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Microarray gene expression data contains missing values (MVs). However, some methods for downstream analyses, including some predic- tion tools, require a complete expression data matrix. Current methods for estimating the MVs include sample mean and K-nearest neighbors (KNN). Whether the accuracy of estimation (imputation) methods depends on the actual gene expression has not been thoroughly investigated. Under this set- ting, we examine how the accuracy depends on the actual expression level and propose new methods that provide improvements in accuracy relative to the current methods in certain ranges of gene expression. In particular, we propose regression methods, namely multiple imputation via ordinary least squares (OLS) and missing value prediction using partial least squares (PLS). Mean estimation of MVs ignores the observed correlation structure of the genes and is highly inaccurate. Estimating MVs using KNN, a method which incorporates pairwise gene expression information, provides substan- tial improvement in accuracy on average. However, the accuracy of KNN across the wide range of observed gene expression is unlikely to be uniform and this is revealed by evaluating accuracy as a function of the expression level.

Dietary data collected from 24-hour dietary recalls are observed with significant measurement errors. In the nonparametric curve estimation literature, much of the effort has been devoted to designing methods that are consistent under contamination by noise, and which have been traditionally applied for analyzing those data. However, some foods such as alcohol or fruits are consumed only episodically, and may not be consumed during the day when the 24-hour recall is administered. These so-called excess zeros make existing nonparametric estimators break down, and new techniques need to be developed for such data. We develop two new consistent semiparametric estimators of the distribution of such episodically consumed food data, making parametric assumptions only on some less important parts of the model. We establish its theoretical properties and illustrate the good performance of our fully data-driven method in simulated and real data. Supplementary materials for this article are available online.

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.

Data with a huge size present great challenges in modeling, inferences, and computation. In handling big data, much attention has been directed to settings with "large p small n", and relatively less work has been done to address problems with p and n being both large, though data with such a feature have now become more accessible than before, where p represents the number of variables and n stands for the sample size. The big volume of data does not automatically ensure good quality of inferences because a large number of unimportant variables may be collected in the process of gathering informative variables. To carry out valid statistical analysis, it is imperative to screen out noisy variables that have no predictive value for explaining the outcome variable. In this paper, we develop a screening method for handling large-sized survival data, where the sample size n is large and the dimension p of covariates is of non-polynomial order of the sample size n, or the so-called NP-dimension. We rigorously establish theoretical results for the proposed method and conduct numerical studies to assess its performance. Our research offers multiple extensions of existing work and enlarges the scope of high-dimensional data analysis. The proposed method capitalizes on the connections among useful regression settings and offers a computationally efficient screening procedure. Our method can be applied to different situations with large-scale data including genomic data.

Measuring usual dietary intake in freely living humans is difficult to accomplish. As a part of our recent study, a food frequency questionnaire was completed by healthy adult men and women at days 0 and 90 of the study. Data from the food questionnaire were analyzed with a nutrient analysis program ( www.Harvardsffq.date ). Healthy men and women consumed protein as 19-20% and 17-19% of their total energy intakes, respectively, with animal protein representing about 75 and 70% of their total protein intakes, respectively. The intake of each nutritionally essential amino acid (EAA) by the persons exceeded that recommended for healthy adults with a minimal physical activity. In all individuals, the dietary intake of leucine was the highest, followed by lysine, valine, and isoleucine in descending order, and the ingestion of amino acids that are synthesizable de novo in animal cells (AASAs) was about 20% greater than that of total EAAs. The intake of each AASA met those recommended for healthy adults with a minimal physical activity. Intakes of some AASAs (alanine, arginine, aspartate, glutamate, and glycine) from a typical diet providing 90-110 g food protein/day does not meet the requirements of adults with an intensive physical activity. Within the male or female group, there were not significant differences in the dietary intakes of all amino acids between days 0 and 90 of the study, and this was also true for nearly all other essential nutrients. Our findings will help to improve amino acid nutrition and health in both the general population and exercising individuals.

A priori dietary indices provide a standardized, reproducible way to evaluate adherence to dietary recommendations across different populations. Existing nutrient-based indices were developed to reflect food/beverage intake; however, given the high prevalence of dietary supplement (DS) use and its potentially large contribution to nutrient intakes for those that use them, exposure classification without accounting for DS is incomplete. The purpose of this article is to review existing nutrient-based indices and describe the development of the Total Nutrient Index (TNI), an index developed to capture usual intakes from all sources of under-consumed micronutrients among the U.S. population. The TNI assesses U.S. adults' total nutrient intakes relative to recommended nutrient standards for eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium, magnesium, potassium, choline, and vitamins A, C, D, E. The TNI is scored from 0 to 100 (truncated at 100). The mean TNI score of U.S. adults (≥19 y; n = 9,954) based on dietary data from NHANES 2011-2014, was 75.4; the mean score for the index ignoring DS contributions was only 69.0 (t-test; p < 0.001). The TNI extends existing measures of diet quality by including nutrient intakes from all sources and was developed for research, monitoring, and policy purposes.

Summary We consider the problem where the data consist of a survival time and a binary outcome measurement for each individual, as well as corresponding predictors. The goal is to select the common set of predictors which affect both the responses, and not just one of them. In addition, we develop a survival prediction model based on data integration. The paper is motivated by the Cancer Genomic Atlas databank, which is currently the largest genomics and transcriptomics database. The data contain cancer survival information along with cancer stages for each patient. Furthermore, it contains reverse phase protein array measurements for each individual, which are the predictors associated with these responses. The biological motivation is to identify the major actionable proteins associated with both survival outcomes and cancer stages. We develop a Bayesian hierarchical model to model jointly the survival time and the classification of the cancer stages. Moreover, to deal with the high dimensionality of the reverse phase protein array measurements, we use a shrinkage prior to identify significant proteins. Simulations and Cancer Genomic Atlas data analysis show that the joint integrated modelling approach improves survival prediction.

