietary data from both short-term (recalls or food records) and long-term (food-frequency questionnaires) instruments on the entire study population to allow for maximizing the strengths of each instrument; and (7) continue to develop, evaluate, and further expand methods of dietary assessment, including dietary biomarkers and methods using new technologies.

Analysis of repeated measures data with clumping at zero.

Longitudinal or repeated measures data with clumping at zero occur in many applications in biometrics, including health policy research, epidemiology, nutrition, and meteorology. These data exhibit correlation because they are measured on the same subject over time or because subjects may be considered repeated measures within a larger unit such as a family. They present special challenges because of the extreme non-normality of the distributions involved. A model for repeated measures data with clumping at zero, using a mixed-effects mixed-distribution model with correlated random effects, is presented. The model contains components to model the probability of a nonzero value and the mean of nonzero values, allowing for repeated measurements using random effects and allowing for correlation between the two components. Methods for describing the effect of predictor variables on the probability of nonzero values, on the mean of nonzero values, and on the overall mean amount are given. This interpretation also applies to the mixed-distribution model for cross-sectional data. The proposed methods are illustrated with analyses of effects of several covariates on medical expenditures in 1996 for subjects clustered within households using data from the Medical Expenditure Panel Survey.

Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes

Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575-1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the foods usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at Americas Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.

Geriatric assessment predicts survival for older adults receiving induction chemotherapy for acute myelogenous leukemia

We investigated the predictive value of geriatric assessment (GA) on overall survival (OS) for older adults with acute myelogenous leukemia (AML). Consecutive patients ≥ 60 years with newly diagnosed AML and planned intensive chemotherapy were enrolled at a single institution. Pretreatment GA included evaluation of cognition, depression, distress, physical function (PF) (self-reported and objectively measured), and comorbidity. Objective PF was assessed using the Short Physical Performance Battery (SPPB, timed 4-m walk, chair stands, standing balance) and grip strength. Cox proportional hazards models were fit for each GA measure as a predictor of OS. Among 74 patients, the mean age was 70 years, and 78.4% had an Eastern Cooperative Oncology Group (ECOG) score ≤ 1. OS was significantly shorter for participants who screened positive for impairment in cognition and objectively measured PF. Adjusting for age, gender, ECOG score, cytogenetic risk group, myelodysplastic syndrome, and hemoglobin, impaired cognition (Modified Mini-Mental State Exam < 77) and impaired objective PF (SPPB < 9) were associated with worse OS. GA methods, with a focus on cognitive and PF, improve risk stratification and may inform interventions to improve outcomes for older AML patients.

Change in Body Size and the Risk of Colorectal Adenomas

Adiposity has been recognized as a risk factor for colorectal adenoma, but the influence of weight gain, adipose tissue distribution, and possible differences between ethnic/racial and gender groups remains unanswered. The aim of this prospective study was to examine the association between adiposity and weight change and colorectal adenoma risk. Over ∼10-year period, anthropometric measures and other risk factors were measured at three time points in the multicenter multiethnic Insulin Resistance Atherosclerosis Study cohort. Colonoscopies were then conducted on 600 cohort participants regardless of symptoms whose mean age at colonoscopy was 64 years. Multivariate logistic regression analyses were used to assess the association between colorectal adenomas and measures of adiposity and weight change over the ∼10-year period before colonoscopy. Obesity was positively associated with risk of colorectal adenomas at the time of colonoscopy [adjusted odds ratio (ORadj), 2.16; 95% confidence interval (95% CI), 1.13-4.14] and was stronger in women (ORadj, 4.42; 95% CI, 1.53-12.78) than in men (ORadj, 1.26; 95% CI, 0.52-3.07). The risk of adenomas increased among participants who gained weight compared with those who maintained weight over the ∼5 years (ORadj, 2.30; 95% CI, 1.25-4.22) and ∼10 years (ORadj, 2.12; 95% CI, 1.25-3.62). These associations were similar for both advanced and nonadvanced adenomas. These results suggest a positive association between obesity, weight gain, and colorectal adenoma risk. Stronger associations were observed when obesity was measured at the time of colonoscopy, suggesting that obesity may be a promoting factor in the growth of colorectal adenomas. (Cancer Epidemiol Biomarkers Prev 2007;16(3):526–31)

Food Intake Patterns Associated With Incident Type 2 Diabetes: The Insulin Resistance Atherosclerosis Study

OBJECTIVE—Markers of hemostasis and inflammation such as plasminogen activator inhibitor-1 (PAI-1) and fibrinogen have been associated with risk of type 2 diabetes. We aimed to identify food intake patterns influencing this pathway and evaluate their association with incident diabetes. RESEARCH DESIGN AND METHODS—The Insulin Resistance Atherosclerosis Study cohort included 880 middle-aged adults initially free of diabetes. At the 5-year follow-up, 144 individuals had developed diabetes. Usual dietary intake was ascertained with a 114-item food frequency questionnaire. Using reduced rank regression, we identified a food pattern maximizing the explained variation in PAI-1 and fibrinogen. Subsequently, the food pattern–diabetes association was evaluated using logistic regression. RESULTS—High intake of the food groups red meat, low-fiber bread and cereal, dried beans, fried potatoes, tomato vegetables, eggs, cheese, and cottage cheese and low intake of wine characterized the pattern, which was positively associated with both biomarkers. With increasing pattern score, the odds of diabetes increased significantly (Ptrend < 0.01). After multivariate adjustment, the odds ratio comparing extreme quartiles was 4.3 (95% CI 1.7–10.8). Adjustment for insulin sensitivity and secretion and other metabolic factors had little impact (4.9, 1.8–13.7). CONCLUSIONS—Our findings provide support for potential behavioral prevention strategies, as we identified a food intake pattern that was strongly related to PAI-1 and fibrinogen and independently predicted type 2 diabetes.

The Feasibility of Inpatient Geriatric Assessment for Older Adults Receiving Induction Chemotherapy for Acute Myelogenous Leukemia

To test the feasibility and utility of a bedside geriatric assessment (GA) to detect impairment in multiple geriatric domains in older adults initiating chemotherapy for acute myelogenous leukemia (AML).

Physical Performance and Subsequent Disability and Survival in Older Adults with Malignancy: Results from the Health, Aging and Body Composition Study

OBJECTIVES: To evaluate objective physical performance measures as predictors of survival and subsequent disability in older patients with cancer.