Laurence S. Freedman

Bayesian approaches to randomized trials

Statistical issues in conducting randomized trials include the choice of a sample size, whether to stop a trial early and the appropriate analysis and interpretation of the trial results. At each of these stages, evidence external to the trial is useful, but generally such evidence is introduced in an unstructured and informal manner. We argue that a Bayesian approach allows a formal basis for using external evidence and in addition provides a rational way for dealing with issues such as the ethics of randomization, trials to show treatment equivalence, the monitoring of accumulating data and the prediction of the consequences of continuing a study

Bias in dietary-report instruments and its implications for nutritional epidemiology.

OBJECTIVE To evaluate measurement error structure in dietary assessment instruments and to investigate its implications for nutritional studies, using urinary nitrogen excretion as a reference biomarker for protein intake. DESIGN The dietary assessment methods included different food-frequency questionnaires (FFQs) and such conventional dietary-report reference instruments as a series of 24-hour recalls, 4-day weighed food records or 7-day diaries. SETTING Six original pilot validation studies within the European Prospective Investigation of Cancer (EPIC), and two validation studies conducted by the British Medical Research Council (MRC) within the Norfolk cohort that later joined as a collaborative component cohort of EPIC. SUBJECTS A sample of approximately 100 to 200 women and men, aged 35-74 years, from each of eight validation studies. RESULTS In assessing protein intake, all conventional dietary-report reference methods violated the critical requirements for a valid reference instrument for evaluating, and adjusting for, dietary measurement error in an FFQ. They displayed systematic bias that depended partly on true intake and partly was person-specific, correlated with person-specific bias in the FFQ. Using the dietary-report methods as reference instruments produced substantial overestimation (up to 230%) of the FFQ correlation with true usual intake and serious underestimation (up to 240%) of the degree of attenuation of FFQ-based log relative risks. CONCLUSION The impact of measurement error in dietary assessment instruments on the design, analysis and interpretation of nutritional studies may be much greater than has been previously estimated, at least regarding protein intake.

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.

Dealing With Dietary Measurement Error in Nutritional Cohort Studies

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.

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.

Relationship among outcome, stage of disease, and histologic grade for 22,616 cases of breast cancer : the basis for a prognostic index

Survival rates for 22,616 cases of breast cancer listed in the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute were stratified on outcome according to the histologic grade and stage of disease. Two different staging systems, “local, regional, and distant” and a modified American Joint Committee on Cancer (AJCC) system adopted for SEER were used. Relative survival rates were calculated at 5 and 10 years. Patients who were assigned Stage II, Grade 1 had the same survival as those assigned Stage I, Grade 3. Their survival was better than patients assigned Stage I, Grade 4. The 5‐year relative survival rate for patients listed as Stage I, Grade 1 was 99% and for patients listed as Stage I, Grade 2, it was 98%. At 10 years, the survival rate of patients assigned Stage I, Grade 1 was 95%. Patients with histologic Grade 1 tumors less than 2 cm in size and with positive axillary lymph nodes had a 5‐year survival rate of 99%. As breast tumors increased in size, the histologic grade also increased. The results suggest that in linking histologic grade with stage of disease, the staging system should also be considered. Histologic grade when used in conjunction with stage of disease can improve the prediction of outcome. Our results also indicate that a prognostic index can be created for breast cancer using a combination of stage of disease and histologic grade. The data suggest that only three grades are needed for breast cancer. 68:2142‐2149, 1991.

Pooled Results From 5 Validation Studies of Dietary Self-Report Instruments Using Recovery Biomarkers for Energy and Protein Intake

We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.

Long-Term Follow-up for Brain Tumor Development after Childhood Exposure to Ionizing Radiation for Tinea Capitis

Abstract Sadetzki, S., Chetrit, A., Freedman, L., Stovall, M., Modan, B. and Novikov, I. Long-Term Follow-up for Brain Tumor Development after Childhood Exposure to Ionizing Radiation for Tinea Capitis. Radiat. Res. 163, 424–432 (2005). Ionizing radiation is an established risk factor for brain tumors, yet quantitative information on the long-term risk of different types of brain tumors is sparse. Our aims were to assess the risk of radiation-induced malignant brain tumors and benign meningiomas after childhood exposure and to investigate the role of potential modifiers of that risk. The study population included 10,834 individuals who were treated for tinea capitis with X rays in the 1950s and two matched nonirradiated groups, comprising population and sibling comparison groups. The mean estimated radiation dose to the brain was 1.5 Gy. Survival analysis using Poisson regression was performed to estimate the excess relative and absolute risks (ERR, EAR) for brain tumors. After a median follow-up of 40 years, an ERR/Gy of 4.63 and 1.98 (95% CI = 2.43–9.12 and 0.73–4.69) and an EAR/Gy per 104 PY of 0.48 and 0.31 (95% CI = 0.28–0.73 and 0.12–0.53) were observed for benign meningiomas and malignant brain tumors, respectively. The risk of both types of tumors was positively associated with dose. The estimated ERR/Gy for malignant brain tumors decreased with increasing age at irradiation from 3.56 to 0.47 (P = 0.037), while no trend with age was seen for benign meningiomas. The ERR for both types of tumor remains elevated at 30-plus years after exposure.

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.

Performance of a food-frequency questionnaire in the US NIH–AARP (National Institutes of Health–American Association of Retired Persons) Diet and Health Study

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 studys 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.