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We investigate and propose robust methods to correct for the effect of measurement error when evaluating a surrogate marker using multiple estimators developed for parametric and nonparametric estimates of the proportion of treatment effect explained by the surrogate marker. In addition, we quantify the attenuation bias induced by measurement error and develop inference procedures to allow for variance and confidence interval estimation. Through a simulation study, we show that our proposed estimators correct for measurement error in the surrogate marker and that our inference procedures perform well in finite samples. We illustrate these methods by examining a potential surrogate marker which is measured with error, hemoglobin A1c, using data from the Diabetes Prevention Program clinical trial.

Infection by a single virus can evoke diverse immune responses, resulting in different neurological outcomes, depending on the host's genetic background. To study heterogenous viral response, we use Theiler's Murine Encephalomyelitis Virus (TMEV) to model virally induced neurological phenotypes and immune responses in Collaborative Cross (CC) mice. The CC resource consists of genetically distinct and reproducible mouse lines, thus providing a population model with genetic heterogeneity similar to humans. We examined different CC strains for the effect of chronic stage TMEV-induced immune responses on neurological outcomes throughout 90 days post infection (dpi), with a particular focus on limb paralysis, by measuring serum levels of 23 different cytokines and chemokines. Each CC strain demonstrated a unique set of immune responses, regardless of presence or absence of TMEV RNA. Using stepwise regression, significant associations were identified between IL-1α, RANTES, and paralysis frequency scores. To better understand these interactions, we evaluated multiple aspects of the different CC genetic backgrounds, including haplotypes of genomic regions previously linked with TMEV pathogenesis and viral clearance or persistence, individual cytokine levels, and TMEV-relevant gene expression. These results demonstrate how loci previously associated with TMEV outcomes provide incomplete information regarding TMEV-induced paralysis in the CC strains. Overall, these findings provide insight into the complex roles of immune response in the pathogenesis of virus-associated neurological diseases influenced by host genetic background., Highlights • Paralysis resulting from long-term TMEV infection varies by host genetic background. • For CC mice, no single immune environment causes paralysis following TMEV infection. • IL-1A and RANTES significantly influence the progression of paralysis over time. • H2 haplotype alone did not predict viral presence or TMEV-induced paralysis. • Paralysis severity is influenced by multiple TMEV- and immune-related loci.

© Lippincott Williams & Wilkins. Purpose Interest in a variety of physical behaviors (e.g., exercise, sitting time, sleep) in relation to health outcomes creates a need for new statistical approaches to analyze the joint effects of these distinct but inter-related physical behaviors. We developed and tested an integrated physical behavior score (PBS). Methods National Institutes of Health-American Association of Retired Persons Diet and Health Study participants (N = 163,016) completed a questionnaire (2004-2006) asking about time spent in five exercise and nonexercise physical activities, two sedentary behaviors (television and nontelevision), and sleep. In half of the sample, we used shape-constrained additive regression to model the relationship between each behavior and survival. Maximum logit scores from each of the eight behavior-survival functions were summed to produce a PBS that was proportionally rescaled to range from 0 to 100. We examined predictive validity of the PBS in the other half-sample using Cox Proportional Hazards models after adjustment for covariates for all-cause and cause-specific mortality. Results In the testing sample, over an average of 6.6 yr of follow-up, 8732 deaths occurred. We found a strong graded decline in risk of all-cause mortality across quintiles of PBS (Q5 vs Q1 hazard ratio [95% CI] = 0.53 [0.49, 0.57]). Risk estimates for the PBS were higher than any of the components in isolation. Results were similar but stronger for cardiovascular disease (Q5 vs Q1 = 0.42 [0.39, 0.48]) and other mortality (Q5 vs Q1 = 0.42 [0.36, 0.48]). The relationship between PBS and mortality was observed in stratified analyses by median age, sex, body mass index, and health status. Conclusions We developed a novel statistical method generated a composite physical behavior that is predictive of mortality outcomes. Future research is needed to test this approach in an independent sample.

In lifestyle intervention trials, where the goal is to change a participant's weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.

© 2018, Institute of Mathematical Statistics. All rights reserved. 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.

Copyright © 2017 by the American College of Sports Medicine Introduction/Purpose: To assess the utility of measurement methods that may be more accurate and precise than traditional questionnaire-based estimates of habitual physical activity and sedentary behavior we compared the measurement properties of a past year questionnaire (AARP) and more comprehensive measures: an internet-based 24-h recall (ACT24), and a variety of estimates from an accelerometer (ActiGraph). Methods: Participants were 932 adults (50-74 yr) in a 12-month study that included reference measures of energy expenditure from doubly labeled water (DLW) and active and sedentary time via activPAL. Results: Accuracy at the group level (mean differences) was generally better for both ACT24 and ActiGraph than the AARP questionnaire. The AARP accuracy for energy expenditure ranged from j4% to j13% lower than DLW, but its accuracy was poorer for physical activity duration (j48%) and sedentary time (j18%) versus activPAL. In contrast, ACT24 accuracy was within 3% to 10% of DLW expenditure measures and within 1% to 3% of active and sedentary time from activPAL. For ActiGraph, accuracy for energy expenditure was best for the Crouter 2-regression method (j2% to j7%), and for active and sedentary time the 100 counts per minute cutpoint was most accurate (j1% to 2%) at the group level. One administration of the AARP questionnaire was significantly correlated with long-term average from the reference measures (Q TX = 0.16-0.34) overall, but four ACT24 recalls had higher correlations (Q TX = 0.48-0.60), as did 4 d of ActiGraph assessment (Q TX = 0.54-0.87). Conclusions: New exposure assessments suitable for use in large epidemiologic studies (ACT24, ActiGraph) were more accurate and had higher correlations than a traditional questionnaire. Use of better more comprehensive measures in future epidemiologic studies could yield new etiologic discoveries and possibly new opportunities for prevention.

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

Systematic investigations into the structure of measurement error of physical activity questionnaires are lacking. We propose a measurement error model for a physical activity questionnaire that uses physical activity level (the ratio of total energy expenditure to basal energy expenditure) to relate questionnaire-based reports of physical activity level to true physical activity levels. The 1999-2006 National Health and Nutrition Examination Survey physical activity questionnaire was administered to 433 participants aged 40-69 years in the Observing Protein and Energy Nutrition (OPEN) Study (Maryland, 1999-2000). Valid estimates of participants' total energy expenditure were also available from doubly labeled water, and basal energy expenditure was estimated from an equation; the ratio of those measures estimated true physical activity level ("truth"). We present a measurement error model that accommodates the mixture of errors that arise from assuming a classical measurement error model for doubly labeled water and a Berkson error model for the equation used to estimate basal energy expenditure. The method was then applied to the OPEN Study. Correlations between the questionnaire-based physical activity level and truth were modest (r = 0.32-0.41); attenuation factors (0.43-0.73) indicate that the use of questionnaire-based physical activity level would lead to attenuated estimates of effect size. Results suggest that sample sizes for estimating relationships between physical activity level and disease should be inflated, and that regression calibration can be used to provide measurement error-adjusted estimates of relationships between physical activity and disease. © 2013 Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2013.

The focus of this paper is dietary intervention trials. We explore the statistical issues involved when the response variable, intake of a food or nutrient, is based on self‐report data that are subject to inherent measurement error. There has been little work on handling error in this context. A particular feature of self‐reported dietary intake data is that the error may be differential by intervention group. Measurement error methods require information on the nature of the errors in the self‐report data. We assume that there is a calibration sub‐study in which unbiased biomarker data are available. We outline methods for handling measurement error in this setting and use theory and simulations to investigate how self‐report and biomarker data may be combined to estimate the intervention effect. Methods are illustrated using data from the Trial of Nonpharmacologic Intervention in the Elderly, in which the intervention was a sodium‐lowering diet and the response was sodium intake. Simulations are used to investigate the methods under differential error, differing reliability of self‐reports relative to biomarkers and different proportions of individuals in the calibration sub‐study. When the reliability of self‐report measurements is comparable with that of the biomarker, it is advantageous to use the self‐report data in addition to the biomarker to estimate the intervention effect. If, however, the reliability of the self‐report data is low compared with that in the biomarker, then, there is little to be gained by using the self‐report data. Our findings have important implications for the design of dietary intervention trials. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

In recent years, genome-wide association studies (GWAS) and gene-expression profiling have generated a large number of valuable datasets for assessing how genetic variations are related to disease outcomes. With such datasets, it is often of interest to assess the overall effect of a set of genetic markers, assembled based on biological knowledge. Genetic marker-set analyses have been advocated as more reliable and powerful approaches compared with the traditional marginal approaches (Curtis and others, 2005. Pathways to the analysis of microarray data. TRENDS in Biotechnology 23, 429-435; Efroni and others, 2007. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS One 2, 425). Procedures for testing the overall effect of a marker-set have been actively studied in recent years. For example, score tests derived under an Empirical Bayes (EB) framework (Liu and others, 2007. Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics 63, 1079-1088; Liu and others, 2008. Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models. BMC bioinformatics 9, 292-2; Wu and others, 2010. Powerful SNP-set analysis for case-control genome-wide association studies. American Journal of Human Genetics 86, 929) have been proposed as powerful alternatives to the standard Rao score test (Rao, 1948. Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Mathematical Proceedings of the Cambridge Philosophical Society, 44, 50-57). The advantages of these EB-based tests are most apparent when the markers are correlated, due to the reduction in the degrees of freedom. In this paper, we propose an adaptive score test which up-or down-weights the contributions from each member of the marker-set based on the Z-scores of their effects. Such an adaptive procedure gains power over the existing procedures when the signal is sparse and the correlation among the markers is weak. By combining evidence from both the EB-based score test and the adaptive test, we further construct an omnibus test that attains good power in most settings. The null distributions of the proposed test statistics can be approximated well either via simple perturbation procedures or via distributional approximations. Through extensive simulation studies, we demonstrate that the proposed procedures perform well in finite samples. We apply the tests to a breast cancer genetic study to assess the overall effect of the FGFR2 gene on breast cancer risk. © 2012 The Author.

The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration- estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001-2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature. © The Author 2011.

Most large prospective cohorts use an FFQ to measure dietary intake. It is well known that an FFQ has substantial measurement error that can affect the results of such studies, leading to bias and the loss of power to detect diet–disease relationships(1,2). In order to evaluate the measurement error in an FFQ, and to correct observed diet–disease relationships for bias due to measurement error, many cohort studies include calibration sub-studies in which another, less biased, dietary instrument is administered as a reference instrument. The reference instrument is usually a short-term instrument such as a 24 h dietary recall (24HR) or food record. Methods for evaluating an FFQ’s ability to measure foods/nutrients that are consumed daily have been developed based on measurement error models that explicitly or implicitly assume that true usual intake and reported intake from the FFQ and reference instrument are all continuous variables(3–5). These methods have sometimes been used to evaluate ‘episodically consumed’ foods, or foods that are not consumed nearly every day by almost everyone in the population(6–8). This can be problematic if the reference instrument covers only a short time period, since short-term instruments may have a substantial proportion of subjects reporting zero intake of an episodically consumed food, violating the assumption that the reported intake is continuous. Recently, a measurement error model for episodically consumed foods has been developed and used in dietary surveillance to estimate population distributions of usual intakes of such foods(9–11) and to correct for measurement error in diet–health relationships when the 24HR is the main dietary instrument(12). The model allows for nonconsumption on a given day by separating the probability to consume from the amount consumed on a consumption day using a two-part model(9). The model has also been extended to a ‘three-part’ model to estimate the joint distribution of intakes of an episodically consumed food and energy(10). In the present paper, we use these models to evaluate an FFQ’s ability to measure intake of episodically consumed foods when the reference instrument measures short-term intake. After fitting a model that describes the relationship between the short-term reference and the FFQ, we use Monte Carlo methods to estimate the relationship between true and FFQ-reported intakes. In 1995, the National Institutes of Health (NIH) and the AARP, formerly the American Association of Retired Persons, initiated a large prospective cohort study called the NIH–AARP Diet and Health Study, which was designed to study relationships between diet and cancer. The study uses an FFQ to measure diet and includes a calibration sub-study of about 2000 subjects who in addition to the FFQ were administered two 24HR. Thompson et al.(13) evaluated the ability of the NIH–AARP FFQ to measure nutrient intake. In the present paper we assess the FFQ’s ability to measure intakes of twenty-nine food groups.

We have demonstrated that 0.45% quercetin added to a diet containing corn oil (15% w/w), as the lipid source, and cellulose (6% w/w), as the fiber source, was able to suppress the formation of high multiplicity aberrant crypt foci (ACF > 4 AC/focus), to lower proliferation and enhance apoptosis in a rat model of colon cancer. This experiment determined whether quercetin was acting as an antiinflammatory molecule in an in vivo model of colon cancer. We used weanling (21 d old) Sprague Dawley rats (n = 40) in a 2×2 factorial experiment to determine the influence of quercetin on iNOS, COX-1 and COX-2 expressions, all of which are elevated in colon cancer. Half of the rats received a diet containing either 0 or 0.45% quercetin, and within each diet group, half of the rats were injected with saline or azoxymethane (AOM, 15 mg/kg BW, sc, 2× during wk 3 and 4). The colon was resected 4 wk after the last AOM injection, and the mucosa scraped and processed for RNA isolation. Data from this experiment were analyzed using a mixed model in SAS for main effects and their interaction. AOM injection stimulated (P < 0.0001) iNOS expression. However there was an interaction such that, relative to rats injected with saline, AOM-injected rats consuming diets without quercetin had significantly elevated iNOS expression (5.29-fold), but the expression in AOM-injected rats consuming the diet with quercetin was not significantly elevated (1.68-fold). COX-1 expression was 20.2% lower (P < 0.06) in rats consuming diets containing quercetin. COX-2 expression was 24.3% higher (P < 0.058) in rats consuming diets without quercetin. These data suggest inflammatory processes are elevated in this early stage of colon carcinogenesis, yet quercetin may protect against colon carcinogenesis by down-regulating the expressions of COX-1 and COX-2.

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 food's 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 America's 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.

In a multicenter study, the overall relationship between diet and cancer risk can be broken down into: (a) within-center relationships, which reflect the relationships at the individual level in each of the centers, and (b) a between-center relationship, which captures the association between exposure and disease risk at the aggregate level. In this work, we propose the use of a Bayesian multilevel model that takes into account the within- and between-center levels of evidence, using information at the individual and aggregate level. Correction for measurement error is performed in order to correct for systematic between-center measurement error in dietary exposure, and for attenuation biases in relative risk estimates within centers. The estimation of the parameters is carried out in a Bayesian framework using Gibbs sampling. The model entails a measurement, an exposure, and a disease component. Within the European Prospective Investigation into Cancer and Nutrition (EPIC) the association between lipid intake, assessed through dietary questionnaire and 24-hour dietary recall, and breast cancer incidence was evaluated. This analysis involved 21 534 women and 334 incident breast cancer cases from the EPIC calibration study. In this study, total energy intake was positively associated with breast cancer incidence at the aggregate level, whereas no effect was observed for fat. At the individual level, height was positively related to breast cancer incidence, whereas a weaker association was observed for fat. The use of multilevel models, which constitute a very powerful approach to estimating individual vs aggregate levels of evidence should be considered in multicenter studies.

Objective It is now well-established that individuals misreport their dietary intake. We propose a new method (National Research Council-Biomarker [NRC-B]) for estimating population distributions of usual dietary intake from national survey 24-hour recall data, using additional biomarker data from an external study to adjust for such dietary misreporting. Statistical analyses performed NRC-B is an extension of the NRC method, and is based upon two developed assumptions: the ratio of the mean of true intake to that of reported intake is equal in the survey and external biomarker study; and the ratio of the variance of true intake to that of reported intake is equal in these two studies. NRC-B adjusts the usual intake distribution both for within-person variation and for bias (underreporting) that occur with 24-hour recall reports. Using doubly labeled water ( 2 H 2 18 O) measurements from the Observing Protein and Energy Nutrition study, we applied NRC-B to data on energy intake for adults aged 40 to 69 years from two national surveys, the Continuing Survey of Food Intakes by Individuals and National Health and Nutrition Examination Survey. We compared the results with the NRC and traditional methods that used only the survey data to estimate dietary intake distributions. Results Estimated distributions from NRC-B and NRC were much narrower and less skewed than from the traditional method. However, unlike NRC, the median of the NRC-B based distribution was higher by 8% to 16% than the traditional method in our examples. Conclusions The proposed method adjusts for the well-documented problem of underreporting of energy intake.

ObjectiveWe 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.DesignThe 124-item FFQ was evaluated within a measurement error model using two non-consecutive 24-hour dietary recalls (24HRs) as the reference.SettingParticipants were from six states (California, Florida, Pennsylvania, New Jersey, North Carolina and Louisiana) and two metropolitan areas (Atlanta, Georgia and Detroit, Michigan).SubjectsA subgroup of the cohort consisting of 2053 individuals.ResultsFor 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.ConclusionThe 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 (≥1.8) relative risks between many energy-adjusted nutrients and common cancers.

We examine two issues of importance in nutritional epidemiology: the relationship between dietary fat intake and breast cancer, and the comparison of different dietary assessment instruments, in our case the food frequency questionnaire (FFQ) and the multiple-day food record (FR). The data we use come from women participants in the control group of the Dietary Modification component of the Women’s Health Initiative (WHI) Clinical Trial. The difficulty with the analysis of this important data set is that it comes from a truncated sample, namely those women for whom fat intake as measured by the FFQ amounted to 32% or more of total calories. We describe methods that allow estimation of logistic regression parameters in such samples, and also allow comparison of different dietary instruments. Because likelihood approaches that specify the full multivariate distribution can be difficult to implement, we develop approximate methods for both our main problems that are simple to compute and have high efficiency. Application of these approximate methods to the WHI study reveals statistically significant fat and breast cancer relationships when a FR is the instrument used, and demonstrate a marginally significant advantage of the FR over the FFQ in the local power to detect such relationships.

The Hanford Thyroid Disease Study (HTDS) is one of the largest and most complex epidemiologic studies of the relation between environmental exposures to I and thyroid disease. The study detected no dose-response relation using a 0.05 level for statistical significance. The results for thyroid cancer appear inconsistent with those from other studies of populations with similar exposures, and either reflect inadequate statistical power, bias, or unique relations between exposure and disease risk. In this paper, we explore these possibilities, and present evidence that the HTDS statistical power was inadequate due to complex uncertainties associated with the mathematical models and assumptions used to reconstruct individual doses. We conclude that, at the very least, the confidence intervals reported by the HTDS for thyroid cancer and other thyroid diseases are too narrow because they fail to reflect key uncertainties in the measurement-error structure. We recommend that the HTDS results be interpreted as inconclusive rather than as evidence for little or no disease risk from Hanford exposures.

Background A study was begun in 1965 to 1966 to determine whether children exposed to radioactive iodine from nuclear weapons testing at the Nevada Test Site from 1951 through 1962 were at higher risk of thyroid disease. In 1993, we reported that among those examined in 1985 to 1986 (Phase II) there was an association between radiation from the Nevada Test Site and thyroid neoplasms. Methods We reevaluated the relationship between exposure to Nevada Test Site fallout and thyroid disease using newly corrected dose estimates and disease outcomes from the Phase II study. A prospective cohort of school children 12 to 18 years old living in Utah, Nevada, and Arizona was first examined for thyroid disease in 1965 to 1966 and reexamined in 1985 to 1986. In the Phase II report, 2497 subjects formed the basis for this analysis. Thyroid disease, including thyroid neoplasms and thyroiditis, was expressed as cumulative incidence and risk ratios (RRs) with a dose-response expressed as excess risk ratio (ERR/Gy). Results The RR between thyroid radiation dose in the highest dose group and thyroid neoplasms increased from 3.4 (in the earlier analysis) to 7.5. The RR for thyroiditis increased from 1.1 to 2.7 with an ERR/Gy of 4.9 (95% confidence interval = 2.0 to 10.0). There were too few malignant thyroid neoplasms to estimate risk. Conclusions Persons exposed to radioactive iodine as children have an increased risk of thyroid neoplasms and autoimmune thyroiditis up to 30 years after exposure.

Previous research suggests food diaries may be more efficient than food frequency questionnaires (FFQ) in detecting a dietary fat-breast cancer relationship. We assessed this further using 4 day food records (FRs) and FFQs in a large sample.Participants were from the non-intervention group of the dietary modification component of the Women's Health Initiative Clinical Trial: 603 breast cancer cases and 1206 controls matched on age, clinic, and length of follow-up. Relative risks (RRs) were estimated using unconditional logistic regression, adjusted for confounders and for the selection into the trial of women with an FFQ report exceeding 32% calories from fat. Direct comparison of the statistical power of the two instruments used the standardized log RR. An alternative analysis after removing subjects with missing covariate data was also conducted.The RR estimate for breast cancer in the top quintile of total fat intake, adjusted for confounders and total energy, was 1.82 (P for trend 0.02) for the FR but 0.67 for the FFQ (P for trend 0.24). Following adjustment for selection, estimates were 2.09 (P for trend 0.008) for the FR (alternative: 2.54, P for trend 0.006) and 1.71 (P for trend 0.18) for the FFQ (alternative: 1.24, P for trend 0.41). Similar results were seen for fat subtypes, particularly unsaturated fats. Comparisons showed higher statistical power for the FR than the FFQ (e.g. total fat, P = 0.08: alternative P = 0.01).Alternative instruments, such as FRs, may be preferable to FFQs for evaluating diet-disease relationships in cohort studies. The results support a positive association between dietary fat and breast cancer.

In the 1940s and 1950s, children in Israel were treated for tinea capitis by irradiation to the scalp to induce epilation. Follow-up studies of these patients and of other radiation- exposed populations show an increased risk of malignant and benign thyroid tumors. Those analyses, however, assume that thyroid dose for individuals is estimated precisely without error. Failure to account for uncertainties in dosimetry may affect standard errors and bias dose-response estimates. For the Israeli tinea capitis study, we discuss sources of uncertainties and adjust dosimetry for uncertainties in the prediction of true dose from X-ray treatment parameters. We also account for missing ages at exposure for patients with multiple X-ray treatments, since only ages at first treatment are known, and for missing data on treatment center, which investigators use to define exposure. Our reanalysis of the dose response for thyroid cancer and benign thyroid tumors indicates that uncertainties in dosimetry have minimal effects on dose-response estimation and for inference on the modifying effects of age at first exposure, time since exposure, and other factors. Since the components of the dose uncertainties we describe are likely to be present in other epidemiological studies of patients treated with radiation, our analysis may provide a model for considering the potential role of these uncertainties.

The nutrient and heterocyclic amine (HCA) intake of 165 healthy participants was assessed using a self-administered food frequency questionnaire (FFQ), which included a meat-cooking practices module. A database containing the HCA [2-amino-3,8-dimethylimadazo [4,5-f] quinoxaline (MeIQx) and 2-amino-1-methyl-6-phenylimadazo [4,5-b] pyridine (PhIP)] composition of various types of meat, cooked by different methods and to varying degrees, was developed and validated in conjunction with this module. The relative validity of dietary and HCA intake estimated by the FFQ was investigated using multiple food diaries (3 sets of 4 nonconsecutive day diaries completed over a 3-month period) as the reference method. Crude correlation coefficients of HCA intake assessed by the FFQ and food diaries were 0.43 [95% confidence interval (CI) 0.30–0.55] for MeIQx and 0.22 (95% CI 0.07–0.36) for PhIP intake. Deattenuated correlations were 0.60 (95% CI 0.49–0.69) and 0.36 (95% CI 0.22–0.49), respectively. Absolute MeIQx and PhIP intake was, however, underestimated by the FFQ (21.9 and 78.1 ng/day) compared with the food diaries (34.9 and 263.8 ng/day). The FFQ underestimated total red meat intake, the percentage of consumers, and the median intake of roast/baked and microwaved red meat. PhIP intake was severely underestimated by the FFQ and was most likely because of an underestimation of the percentage of people who cooked chicken using PhIP-producing cooking methods such as broiling and pan-frying. Additionally, the FFQ overestimated the percentage of consumers of baked chicken, a cooking method that produces less PhIP. In conclusion, although the FFQ and meat module underestimated absolute MeIQx and PhIP intake, its ability to rank individuals according to intake was acceptable.

Motivation: Ranking gene feature sets is a key issue for both phenotype classification, for instance, tumor classification in a DNA microarray experiment, and prediction in the context of genetic regulatory networks. Two broad methods are available to estimate the error (misclassification rate) of a classifier. Resubstitution fits a single classifier to the data, and applies this classifier in turn to each data observation. Cross-validation (in leave-one-out form) removes each observation in turn, constructs the classifier, and then computes whether this leave-one-out classifier correctly classifies the deleted observation. Resubstitution typically underestimates classifier error, severely so in many cases. Cross-validation has the advantage of producing an effectively unbiased error estimate, but the estimate is highly variable. In many applications it is not the misclassification rate per se that is of interest, but rather the construction of gene sets that have the potential to classify or predict. Hence, one needs to rank feature sets based on their performance. Results: A model-based approach is used to compare the ranking performances of resubstitution and cross-validation for classification based on real-valued feature sets and for prediction in the context of probabilistic Boolean networks (PBNs). For classification, a Gaussian model is considered, along with classification via linear discriminant analysis and the 3-nearest-neighbor classification rule. Prediction is examined in the steady-distribution of a PBN. Three metrics are proposed to compare feature-set ranking based on error estimation with ranking based on the true error, which is known owing to the model-based approach. In all cases, resubstitution is competitive with cross-validation relative to ranking accuracy. This is in addition to the enormous savings in computation time afforded by resubstitution.

Background Most large cohort studies have used a food frequency questionnaire (FFQ) for assessing dietary intake. Several biomarker studies, however, have cast doubt on whether the FFQ has sufficient precision to allow detection of moderate but important diet-disease associations. We use data from the Observing Protein and Energy Nutrition (OPEN) study to compare the performance of a FFQ with that of a 24-hour recall (24HR). Methods The OPEN study included 484 healthy volunteer participants (261 men, 223 women) from Montgomery County, Maryland, aged 40-69. Each participant was asked to complete a FFQ and 24HR on two occasions 3 months apart, and a doubly labelled water (DLW) assessment and two 24-hour urine collections during the 2 weeks after the first FFQ and 24HR assessment. For both the FFQ and 24HR and for both men and women, we calculated attenuation factors for absolute energy, absolute protein, and protein density. Results For absolute energy and protein, a single FFQ's attenuation factor is 0.04-0.16. Repeat administrations lead to little improvement (0.08-0.19). Attenuation factors for a single 24HR are 0.10-0.20, but four repeats would yield attenuations of 0.20-0.37. For protein density a single FFQ has an attenuation of 0.3-0.4; for a single 24HR the attenuation factor is 0.15-0.25 but would increase to 0.35-0.50 with four repeats. Conclusions Because of severe attenuation, the FFQ cannot be recommended as an instrument for evaluating relations between absolute intake of energy or protein and disease. Although this attenuation is lessened in analyses of energy-adjusted protein, it remains substantial for both FFQ and multiple 24HR. The utility of either of these instruments for detecting important but moderate relative risks (between 1.5 and 2.0), even for energy-adjusted dietary factors, is questionable.

This paper discusses and illustrates the qvf command for fitting generalized linear models. The differences between this new command and StataÕs glm command are highlighted. One of the most notable features of the qvf command is its ability to include instrumental variables. This functionality was added specifically to address measurement error but may be utilized by the user for other purposes. The qvf command was developed in the C-language using StataÕs new plugin features and executes much faster than the glm ado-file. Copyright 2003 by StataCorp LP.

W e discuss and illustrate the method of simulation extrapolation for “tting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation de“ned in Hardin and Carroll (2003). As in Hardin, Schmiediche, and Carroll (2003), our discussion i ncludes speci“ed measurement error and measurement error estimated by repli- cate error-prone proxies. In addition, we discuss and illustrate three extrapolant functions. This paper describes software for analyzing measurement error mode ls. Software pro- duction by StataCorp was funded by a National Institutes of Health (NIH) Small Busi- n ess Innovation Research Grant (SBIR). The goal of the work described in the grant is t he production of software to analyze statistical models where one or more covariates are measured with error. The software development includes two major features. The “rst development feature is the development of the Stata program to support communication to dynamically linked user-written computer code. StataCorp was responsible for this development, and support for user-written code in the C/C++ programming languages was added to Stata version 8. Stata refers to compiled user-written code as plugins and maintains documentation on their web site at http://www.stata.com/support/plugins. See Hardin and Carroll (2003) for notational conventions and an introduction to the t opic of measurement error models. The project described was supported by Grant Number R44 RR12435 from the National Institutes of Health, National Center for Research Resources. The contents of this article are solely the respon- sibility of the authors and do not necessarily represent the official views of the National Center for Research Resources.

Multiple-day food records or 24-hour dietary recalls (24HRs) are commonly used as "reference" instruments to calibrate food frequency questionnaires (FFQs) and to adjust findings from nutritional epidemiologic studies for measurement error. Correct adjustment requires that the errors in the adopted reference instrument be independent of those in the FFQ and of true intake. The authors report data from the Observing Protein and Energy Nutrition (OPEN) Study, conducted from September 1999 to March 2000, in which valid reference biomarkers for energy (doubly labeled water) and protein (urinary nitrogen), together with a FFQ and 24HR, were observed in 484 healthy volunteers from Montgomery County, Maryland. Accounting for the reference biomarkers, the data suggest that the FFQ leads to severe attenuation in estimated disease relative risks for absolute protein or energy intake (a true relative risk of 2 would appear as 1.1 or smaller). For protein adjusted for energy intake by using either nutrient density or nutrient residuals, the attenuation is less severe (a relative risk of 2 would appear as approximately 1.3), lending weight to the use of energy adjustment. Using the 24HR as a reference instrument can seriously underestimate true attenuation (up to 60% for energy-adjusted protein). Results suggest that the interpretation of findings from FFQ-based epidemiologic studies of diet-disease associations needs to be reevaluated.

This paper discusses and illustrates the method of regression calibra- tion. This is a straightforward technique for fitting models with additive mea- surement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on software developed as part of a small business innovation research (SBIR) grant from the National Institutes of Health (NIH). The authors of this paper served as consultants to Stata Corporation on that grant.

In this article we develop new methods for analyzing the data from an experiment using rodent models to investigate the effect of type of dietary fat on O6-methylguanine-DNA-methyltransferase (MGMT), an important biomarker in early colon carcinogenesis. The data consist of observed profiles over a spatial variable contained within a two-stage hierarchy, a structure that we dub hierarchical functional data. We present a new method providing a unified framework for modeling these data, simultaneously yielding estimates and posterior samples for mean, individual, and subsample-level profiles, as well as covariance parameters at the various hierarchical levels. Our method is nonparametric in that it does not require the prespecification of parametric forms for the functions and involves modeling in the wavelet space, which is especially effective for spatially heterogeneous functions as encountered in the MGMT data. Our approach is Bayesian; the only informative hyperparameters in our model are effectively sm...

This paper introduces additive measurement error in a generalized linear-model context. We discuss the types of measurement error along with their effects on fitted models. In addition, we present the notational conventions to be used in this and the accompanying papers. Copyright 2003 by StataCorp LP.

© 2016 American Statistical Association. Information from various public and private data sources of extremely large sample sizes are now increasingly available for research purposes. Statistical methods are needed for using information from such big data sources while analyzing data from individual studies that may collect more detailed information required for addressing specific hypotheses of interest. In this article, we consider the problem of building regression models based on individual-level data from an “internal” study while using summary-level information, such as information on parameters for reduced models, from an “external” big data source. We identify a set of very general constraints that link internal and external models. These constraints are used to develop a framework for semiparametric maximum likelihood inference that allows the distribution of covariates to be estimated using either the internal sample or an external reference sample. We develop extensions for handling complex stratified sampling designs, such as case-control sampling, for the internal study. Asymptotic theory and variance estimators are developed for each case. We use simulation studies and a real data application to assess the performance of the proposed methods in contrast to the generalized regression calibration methodology that is popular in the sample survey literature. Supplementary materials for this article are available online.

© Cambridge University Press 2015. We present hierarchical Bayesian models to integrate an arbitrary number of genomic data platforms incorporating known biological relationships between platforms, with the goal of identifying biomarkers significantly related to a clinical phenotype. Our integrative approach offers increased power and lower false discovery rates, and our model structure allows us to not only identify which gene(s) is (are) significantly related to the outcome, but also to understand which upstream platform(s) is (are) modulating the effect(s). We present both a linear and a more flexible non-linear formulation of our model, with the latter allowing for detection of non-linear dependencies between the platform-specific features. We illustrate our method using both formulations on a multi-platform brain tumor dataset. We identify several important genes related to cancer progression, along with the corresponding mechanistic information, and discuss and compare the results obtained from the two formulations.

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.

The authors describe a method for assessing and characterizing effect heterogeneity related to a matching covariate in case-control studies, using an example from veterinary medicine. Data are from a case-control study conducted in Texas during 1997-1998 of 498 pairs of horses with colic and their controls. Horses were matched by veterinarian and by month of examination. The number of matched pairs of cases and controls varied by veterinarian. The authors demonstrate that there is effect heterogeneity related to this characteristic (i.e., cluster size of veterinarians) for the association of colic with certain covariates, using a moving average approach to conditional logistic regression and graphs-based methods. The method described in this report can be applied to examining effect heterogeneity (or effect modification) by any ordered categorical or continuous covariates for which cases have been matched with controls. The method described enables one to understand the pattern of variation across ordered categorical or continuous matching covariates and allows for any shape for this pattern. This method applies to effect modification when causality might be reasonably assumed.

Utilizing foreign clinical data from a large-scale trial to draw conclusions about the safety and efficacy of a drug in a new region will eliminate the need to repeat a large-scale clinical trial in the new region. This approach is not only cost-effective; it expedites the approval of valuable new drugs, so subjects in the new region can benefit sooner from new therapies that are currently helping millions of people worldwide. The International Conference on Harmonization (ICH) E-5 guideline (ICH Harmonized Tripartite Guideline E-5: Ethnic factors in the acceptability of foreign clinical data,) indicates that a bridging study can be performed in a new region to provide efficacy, safety, and dosage information and allow subsequent extrapolation of foreign clinical data to the population in that new region. However, this definition does not identify appropriate statistical methods for “bridging” the two populations. The statistical method proposed in this article selects subjects from the global population who have characteristics similar to those of subjects in the new region. These methods have been used in other applications, but not to bridge data. The paper describes a new application of this methodology and evaluates other methodologies proposed for bridging. An example of how the proposed methodology can be used to bridge global clinical trial data to a new population is also provided.

Objectives: To evaluate the associations of dietary macronutrients, food groups, and eating patterns with risk of breast cancer in a population-based case–control study. Methods: In this study among women 20–44 years of age, 568 cases with breast cancer and 1451 population-based controls were included. They completed a detailed in-person interview, a self-administered food-frequency questionnaire and were measured for anthropometric indices. Logistic regression was used to estimate odds ratios (OR) and their 95% confidence intervals (CI) of breast cancer, adjusted for age, study site, race, education, alcohol consumption, oral contraceptive usage, smoking status, and body mass index. Results: There was no association between breast cancer risk and intake of calories, macronutrients, or types of fat. Risk of breast cancer was unrelated to intakes of a variety of food groups, including red meats, dairy, high-fat snacks and desserts, or foods high in animal fat. Increased risk was observed for high intake of a food group composed of sweet items, particularly sodas and desserts. Risk increased linearly with percent of calories from sweets and frequency of sweets intake. Consumption of sweets 9.8 or more times per week compared with

Statistical tables are an important component of data analysis and reports in biological sciences. However, the traditional manual processes for computation and presentation of statistically significant results using a letter-based algorithm are tedious and prone to errors. Based on the R language, we present two web-based software for individual and summary data, freely available online, at https://houssein-assaad.shinyapps.io/TableReport/ and https://houssein-assaad.shinyapps.io/SumAOV/ , respectively. The software are capable of rapidly generating publication-ready tables containing one-way analysis of variance (ANOVA) results. No download is required. Additionally, the software can perform multiple comparisons of means using the Duncan, Student-Newman-Keuls, Tukey Kramer, and Fisher’s least significant difference (LSD) tests. If the LSD test is selected, multiple methods (e.g., Bonferroni and Holm) are available for adjusting p-values. Using the software, the procedures of ANOVA can be completed within seconds using a web-browser, preferably Mozilla Firefox or Google Chrome, and a few mouse clicks. Furthermore, the software can handle one-way ANOVA for summary data (i.e. sample size, mean, and SD or SEM per treatment group) with post-hoc multiple comparisons among treatment means. To our awareness, none of the currently available commercial (e.g., SPSS and SAS) or open-source software (e.g., R and Python) can perform such a rapid task without advanced knowledge of the corresponding programming language. Our new and user-friendly software to perform statistical analysis and generate publication-ready MS-Word tables for one-way ANOVA are expected to facilitate research in agriculture, biomedicine, and other fields of life sciences.